oilfield review winter 2000-2001 - all articles
TRANSCRIPT
SCHLUMBERGER OILFIELD REVIEW
WIN
TER 2000/2001VOLUM
E 12 NUM
BER 4
Winter 2000/2001
Decision Analysis
Portfolio Management
Carbonate Reservoir Evaluation
Stimulation and Completion Optimization
Oilfield Review
Any action undertaken on a reservoir by an engineer orgeologist requires a clear concept or model of what thereservoir looks like. The more realistic the concept, themore likely the action will be successful. It is a basic truththat geologists have a different view of a reservoir thanengineers. Geologists tend to think in terms of stratigraphyand structure, whereas engineers are more concerned withpetrophysical and fluid properties. These two views of areservoir are not mutually exclusive. In fact, the key to arealistic reservoir concept or model is the integration ofdescriptive geologic information and numerical petrophysicaldata because geologic data contains three-dimensional(3D) information that petrophysical data does not. Foryears, this difference in dimensionality has been ignored.We must now accept the fact that only by linking petro-physical properties to geologic processes can realisticreservoir models be conceived and imaged.
Petrophysicists can play a key role in providing this vitallink. Although petrophysicists can generate large volumes ofpetrophysical data from wireline logs, the data is of little usein visualizing a 3D model unless it can be linked with datathat has spatial information. Geologic descriptions containenormous amounts of spatial information that can be linkedto porosity, saturation and permeability if collected properly.In the 1950s, Gus Archie demonstrated that petrophysicaland geologic data are linked at the pore scale. Porosity, per-meability and capillary forces are related to pore-size distrib-ution, and pore-size distribution is related to depositional,diagenetic and structural history. The problem is to describethe link in a manner that is useful for constructing 3Dimages of hydrocarbon reservoirs, and nowhere is this prob-lem more difficult than in carbonate reservoirs.
In carbonate rocks, rock-fabric descriptions provide thelink between petrophysics and geology. Today, the mostpopular methods of describing carbonate textures and poresystems are geological (see “A Snapshot of CarbonateReservoir Evaluation,” page 20). The method I propose ismore petrophysical in nature and focuses on descriptions ofpresent-day rock fabrics in terms of pore-size distribution.1
This method divides carbonate pore space into interpar-ticle, separate-vug and touching-vug porosity based on thelocation of the pore space relative to grains or crystals. Thepore-size distribution of interparticle pore space, whetherbetween grains or crystals, is a function of interparticleporosity, particle size and sorting. The amount of interpar-ticle porosity is primarily related to diagenetic processessuch as cementation, compaction and dolomitization.Grain size and sorting are related to depositional processes.Dolomite crystal size is related to the precursor rock fabricand to dolomitization models. Separate vugs usually arelocated within grains and have little impact on permeability.This pore space is often diagenetic in origin, but also may
Linking Petrophysical and Geologic Information: A Task for Petrophysics
be related to grain type, and thus to depositional processes.Touching vugs, on the other hand, form a pore systemunrelated to the fabric and represent a special type ofreservoir. This pore type is rarely related to depositionalprocesses and is typically diagenetic or structural in origin.Therefore, by describing the fabrics properly, a linkbetween petrophysical properties, pore-size distributionand geological processes can be established and used todistribute petrophysical data in 3D space.
Rock-fabric descriptions must be calibrated to wirelinelogs because the logs are not designed to capture geologicinformation. Three key descriptive elements are rock-fabricpetrophysical class, separate-vug porosity and interparticleporosity. Crossplots of porosity and water saturation oftencan be calibrated to petrophysical class, and crossplots oftransit time and porosity can be used to estimate interpar-ticle and separate-vug porosity. The integration of thesethree elements into wireline log analysis results in moreaccurate estimations of matrix permeability and watersaturation, as well as rock-fabric facies information thatcan be used to build a geologic model.
The task of linking geological descriptions and petrophys-ical properties falls between engineering and geological dis-ciplines. Petrophysicists are in a perfect position to providethis all-important link because they have direct contactwith both disciplines and are experts in both log and coreanalysis. Gus Archie envisioned this task as an integralpart of the “Petro” portion of Petrophysics. However, petro-physicists have not yet accepted the task and instead haveremained focused on core analysis and the development oflogging tools. It is time petrophysicists provide the missinglink between engineering and geological interpretationsand embrace this task as part of their responsibility.
F. Jerry LuciaSenior Research FellowBureau of Economic GeologyThe University of Texas at AustinAustin, Texas, USA
F. Jerry Lucia is a senior research fellow at the Bureau of Economic Geology,The University of Texas at Austin. Previously, he was a geological engineer forShell Oil Company for 31 years, working in research, operations and the headoffice. A prolific author, Jerry earned a BS degree in engineering and an MSdegree in geology, both from the University of Minnesota at Minneapolis, USA.
1. Lucia FJ: “Rock Fabric/Petrophysical Classification of Carbonate Pore Space forReservoir Characterization,” AAPG Bulletin 79, no. 9 (September 1995): 1275-1300.
Advisory PanelTerry AdamsAzerbaijan International Operating Co., Baku
Syed A. AliChevron Petroleum Technology Co.Houston, Texas, USA
Antongiulio AlborghettiAgip S.p.AMilan, Italy
Svend Aage AndersenMaersk Oil Kazakhstan GmBHAlmaty, Republic of Kazakhstan
Michael FetkovichPhillips Petroleum Co.Bartlesville, Oklahoma, USA
George KingBPHouston, Texas
David Patrick MurphyShell E&P CompanyHouston, Texas
Richard WoodhouseIndependent consultantSurrey, England
Executive EditorDenny O’BrienAdvisory EditorLisa StewartSenior EditorMark E. Teel EditorsGretchen M. GillisMark A. AndersenContributing EditorRana RottenbergDistributionDavid E. Bergt
Design/ProductionHerring DesignSteve FreemanKaren MalnarIllustrationTom McNeffMike MessingerGeorge StewartPrintingWetmore Printing CompanyCurtis Weeks
Oilfield Review is published quarterly by Schlumberger to communicatetechnical advances in finding and producing hydrocarbons to oilfieldprofessionals. Oilfield Review is distributed by Schlumberger to itsemployees and clients. Oilfield Review is printed in the USA.
Contributors listed with only geographic location are employees ofSchlumberger or its affiliates.
© 2001 Schlumberger. All rights reserved. No part of this publicationmay be reproduced, stored in a retrieval system or transmitted in anyform or by any means, electronic, mechanical, photocopying, recordingor otherwise without the prior written permission of the publisher.
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Winter 2000/2001Volume 12Number 4
Schlumberger
20 A Snapshot of Carbonate Reservoir Evaluation
The heterogeneity of carbonate rocks presents significant challenges thatmust be overcome to produce the 60% of known oil reserves they contain.Examples from around the world illustrate current approaches for evaluatingcarbonate reservoirs and provide direction for ongoing research initiatives.
2 Making Decisions in the Oil and Gas Industry
Advanced technology is available to analyze and direct both technical andeconomic decisions in the petroleum industry. One new solution, decision-tree analysis, is helping decision-makers prioritize problems, understand theimpact of factors that influence the decision, evaluate uncertainty, quantifythe value of new information and build confidence in the final decision.Decision trees help build a framework for difficult, multidisciplinary problemsand test the effect of each step in the decision process. Case studies showhow these tools help combine technical and economic information to promotesound decisions in field development programs and economic analysis.
61 Contributors
64 Coming in Oilfield Review and New Books
66 Annual Index
Oilfield Review
1
42 From Reservoir Specifics to Stimulation Solutions
Large investments associated with well completion call for an approach thatintegrates reservoir characterization with production engineering. Case his-tories illustrate how distributed teams of experts use field- or basin-specificmodeling to make stimulation and completion recommendations, focusingon productivity throughout the life a well, irrespective of boundaries betweentechnical disciplines. Utilizing Web-based tools to build comprehensive datasets and improved models, this methodology takes advantage of the latestformation evaluation and fracturing technologies.
10 Portfolio Management for Strategic Growth
The petroleum industry is now able to take advantage of asset-managementtechniques—developed for the financial-investment industry—that viewprojects and investments as an interdependent ensemble, or portfolio, ratherthan as independent entities. New software and consulting services helpdecision-makers select and analyze portfolios of projects that achieve avalue-risk balance in line with company strategy. Case studies show how oilcompanies are using portfolio optimization and other opportunity-managementtechniques to meet targeted levels of production, revenues, reserves andother key objectives.
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2 Oilfield Review
For help in preparation of this article, thanks to Joe Fay,Austin, Texas, USA; Kent Burkholder and Alexander Lythell,London, England; Paige McCown, Houston, Texas; PatParry, Centrica, Slough, Berkshire, England; KennethRicard, Aker Maritime, Houston, Texas; and LaurenceWickens, AEA Technology, Didcot, Oxfordshire, England.Decision Tree and Peep are marks of Schlumberger. DPS-2000 is a mark of Aker. Excel is a mark of MicrosoftCorporation.
Decisions in the oil and gas industry determinethe direction and course of billions of dollarsevery year. The complexity of a decision canrange from simple and Shakespearean—to drillor not to drill—to elaborate. Some of the moremonumental decisions determine the maximumbid for a lease, the best development process fora given asset, the drilling priority of a company’sexploration opportunities, the timing of increas-ing facility capacity, or whether to sign a long- orshort-term supply contract.
While simpler problems may be analyzedwith a few quick calculations, reaching morecomplicated decisions can take a companymonths or years of preparation. For example, oneof the dilemmas challenging E&P companiestoday is how to develop deepwater prospects.Sometimes subsea development is best; some-times a tethered floating structure is the solution.Typically, oil companies spend 12 to 18 months inthe decision cycle—gathering information, ana-lyzing data and modeling risk and uncertainty—before selecting a production system. Streamliningthis process may increase profit by shortening thetime to first production.
Ellen CoopersmithDecision FrameworksHouston, Texas, USA
Graham DeanCentricaSlough, Berkshire, England
Jason McVeanCalgary, Alberta, Canada
Erling StorauneAker Maritime, Inc.Houston, Texas
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The difference between a good decision and a bad one can be the difference
between success and disaster, profit and loss, or even life and death. Decision-
analysis software can help decision-makers identify factors that influence the
decision at hand and choose a path to desirable outcomes.
Making Decisions in theOil and Gas Industry
1. Newendorp PD: Decision Analysis for PetroleumExploration. Aurora, Colorado, USA: Planning Press,1996.
2. Bailey W, Couët B, Lamb F, Simpson G and Rose P:“Taking a Calculated Risk,” Oilfield Review 12, no. 3(Autumn 2000): 20-35.
3. Net present value is one possible value measure, butmany others can be used, including rate of return andprofit-to-investment ratio.
4. Newendorp, reference 1, chapter 4.
Winter 2000/2001 3
Several methods are available to help decision-makers evaluate uncertainty, reduce risk andchoose workable solutions.1 These methodsinclude net present value (NPV) calculations, dis-counted cash-flow analysis, Monte Carlo simula-tion, portfolio theory, decision-tree analysis andpreference theory—all of which were reviewedin a recent Oilfield Review article.2 Elementarysituations can be solved with basic expected-value computations, but more involved casesrequire a decision-analysis process that com-bines information from multiple disciplines,allows for uncertainty and evaluates the impactof different decisions. This article focuses ondecision-tree analysis and how it works, asdemonstrated through two case studies.
Decision-Tree AnalysisDecision-tree analysis is one way to frame andsolve complex situations that require a decision.The key to obtaining a useful solution is to clearlydefine the problem at the start and determinewhat decisions need to be made. The problem-definition stage includes identifying all knowninformation and listing any factors that may influence the final outcome. To expedite the process, decisions that can be deferred are post-poned so that future information can aid the deci-sion process.
Capturing the essence of a problem by deter-mining which are the influential factors helpsdecision-makers concentrate on only thoseissues that play a major role in the outcome.Such sensitivity analysis prioritizes the impor-tance of factors to be considered in a decision.For example, one decision may depend on six fac-tors: oil price, oil volume, gas price, gas volume,capital expenditures and operating costs—but the relative importance of these is unknown.For given uncertainties, or a range of possiblevalues, for each factor, sensitivity analysis cal-culates the net present values (sometimes writ-ten as after-tax cash) represented by those
uncertainties and ranks each factor (left).3 Thefactors that most affect project outcome are theones with the highest NPV range. The shape of thegraph, with large values on the top and small val-ues on the bottom, gives it the name “tornadoplot.” In this example, the two most importantfactors are oil price and oil volume. Operating-cost uncertainty does not significantly affect theoutcome, and so can be treated as a certaintywithout markedly affecting the results.
Once the problem is framed, decision treeshelp find a route to an advantageous solution.Decision trees are diagrams that portray the flowof a decision-making process as a sequence ofevents and possible outcomes (below). Eventsare represented as points, or nodes, and out-comes are depicted as branches issuing fromeach node. Nodes are either decision nodes—atwhich the decision-maker determines whatbranch to take—or uncertainty nodes, wherechance rules the outcome.4 Associated with eachbranch is the expected monetary value of the out-come. Additionally, branches emanating fromuncertainty nodes are weighted by the probabilityof that outcome occurring. In common notation,decision nodes plot as squares and uncertaintynodes as circles.
Sensitivity Analysis
Influence, %
Oil price
Oil volume
Capital expenditures
Gas volume
Gas price
Operating costs
51
31
09
05
03
01
30,000
31,538
36,068
42,083
41,776
42,862
43,643
Factors45,010 60,000
59,937
57,873
54,004
50,956
50,157
46,181
Net present value dollars, thousands
> Tornado plot showing factors that most influence a decision. Of the six factors selected for analysis,oil price and oil volume have the highest range in net present value (NPV), making the outcome mostsensitive to those factors.
Dry hole_ $50,000
2 Bcf+ $100,000
5 Bcf+ $250,000
$0
+ $100,000
Drill
Don’t drill
0.7
0.1
0.2A
B
> Simple decision tree showing one decisionnode (square) and possible outcomes. Outcomesare labeled with their expected value multipliedby the probability of that outcome occurring. The path with the highest expected value is high-lighted in yellow. (Adapted from Newendorp, reference 1.)
In this simple example, the decision nodemarks where the decision-maker elects to drill ornot to drill. The expected value associated with adecision not to drill is $0; that is, no money isspent or gained. The value expected from thedecision to drill depends on what is encountereddownhole: there is a 10% chance of 5 Bcf of gas,a 20% chance of 2 Bcf, and a 70% chance of adry hole. The expected reservoir size is in fact acontinuous distribution rather than three-valued,but for the purpose of this example, three sizesare examined (above). Ideally, branches of theuncertainty node try to capture the most impor-tant aspects of this continuous distribution.
The expected value of an uncertainty node isthe sum of all probability-weighted expected val-ues of the outcomes stemming from that node. Inthis way, working back from the end, or right-hand side of the tree, expected values can be cal-culated for every outcome. Once all the expectedvalues have been calculated, the optimal deci-sion route—the one along maximum expectedvalue—can be taken.
The same method works for more compli-cated decisions (next page). In this example, thedecision to buy acreage or not depends on under-standing the possible outcomes of a succession
of decisions, including to run a seismic survey ornot, to drill or not, and to drill a second well ornot. The possible final outcomes—large field,marginal field and dry hole—are the sameregardless of the decision route. However, theyhave different probabilities of occurrence at dif-ferent stages of the decision tree because as thetree grows, more information becomes available.For this decision tree, the solution that deliversthe highest expected monetary value traces thefollowing branches: Buy acreage, run seismicsurvey, seismic survey confirms lead, drill—andif the first hole is dry, drill a second wildcat.
Assigning probabilities to the tree branchesrequires technical expertise and, in this case,relies on prior knowledge of the region. The like-lihood and value of the various outcomes alsocan be based on the result of more detailedMonte Carlo simulations. For example, the cutoffvalue for what constitutes a large field could bethe high side of a probability distribution that isthe result of Monte Carlo simulation of the reser-voir volume parameter.
Depending on the type of decision to bemade, specialists from many oilfield disciplinesmay be called upon to supply information for
decision-tree analysis. In addition to the unknownreservoir size and content, outcomes that need tobe predicted include the following:
• price of oil and gas• quality and reliability of seismic imaging or
logging data• cost and risk versus value of additional
information• likelihood of drillpipe or logging tools get-
ting stuck, and other nonproductive rig time• reservoir compartmentalization, or number
of wells• reservoir fluid properties and performance• completion complexity• cost of transport to market• improvement gain from stimulation,
workover or enhanced-recovery methods.Less obvious to many oilfield professionals,
perhaps, but also important to estimate in casesthat lend themselves to decision-tree analyses,are eventualities such as government legislationand stability, company mergers, court cases andhealth, safety and environment (HSE) issues.
Numerous software products are availablethat facilitate decision-tree analysis for oil andgas E&P and other industries. These includePrecision Tree by Palisade, Decision Program-ming Language (DPL) by ADA (Applied DecisionAnalysis) and the Decision Tree package devel-oped by Merak, a Schlumberger company. Thesesystems link to calculation engines that computenet present values for each branch of the tree.Broadly speaking, decision-tree software pack-ages link to Excel as the calculation engine. Onlythe Merak Decision Tree software also linksdirectly to the Peep economic analysis program,which is a standard asset-management packagein the petroleum industry.
Decision trees can be helpful for analyzingmany types of oilfield decisions. Examplesinclude deciding whether to replace wireline logswith logs acquired while drilling, evaluatingwaterflood programs, optimizing workovers, andchoosing the best offshore platform topsidesconfiguration.5 The next section describes howdecision trees can help evaluate a deepwaterproduction system.
4 Oilfield Review
Maximum possiblereserves
Mean reserves
Minimum possiblereserves (dry hole )
> Continuous distribution of expected reservoir size. Although theexpected value of the reservoir size can fall anywhere in the contin-uous distribution, the most likely values should be selected for thedecision-tree branches. (Adapted from Newendorp, reference 1.)
Winter 2000/2001 5
Choosing a Production SystemAker Maritime, Inc., a maker of offshore platformsand spars, was approached by an operator prepar-ing to select a deepwater production system for adevelopment offshore West Africa.6 The client hadto decide whether to act early and buy a produc-tion system that could be adapted in case thereservoir turned out to be larger than expected, orwait for more information and optimize the size ofthe system. An early decision could mean quickerproduction of first oil. And an adaptive system hasthe flexibility to allow for future additions of fluid-processing modules or wells. However, such adecision would be based on minimal information.The alternative was to drill more wells, acquire
more information and buy a production systemoptimized for the reservoir size, but at additionalexpense and production delay.
Aker Maritime worked with DecisionFrameworks, a decision analysis and facilitationconsulting firm, to structure the decision andmodel development alternatives. The DecisionFrameworks approach relies on technical andcommercial petroleum-industry expertisepaired with Merak software, specifically theDecision Tree product and Peep economicdatabase application.
The first steps in the decision analysis wereto structure the problem, understand issues associated with the deepwater discovery andreview alternative development solutions.
> More complicated decision tree for buying acreage. In this example, the decision depends on a succession of decisions (highlighted in yellow) including running a seismic survey and drilling one or two wells. (Adapted from Newendorp, reference 1.)
5. Beck GF: “Examination of MWD (Measuring WhileDrilling) Wireline Replacement by Decision AnalysisMethods: Two Case Studies,” Transactions of theSPWLA 37th Annual Logging Symposium, New Orleans,Louisiana, USA, June 16-19, 1996, paper U.Martinsen R, Kjelstadli RM, Ross C and Rostad H: “TheValhall Waterflood Evaluation: A Decision Analysis CaseStudy,” paper SPE 38926, presented at the SPE TechnicalConference and Exhibition, San Antonio, Texas, USA,October 5-8, 1997.Macary S and El-Haddad A: “Decision Trees OptimizeWorkover Program,” Oil & Gas Journal 96, no. 51(December 21, 1998): 93-97, 100.MacDonald JJ and Smith RS: “Decision Trees ClarifyNovel Technology Applications,” Oil & Gas Journal 95,no. 8 (February 24, 1997): 69-71, 74-76.
6. A spar, sometimes called a dry-tree unit, is a floating vertical cylinder that is moored to the seafloor. Sparsallow production from deepwater fields to “dry” surfacefacilities as opposed to subsea facilities.
B
C
A
IH
GF
J
DE
Large field+$36 MM
Marginal field+$11 MM
Dry hole
Drill second
wildcat
Large field+$35 MM
Marginal field+$10 MM
Drill
Don’t buy acreage$0
Buy acreageRun seismicsurvey
Seismic survey
confirms lead
Large field+$33 MM
Marginal field+$8 MM
Dry hole,drop acreage
–$7 MMDrop acreage
–$6 MM
Drill secondwildcat
Large field+$34 MM
Marginal field+$9 MM
Dry hole
Drill
0.90
0.05 0.05
0.075
0.075
0.85
0.50
0.50
0.15
0.15
0.700.20
0.200.60
Dry hole,drop acreage
–$5 MM
Seismic shows nostructure, drop acreage
–$5 MM
Drop acreage–$4 MM
Drop acreage–$5 MM
Decision Frameworks worked with Aker and theiroil company client to define parameters of thediscovery, such as reservoir size, productionrates, number of wells and drilling schedule.Then, high-level decision trees were constructedfor the four development concepts under consid-eration. Two concepts were adaptive structures,the Aker adaptive spar and DPS-2000 (above andnext page, top). The other two were designs that
could be optimized to suit the reservoir size: afloating production, storage and offloading(FPSO) system and an optimized spar. All fourconcepts allowed oil storage.
The Decision Tree analysis for the adaptivedesigns called for selection of surface structureand topsides based on information from only twowells. This was followed by drilling of two wells,installation of the structure, development drilling
and production, then installation of additional pro-duction modules or wells as necessary (next page,bottom). The case of optimized designs starts withinformation from four wells before selecting andinstalling the production system, followed bydrilling of additional development wells and finallysome limited adjustment of the well count,depending on what production information indi-cated the size of the actual reservoir to be.
6 Oilfield Review
> Aker Maritime adaptive spar.
Winter 2000/2001 7
> Aker Maritime DPS-2000.
Limitedadjustment
of well count
Add productionmodules and
adjust well count
Development drillingand productionTwo penetrations
Four penetrationsDevelopment drilling
and production
Adaptive Designs
Optimized Designs
Decision Tree Structure
Selectstructure
and topsideequipment
Predrilltwo wells
Indicatedreservoir size
(capacity/wells)
Installstructureand drill
Installstructureand drill
Furtherindication ofreservoir size
Start withmore
information
Indicatedreservoir size
(capacity/wells)
Selectstructure
and topsideequipment
Furtherindication ofreservoir size
> Decision Tree structure for adaptive design compared to optimized design. The adaptive design (top) starts with less information and drills fewer wells. The optimized design (bottom) uses information from four wells to size thedevelopment concept.
The key uncertainty was the reservoir size,which governs the facility capacity and the num-ber of wells required to develop the reserves. Theresults of the Decision Tree analysis are the eco-nomic impacts of multiple scenarios that occur ifthe reservoir is:
• believed to be large and actually is large,medium or small;
• believed to be medium and actually islarge, medium or small;
• believed to be small and actually is large,medium or small.
A sample decision tree demonstrates the netpresent values calculated for one of the four
development concepts: the DPS-2000 adaptivesystem (above). The total NPV for this concept is$412 million. Comparing this figure to thoseobtained for the other three concepts shows theDPS-2000 to have the greatest NPV (below left).
The timing of the steps in the developmentplays a key role in investment payback. A largepart of the value in selecting an adaptive systemis in the reduced time to first oil. The DecisionTree software followed a schedule from January2001 to June 2005 that included front-end engi-neering and design (FEED), construction, deliveryand commissioning (below right). Both adaptiveconcepts could deliver first oil in 2003, compared
with the 2005 deliveries possible with the optimized systems. However, the added value ofthe adaptive systems was accompanied by added risk.
The Decision Tree software helped demon-strate the added value achievable with the earlyadaptive production systems and allowed AkerMaritime to present a full range of decisionoptions to the oil company client. It also under-scored the fact that uncertainty often exists evenafter more information is gathered. Recognizingthis during the selection of development con-cepts is important, and can add value.
8 Oilfield Review
Adaptive Concepts Optimized Concepts
Value Captured
DPS-2000
Adaptive spar
$412 MM
$350 MM
Optimized spar
FPSO
$313 MM
$182 MM
> Comparison of final NPV calculations for the four develop-ment concepts under consideration. The adaptive conceptsoffer up to $230 million higher NPV than the optimized concepts.
Jan.2001
Jun.2001
Jan.2002
Jun.2002
Jan.2003
Jun.2003
Jan.2004
Jun.2004
Jan.2005
Jun.2005
Jan.2006
Decision Tree Schedule Inputs
Aker DPS-2000
Adaptive spar
FPSO
Optimized spar
First oilDec. 2003
First oilAug. 2003
First oilFeb. 2005
First oilApr. 2005
Includes front-end engineering and design (FEED), construction, delivery and commissioning
> Schedule of Decision Tree events. The adaptive concepts start first and producefirst, while production from the optimized projects lags by about 18 months.
0.48 large
0.36 medium
0.16 small
882
NPV $MM
359
9
Actual reservoirprobability, size
0.23 large
0.46 medium
0.31 small
882
361
12
0.23 large
0.36 medium
0.41 small
882
361
14
Large reservoirindicated
Medium reservoirindicated
Small reservoirindicated
Predrill2 wells
Predrill2 wells
Predrill2 wells
InstallDPS-2000
InstallDPS-2000
InstallDPS-2000
p =0.2
8p = 0.39
p = 0.33
Two penetrations
DPS-2000
$412 MM
Decision Tree Results for Aker DPS-2000
$554 MM
$373 MM
$339 MM
26 wells, $100 M modification
14 wells, 1 dry hole
6 wells, 2 dry holes
14 wells
6 wells, 1 dry hole
14 wells
6 wells
26 wells, $100 M modification
26 wells, $100 M modification
> Decision Tree output showing NPV calculated for the DPS-2000 adaptive deepwater system.
Winter 2000/2001 9
Making a CaseThe decision-tree methodology also can beapplied to other types of E&P problems. As part ofCentrica’s strategy to acquire additional UK conti-nental shelf assets, the company can—when italready has contracts to buy the gas from theassets—often be required to consider potentiallyconflicting buyer-seller interests. In one example,Centrica needed to consider the impact of a pastdispute on the future value of an asset under con-sideration. The dispute related to the previousfailure of the sellers to meet contractual obliga-tions to provide gas and to Centrica’s applicationof the contractual remedies. The sellers objectedto this action and litigated to limit it. Centrica hadto consider the possible outcomes from a litigatedversus negotiated settlement on the future valueof the asset—the Hewett field (above).
Several conditions complicated the decisionprocess. Acquiring additional equity in the fieldor taking on operatorship could increase reservesand value, and allow Centrica to provide moregas, but the field was old and nearing expensiveabandonment. However, there also was potentialfor workover or developing neighboring fields.
So many elements were involved in the decisionthat the problem appeared quite difficult to solve.
Centrica consulted with AEA Technology plc tohelp them frame the problem. The resulting deci-sion tree was large, requiring evaluation of 7000alternatives with hours of computer runs per out-come that totalled a man-year of effort. An auto-mated solution was needed to generate and inputnumbers for the decision tree. Centrica analystsused the Merak Decision Tree product, and, byparing down some of the constraints, were ableto achieve a solution with 500 outcomes and com-puter run times of 7 minutes (above right).
The benefits of a Decision Tree solution weretwofold: first, the decision-analysis process pro-vided clear insight into the problem. In spite of thecomplexity of the situation, the Decision Treeresults clarified what was driving the decision aswell as the direction to be taken. For the first time,everyone involved was in agreement with therationale for the set of decisions. Second, theMerak tools made it easy to solve the problem andcompleted the calculations and analysis quickly.
Simplifying Decision-MakingFor large organizations like those in thepetroleum industry, it is still people, not pro-cesses, that make complex, expensive decisions.The decision-analysis technique is usually cus-tomized from one organization to the next, butthe most successful system is one of framing theproblem, understanding uncertainties, develop-ing stronger, often hybrid solutions, and balanc-ing risk against expected value.
As the petroleum E&P industry continues topursue prospects in more remote and potentiallysensitive regions, decision-making tools thatincorporate information from all sources ofexpertise will make important contributions toproject success.
Although decisions are ultimately made bypeople, computer and software solutions makethe job easier. Decision-analysis products canhelp identify how sensitive a decision is to all thefactors involved, determine the value of movingahead or gathering data, point decision-makersin the most valuable direction, and produce moreconsistent decisions.
The benefits of a consistent decision-analysisprocess are felt by decision-makers in all parts ofthe company, allowing technical staff and plan-ning organizations to increase the efficiency andvalue of their work. —LS
> Schematic decision tree created to help Centrica analysts reach a decisionon the Hewett field case. The tree uses a compact notation whereby a nodenext to the outcomes of a previous node means that node is repeated foreach input branch. In this way, the first decision node, “Acquire additionalequity,” applies to all three outcomes of the previous node, “Calculatedchange in gas initially in place.” Similarly, the decision node to settle bynegotiation applies to all Yes or No outcomes of the previous decision, andso on. This notation conveys the same information as a whole tree butkeeps the tree compact and manageable.
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> Hewett field, UK North Sea, where low yields prevented Centrica and partners from meeting contracts to provide gas. Decision Tree analysishelped Centrica decide whether to proceed with the ensuing settlement.
10 Oilfield Review
Portfolio Management for Strategic Growth
Tom AdamsJeff LundKerr-McGee Oil and Gas Corp.Houston, Texas, USA
Jack A. Albers Burlington Resources InternationalHouston, Texas
Michael BackJason McVeanCalgary, Alberta, Canada
John I. Howell III Portfolio Decisions, Inc.Houston, Texas
For help in preparation of this article, thanks to FionaMacmillan, London, England; Graeme Simpson, Gaffney,Cline and Associates, Guildford, Surrey, England; and Jim Thorson, Resource Solutions, Denver, Colorado, USA.Capital Planning and Peep are marks of Schlumberger.TERAS is a mark of Landmark.
The petroleum industry is shifting emphasis from cost-cutting to more diverse asset-
management practices. Portfolio optimization, a fast and effective way of analyzing
and improving overall asset value, is one tool that can help.
Ups and downs in the exploration and production(E&P) business are accepted as facts of life in anindustry known for its risks. How high the highs,how low the lows and when they occur are diffi-cult to predict, but the cyclic nature itself is takenfor granted. During good times, companies grow,invest in riskier ventures and profit if the upturnlasts long enough. During slumps, companiesdivest and implement cost-cutting measures.Given such unpredictable swings, how can thepetroleum industry plan for long-term growth?
Many E&P companies have discovered thevalue of managing their assets as a blended col-lection, or portfolio, taking into account interde-pendence of projects rather than consideringinvestments on a project-by-project basis.1
Common practice is for a company to first rankindividual projects either by net present value(NPV) at a given discount rate or by some othermeasure of worth, then initiate those projectsthat fit the current investment budget—startingwith the best.2 This method assumes that projectsare independent, or have no common factors.
1. Ball BC and Savage SL: “Holistic vs. Hole-istic E&PStrategies,” Journal of Petroleum Technology 51, no. 9(September 1999): 74, 76, 78, 80, 82, 84.Howell JI III, Anderson RN, Boulanger A and Bentz B:“Managing E&P Assets from a Portfolio Perspective,” Oil & Gas Journal 96, no. 48 (November 30, 1998): 54-57.Anderson RN, Amaefule J, Forrest M, Howell JI III,Nelson HR Jr and Rumann HA: “Quantitative Tools LinkPortfolio Management with Use of Technology,” Oil &Gas Journal 96, no. 48 (November 30, 1998): 48-50, 53-54.
2. Net present value represents the difference betweenpresent values of cash outflows over the life of the projectand present values of cash inflows, all discounted at aselected interest rate.
Winter 2000/2001 11
The portfolio-management approach capital-izes on the fact that all projects interact, whetherthey involve exploration, development, produc-tion or acquisition. Factors such as market fluctu-ations, performance targets and technical riskare among the elements that tie one project toanother. Even if there is no apparent technicallink between projects, they interact in the sensethat pursuing one project may prevent anotherfrom being pursued, or one project’s success maycause others to be possible. The portfolio per-spective helps decision-makers throughout the
organization understand how projects interact tosatisfy balanced business requirements.
Portfolio management can be thought of as abridge between a company’s vision, or businessstrategy, and the collection of projects that willbring that strategy to fruition. Corporate strategyand metrics—standards of measurement used toquantify the strategy—along with long-term tar-gets for each metric form the foundation. Forexample, results of the company’s existing basebusiness can be compared to targets for metrics
such as earnings, net cash flow, production andreserves (above).3 Discrepancies between base-business results and targets highlight potentialproblems in business performance that must becorrected to meet the targets. However, optimizinga set of assets while simultaneously satisfyingmultiple competing metrics is not a trivial task.
The next structural element of the bridge isselection of assets to pursue, acquire, divest andreconfigure so that overall, the portfolio willmeet strategic targets (next page, top). Excessesin net cash flow, for example, might be investedto bring reserves, production and earnings in linewith target levels. However, it is not likely thatselection of one isolated project will bring base-business results up to target levels. A subset ofprojects must be selected from what is generallya much larger assortment of possible projects.Potential projects may include exploration opportu-nities, current development and production assets,and full- or part-ownership of new acquisitions,
mergers and trades. As the number of projectopportunities grows, corporate or financial plan-ners are faced with the increasingly difficult taskof choosing projects that best achieve companyobjectives.
This article describes some of the techniquesavailable for analyzing and optimizing asset port-folios, including software and consulting servicesthat help rank investments, select projects andpredict the probability of portfolio success. Thesetechniques can be used on multiple levels by avariety of decision-makers: at the highest level,for developing a business strategy; at a secondlevel, for evaluating investment opportunities;and at a project level, for supporting ongoingbusiness. First we look at how a method calledefficient-frontier analysis, designed for analyzingfinancial-investment portfolios, is being tailoredto suit petroleum-industry problems. Then wepresent case studies showing how two oil com-panies are starting to apply these optimizationmethods to asset-portfolio management.
On the Frontier of Efficient PortfoliosEfficient-frontier analysis considers the balancebetween value and risk in the selection of opti-mal portfolios. Efficient-frontier theory was origi-nally developed about 50 years ago to analyzesecurities portfolios, but it differs in somerespects when applied to petroleum portfolios.4
The original idea states that a portfolio can beworth more or less than the sum of its compo-nent projects and that there is not one best port-folio, but a family of optimal portfolios thatachieve a balance between value and risk. Thesestatements remain at the heart of efficient-frontier theory as it pertains to the oil field.
A portfolio is said to be efficient if no otherportfolio has more value while having the same orless risk, and if no other portfolio has less risk
12 Oilfield Review
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> Metrics, or standards of measurement, and strategic targets for a generic exploration and production (E&P) portfolio.Metrics for this study are earnings, production, net cash flow and reserves. Targeted levels for these metrics are displayedas vertical bars. The purple shaded area represents business as usual, or the level achieved by the base business, over a14-year period. Disparity between base-business results and targets shows where company performance falls short.(Adapted from Howell et al, reference 1.)
Winter 2000/2001 13
while having the same or greater value (right).5 Forthe purpose of this example, value is defined asthe mean net present value of the portfolio, andrisk is defined as the semistandard deviation ofthe portfolio’s possible value. Semistandard devia-tion is a statistical measure of the distribution ofpossible values a portfolio could have, given thatthe portfolio value is uncertain. It is calculated in
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> A portfolio perspective. The pink line shows an optimized portfolio solution that meets strategic targets, filling in gapsleft by the base business. (Adapted from Howell et al, reference 1.)
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> Efficient-frontier theory. Oil-industry portfolios plotted by risk and valuedelineate the efficient frontier. A portfolio is efficient if no other portfoliohas more value for the same or less risk, and if no other portfolio has lessrisk for the same or more value. Portfolios B, C, D, E and all blue dots areefficient, while A and other pink dots are not. In the securities-investmentindustry, for which efficient-frontier theory was developed, the efficientfrontier is a continuous line (insert). (Adapted from McVean, reference 4.)
3. Base business is the course of business if current pro-jects are continued but no new projects are undertaken.
4. McVean JR: “The Significance of Risk Definition ofPortfolio Selection,” paper SPE 62966, presented at the2000 SPE Annual Technical Conference and Exhibition,Dallas, Texas, USA, October 1-4, 2000.
5. Markowitz H: Portfolio Selection: Efficient Diversificationof Investments, 2nd ed. Oxford, England: BlackwellPublishing Co., 1991.Bailey W, Couët B, Lamb F, Simpson G and Rose P:“Taking a Calculated Risk,” Oilfield Review 12, no. 3(Autumn 2000): 20-35.
the same way as standard deviation, but only val-ues less than the mean are used (above).
On a plot of value versus risk, the upperboundary of the group of portfolios approachesthe efficient frontier. In the financial-investmentindustry, where each investment can representinfinitesimally small shares in a project, the effi-cient frontier plots as a continuous line. In thepetroleum-asset case, projects are often eitherdone or not done, and so the value-risk space isa collection of points rather than a continuousspace. The efficient frontier itself plots as a setof portfolios rather than a line. In this example,portfolios B, C, D and E are all relatively effi-cient—they lie on or near the frontier—while
portfolio A could become more efficient by reduc-ing its risk or increasing its value, or both.
Several companies and consultants havedeveloped software packages to calculate anddisplay efficient-frontier analysis specifically forE&P portfolios. These include the Perspectivespackage by Portfolio Decisions, Inc. (PDI), CapitalPlanning software by Merak, a Schlumbergercompany, and the Portfolio module of the TERASsoftware by Landmark.6 Merak and PDI havedeveloped a business partnership combining PDI consulting and functionality from thePerspectives package with Merak CapitalPlanning software to create an enhancedportfolio-management process.
In the Merak suite of software, analyzing port-folios in value-risk space follows three steps. First,the set of projects that could potentially beincluded in a portfolio must be collected and eval-uated. Economic evaluations are carried out by theMerak Peep software, an economic engine capa-ble of performing calculations for fiscal regimes allaround the world. Monte Carlo methods are usedto model the uncertainty inherent in each of theseprojects. If required, correlation—such as price—among projects can be set up at this stage.
In the second step, the business strategy isdefined in terms of economic, strategic andphysical requirements for the portfolio.Constraints may be in terms of maximum capitalcost, minimum production, minimum reservesgrowth, or any other strategic metric, and maybe fixed over one or more years of the life of theportfolio. Other factors, such as rig availability,geographic distribution of assets in the corpo-rate strategy and contractual obligations, can beincluded as constraints.
14 Oilfield Review
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> The effect of different definitions of risk on efficient-frontier analysis. The left plot uses the common E&Pdefinition of risk, semistandard deviation of net present value (NPV). Efficient portfolios are blue dots, and are labeled EP01 through EP56, starting at the bottom. On the right, the same portfolios are plotted, but the definition of risk is the probability of exceeding the capital-spending limit in the first year of portfolio life. Someportfolios that were attractive under the original definitions become less so with the new definitions, and viceversa. Portfolios that were efficient before are again shown as blue dots. (Adapted from McVean, reference 4.)
6. For a list of software and service providers in decision,risk and portfolio management: Thorson J: “OpportunityManagement Resources,” Exploration Business Journal4, no. 3 (Third Quarter, 2000): 14-15.
7. A genetic algorithm can be thought of as a guided randomsearch engine. For more on its use in portfolio optimiza-tion: Fichter DP: “Application of Genetic Algorithms inPortfolio Optimization for the Oil and Gas Industry,”paper SPE 62970, presented at the 2000 SPE AnnualTechnical Conference and Exhibition, Dallas, Texas, USA,October 1-4, 2000.
8. McVean, reference 4.9. Albers JA and Howell JI III: “Portfolio Balancing to
Achieve Long Term Strategic Goals,” presented at theEuroforum International Symposium for StrategicPortfolio Management for Upstream Oil and Gas, London,England, March 22-23, 1999.
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Winter 2000/2001 15
The third step combines groups of projects tocreate portfolios, then compares and analyzesthe results. Portfolios can be created manually orthrough a variety of automatic techniques. One ofthese methods is the random portfolio generator,which creates a selection of portfolios that sat-isfy the business strategy. Better portfolios canalso be sought using more intelligent optimizers.For instance, linear programming—featured inthe PDI Perspectives and Landmark TERAS soft-ware—can be used when the description of theproblem and its constraints are linear. Linear pro-gramming delivers optimized solutions for abroad range of business problems. However,some problems require long solution times andmay generate suboptimal results when tackledwith linear programming.
Another optimizer known as the genetic algo-rithm has the power to handle highly nonlinearproblems.7 Because of its robustness, it can bedirected to maximize value or minimize risk forcases in which value and risk can be defined invirtually unlimited ways. With these methods,thousands of projects can be scrutinized andsorted to compile candidate portfolios.
All of these portfolios, however they are gen-erated, can be compared and examined in a vari-ety of ways. The Capital Planning softwareprovides graphical, tabular and data-manage-ment tools for examining and comparing portfo-lios. Some analysts prefer efficient-frontier plotsto evaluate portfolios, while others focus on theprobabilities of meeting metric targets. All areuseful tools for exploring the strengths andweaknesses of different portfolios.
The efficiency of a selected portfolio, or itsposition on a risk-value plot, depends on the defi-nitions of risk and value.8 In the E&P industry, valueis often defined as mean NPV at a specific discountrate, and risk is taken to be the semistandard devi-ation of NPV—representing only the downside inthe variance of NPV, or only the results that are lessthan the mean. A set of portfolios can have a com-pletely different appearance if plotted using differ-ent definitions of risk and value (previous page,bottom). In this example, one plot uses the com-mon E&P definition of risk, and the other quantifiesrisk as the probability of exceeding the capital-spending limit in the first year of portfolio life.Since the cost of each project is uncertain, there isa chance of failing to satisfy this constraint byoverspending in the first year. Under this definitionof risk, some portfolios that previously were inad-equate now appear attractive, and vice versa.
The selection of an optimal portfolio is stronglydependent on which definition of risk is selected.Consequently, it is important to explore multiplerisk definitions in order to more fully understandthe quality of a portfolio, and ultimately makewise decisions about which projects to pursue.
Stepping into Portfolio ManagementIn 1999, Burlington Resources International (BRI),the international division of Burlington Resources,began using the portfolio-management approachto evaluate both its existing properties and newopportunities. After successful implementationin the international division, modern portfolio-management techniques are now being appliedacross the corporation. Historically, decision-makers in the industry would have based oppor-tunity-evaluation decisions on intuition and
experience. However, these insights are subjec-tive and could result in different decisions fromone decision-maker to the next. Under the newapproach, projects are judged based on quantita-tive information about their contribution to thecompany’s long-term strategy and how they inter-act with other projects in the portfolio.9
At BRI, the portfolio model is used in severalways to identify why and how a particular oppor-tunity may be beneficial to the portfolio. Efficientfrontiers of the portfolio with and without thenew opportunity are analyzed to understand itsimpact on total portfolio value, which may behigher than the NPV of the opportunity alone. Theportfolio output is reviewed to determine why thenew opportunity may be valuable to the strategyand to identify any downside risks. Confidencelevels of meeting strategic goals with and with-out the opportunity are studied to evaluate howthe new project affects the likelihood of meetingthose goals. The opportunity is then characterizedfor the decision-maker in terms of its effect on thetotal business performance of the portfolio.
One example of how Burlington Resources hasused portfolio-management tools comes fromevaluation of a specific decision to acquire a pro-duction project. As a starting point, an original,optimized $5.5 billion portfolio is analyzed usingmultiple criteria. Then, economic data for the newproject are added, and the analysis is repeated.
The initial analysis compares targets, base-business values and portfolio values for six met-rics: net income, net cash flow, capital, production,exploration expense and oil reserves (above).Several of these targets were applied as con-straints to the portfolio solution. For example, the
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> Comparison of metrics and targets for a Burlington Resources International (BRI) $5.5 billion portfolio. The base business (purple shading)meets or exceeds only a few of the targets (vertical bars), for example, net income and net cash flow for the years 2002 through 2005. By includingother assets and activities, an optimized portfolio (pink line) can be created that meets many more of the target levels.
net income and net cash flow targets for 2000 and2001 were constraints, and so forced the solutionto equal these values exactly. Similarly, explo-ration expense for 2005 and capital targets for theyears 2001 through 2004 were matched exactly.
The original optimized portfolio is only one ofa set of optimized solutions that can be createdusing the initial set of projects. An efficient fron-tier displays the set of portfolio solutions thatmeet the same performance metrics but have dif-ferent values and risks (above left). This plot,with risk on the vertical axis and value on thehorizontal, contains the same kind of informationas in previously shown efficient frontiers, but issimply transposed (software packages differ intheir presentation style). The $5.5 billion portfolio
was selected as the original test portfoliobecause it has the maximum value for the levelof risk that could be tolerated.
With uncertainty information provided bytechnical specialists, the original portfolio wasanalyzed to determine the probability of meetingeach performance target through the 14-yearperiod (below). The portfolio has a low chance ofmeeting near-term targets for net income, netcash flow and production. It also has a poorchance of staying within the target for capitalbetween 2002 and 2010, and likelihood of mak-ing the reserves target declines after 2006.These displays help decision-makers understandthe impact of project uncertainties at a portfoliolevel, and can be compared with results thatinclude the new project under consideration.
The production-acquisition project, whenadded to the pool of available projects, could notbe selected for inclusion in the portfolio becauseinitial testing showed that it violates the con-straint on capital for the first two years.Following discussion with and approval by thedecision-maker, that constraint was relaxed andthe project was added to the pool.
All the efficient-frontier portfolios generatedfrom the new pool include the new project, so itunquestionably adds value (above right). The newefficient frontier shifts down and to the right invalue-risk space. For the same value, a portfoliofrom the new pool has less risk, and for the samerisk, more value. The increase in value at a con-stant risk is not the same for all portfolios. For low-risk, low-value portfolios, say at risk level 480, the
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> Efficient frontier for the pool of Burlington ResourcesInternational (BRI) portfolios. The plot has a differentshape than previously shown efficient frontiers becausethe axes are interchanged. The curve represents thefamily of minimum-risk, maximum-value solutions that allmeet the same performance constraints. The $5.5 billionportfolio has the highest value that could be achievedbefore the risk component increases significantly andbecomes too extreme.
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> Probability of meeting or exceeding six metric targets with the optimized portfolio. The portfolio has a low probability of meeting first-year ornear-term targets for net income, net cash flow and production. It also has a poor chance of meeting the capital target after the first two years,and a decreasing probability of making the reserves target in the long term.
> Comparison of efficient frontiers with and without thenew production-acquisition project. The efficient frontier ofportfolios that include the new project (pink curve) movestoward higher value for the same risk relative to the efficientfrontier of portfolios without the new project (black curve).
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increase in portfolio value if the new project isincluded is about $1.5 billion. For the higher risk,higher value portfolio at $5.5 billion, the addedvalue is $0.25 billion.
This example demonstrates the differencebetween portfolio value and NPV of a project.The net present value of a project is constant,and measures properties of the project alone.The portfolio value of a project varies as a func-tion of the portfolio and quantifies the cumulativeperformance difference the new project brings.
To understand why this project is valuable tothe strategy, the newly optimized portfolio can be
analyzed without the project (above). Lacking theproduction-acquisition project, the portfolio failsto meet targets for net income in 2005, net cashflow in 2002 and production and reserves in1999. These critical contributions are directlyattributable to the new project, and show whereit adds unique value to the portfolio.
The value is further defined by comparing theprobabilities of success for the new and originalportfolios (below). Significant improvements areclear in the probabilities of meeting short-term
targets for net income, net cash flow and produc-tion. However, the improvements are counterbal-anced by a marginal reduction in the probabilityof meeting the long-term reserves target and theshort-term capital target—not surprising sincethis constraint was relaxed earlier.
With these portfolio-optimization techniques,uncertainties in technical information can betranslated into chances of success. Decision-makers can quantify the value of each project interms of its contribution to total business perfor-mance and its interaction with other projects.
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> Increased probabilities for the new portfolio to meet targets. In some years, the improvements are small, but overall, the new portfolio (pink line)has higher probabilities of meeting targets than does the original portfolio (blue line). Specifically, short-term targets for net income, net cashflow and production are more likely to be met by the new portfolio. Probabilities for meeting long-term targets for reserves and production aremarginally lower.
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A Strategy for GrowthThe portfolio-management approach is helpingmanagers at Kerr-McGee Oil & Gas Corporationtest and refine strategies and communicate thosestrategies within their organization. The portfolioperspective provides a critical link between strat-egy and investment options for the teams thattake the vision of success defined by top man-agement and produce results that consistentlyplace Kerr-McGee in the top quartile among peer companies.
In 1997, Kerr-McGee began an internal pro-cess to examine peer company best practices,and top management adopted a vision to attain a top-quartile ranking among independent oil and gas companies.10 To reach that status, Kerr-McGee would consider all value-addedinvestment options within their two core busi-nesses—oil and gas E&P and the production andmarketing of titanium dioxide chemicals. Theywould also exploit a deepwater core compe-tence, optimize all existing assets and initiate
pay-for-performance incentives based on exter-nal performance benchmarking. Kerr-McGee’schallenge was the same one facing all oil and gas companies: to generate controlledgrowth in an industry that is characterized bydepleting resources.
A representative E&P portfolio example illus-trates the portfolio-modeling methodologyadopted by Kerr-McGee. The building blocks of atypical company’s economic models for availableprojects include proven oil and gas properties,exploitation and exploration projects, and com-mercial opportunities. A sample generic E&Pproduction projection from four types of assets—base production, identified development, explo-ration and commercial opportunities—indicatesa slight increase in production over an eight-yearperiod (above). Comparing production targets forthat period with the chance of reaching thosetargets—90% probability, mean and 10% prob-ability—showed that production targets wereunlikely to be met with the existing mix of assets(next page, top left).
Changing the mix of exploration, commercialopportunities and other projects can help identifyan optimized strategy (next page, bottom). In thisexample, the new asset mix contains a better bal-ance of low-risk, high-certainty opportunities withhigher risk, lower certainty projects. This yields aproduction projection that increases significantlythrough 2007, meeting short- and long-term objec-tives while adding significant value to the portfo-lio. The probability of achieving the productiontarget, as well as other metrics, also increases(next page, top right). The targets correspond tothe metrics used to define success—in this case,top-quartile performance.
In addition to providing a useful tool for test-ing various goals and objectives against the fea-sibility of generating acceptable results, theseplots are an excellent way to communicate therequired changes—both upward to the chairmanand key corporate executives and downward toregional vice presidents and staff. The portfolio-modeling concept helps to quantify many ques-tions that must be asked when determining thestrategic direction and goals of an organization:Is the strategy feasible? How likely is the strat-egy to succeed? How sensitive is the strategy toprice changes or political events? Which goalsare problematic? What other strategic alterna-tives exist?
At Kerr-McGee, the strategy acts as a compass,or general direction, for the company to pursue,and provides a focus to ensure movement towardtop-quartile results. The portfolio-managementapproach continues to be a valued tool after goalsand objectives have been set, as internal andexternal variables change and new opportunitiesarise. The portfolio-modeling concept also providesan excellent mechanism to investigate investmentoptions, determine trade-offs between opportuni-ties and help managers make better business deci-sions that add value to the portfolio at anacceptable risk. While the portfolio-modeling con-cept does not provide “the” answer, it adds disci-pline to the decision-making process.
18 Oilfield Review
> Kerr-McGee E&P production projection before portfolio optimization, showing base production (pink), identified development (dark blue), exploration (light blue) and commercialopportunities (yellow). Production volumes increase slightly from 1999 to 2007.
10. Adams T: “Using Portfolio Models to Optimise andCommunicate Strategy and Achieve Goals,” presentedat the Gulf Coast Association of Geological SocietiesConvention, Houston, Texas, USA, October 26, 2000.
11. Haspeslagh PL: “Portfolio Planning: Uses and Limits,”Harvard Business Review (January-February 1982): 58-73.
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Winter 2000/2001 19
The Portfolio Vision Portfolio management offers a methodology fordecision-makers to evaluate asset portfolios,assess the likelihood of meeting objectives andbridge the gap between targets and the resultsattainable under the current strategy. Many indus-tries already use these methods to reach theirgoals for long-term growth. Results of a surveyconducted almost 20 years ago indicate that sev-eral process-driven industries such as chemical,
food and paper manufacturing, as well as thedownstream petroleum-refining industry, alreadyhad years of experience with the portfolio per-spective.11 Now, 20 years later, the upstreampetroleum industry finally can take advantage ofthe approach, thanks to improved computingpower and analysis tools.
The same survey found that when companiesadopted a portfolio approach, their focus shiftedfrom short term to long term. Instead of ranking
next year’s profit objectives as most important inthe planning process, the top ranking went tolong-range profit objectives.
Some companies reach the portfolio-management stage quickly, within about threeyears, while others take longer. In all cases, astrong commitment from top management is the key to fast implementation, and success isbased on dealing with administrative andorganizational issues related to the portfolio-management approach.
Success also requires that the elegant theoryof portfolio management be tailored to fit thecomplex reality of the E&P business. Several com-putational-optimization tools, such as the MerakCapital Planning software, allow decision-makersto focus on issues that help balance business per-formance and manage diverse opportunities.
Using techniques as sophisticated as thosefound in other E&P domains, such as reservoirmodeling and simulation packages, thesetools help reduce complex problems to man-ageable ones that can be analyzed consis-tently and logically. —LS
Year20001999 2001 2002 2003 2004 2005 2006 2007
Typical Base Before Optimization
10% probabilityMean90% probability
> Production targets (vertical bars) and three curves showing probabilitiesof reaching those targets with the original portfolio. The 90% probabilitycurve falls far below the targets, and the mean and 10% curves also fallshort of the targets. Probability of meeting the targets is less than 10%.
> A new mix of assets for an optimized strategy. Including a better balance of lower risk and higher risk exploration and commercial opportunities yields a production projection thatincreases significantly over the eight-year period (1999 to 2007).
Year20001999 2001 2002 2003 2004 2005 2006 2007
Optimized Strategy
10% probabilityMean90% probability
> Production targets (vertical bars) and three curves showing probabili-ties of reaching those targets with the optimized portfolio. Probabilitiesare high that the new portfolio will meet production targets, and there issome chance that the targets will be exceeded.
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20 Oilfield Review
A Snapshot of Carbonate Reservoir Evaluation
Mahmood AkbarBadarinadh VissapragadaAbu Dhabi, UAE
Ali H. AlghamdiSaudi AramcoDhahran, Saudi Arabia
David AllenMichael HerronRidgefield, Connecticut, USA
Andrew CarnegieDhruba DuttaJean-Rémy OlesenOil & Natural Gas Corporation-SchlumbergerJoint Research CenterNew Delhi, India
R. D. ChourasiyaOil & Natural Gas Corporation, Ltd.Mumbai, India
Dale LoganDave StiefMidland, Texas, USA
Richard NetherwoodJakarta, Indonesia
S. Duffy RussellAbu Dhabi Company for Onshore Oil OperationsAbu Dhabi, UAE
Kamlesh SaxenaMumbai, India
For help in preparation of this article, thanks to Kamal Babourand Robert Dennis, Al-Khobar, Saudi Arabia; Tim Diggs,Shell International EP, Houston, Texas, USA; Jack Horkowitz,Sugar Land, Texas; Fikri Kuchuk and participants in the firstannual MEA Carbonates Forum, Dubai, UAE; Chris Lenn,Houston, Texas; T. S. Ramakrishnan and Yi-Qiao Song,Ridgefield, Connecticut, USA; Charlotte Sullivan, Universityof Houston, Texas; W. Bruce Ward, Earthworks LLC,Norwalk, Connecticut.BorTex, CMR (Combinable Magnetic Resonance), CNL(Compensated Neutron Log), ECS (Elemental CaptureSpectroscopy), ELAN (Elemental Log Analysis), FMI(Fullbore Formation MicroImager), GeoFrame, Litho-Density,MDT (Modular Formation Dynamics Tester), PL Flagship, PS Platform, Q, RockCell, RST (Reservoir Saturation Tool),RSTPro, SpectroLith and TDT (Thermal Decay Time) aremarks of Schlumberger.
Carbonate reservoir evaluation has been a high priority for researchers and oil
and gas producers for decades, but the challenges presented by these highly
heterogeneous rocks seem to be never-ending. From initial exploration through
mature stages of production, geoscientists, petrophysicists and engineers work
together to extract as much information as possible from their data to produce
maximum reserves from the ground.
Carbonate reservoirs present a picture ofextremes. Reservoirs can be colossal thoughtheir pores can be microscopic (next page, top).Matrix permeability can be immeasurably low,while fluids flow like rivers through fractures.Evaluation techniques that succeed in sandstonereservoirs sometimes fail in carbonate reservoirs.These variations complicate both reservoir evalu-ation and hydrocarbon recovery. However,researchers are working to overcome these prob-lems because of the economic significance of oilproduction from carbonate reservoirs, especiallygiant and supergiant fields in the Middle East.
The potential rewards are great: about 60% ofthe world’s oil reserves lie in carbonate reser-voirs, with huge potential for additional gasreserves, particularly in the Middle East. In thisarticle, we examine ways to evaluate carbonatereservoirs at the scale of cores and well logs withexamples from both research and operationsgroups around the world (next page, bottom).1
Methods range from tried-and-true to experimen-tal, and represent a subset of current initiatives
rather than a comprehensive review. The resultsof borehole-scale evaluations play significant rolesin larger scale field development, simulation andmanagement efforts. We also discuss the impactof these results on ongoing research efforts.
Why the Fuss about Carbonate Rocks?Carbonate sedimentary rocks differ from silici-clastic sedimentary rocks in several ways.Siliciclastic rocks form as sediments are trans-ported, deposited and lithified, or compacted andcemented into solid rock. Most carbonate rocksdevelop from biogenic sediments formed by bio-logical activity, such as reef building and accu-mulation of the remains of organisms on theseafloor. Other types form as water evaporatesfrom shallow onshore basins or as precipitatesfrom seawater. The fragments that make up mostcarbonate rocks typically have undergone muchless transport than most siliciclastic sediments.
Siliciclastic rocks are predominantly sand-stones and shales that contain a wide variety ofminerals and particles, including quartz, feldspar,
1. For a general introduction to carbonate interpretation:Akbar M, Petricola M, Watfa M, Badri M, Charara M,Boyd A, Cassell B, Nurmi R, Delhomme J-P, Grace M,Kenyon B and Roestenburg J: “Classic InterpretationProblems: Evaluating Carbonates,” Oilfield Review 7, no. 1 (January 1995): 38-57.
Winter 2000/2001 21
> Carbonate heterogeneity. Photomicrograph pairs show three rock fabrics from the same reservoir. The images at the top are conventional plane-polarized light photomicrographs of thin sections. The cathodoluminescence photomicrographs (bottom) reveal different generations of carbonate minerals formed during diagenesis. Each rock fabric has a different response to nuclear magnetic resonance (NMR) because of thedifferent relationships of the pores within and between grains. Differences in depositional facies and stratigraphic position produced three distinctdiagenetic pathways. In the ooid grainstone (left), the cores of the ooids were dissolved early in the depositional history. Calcite cements filledboth intergranular and intragranular porosity. The fabric-retentive, dolomitized ooid-peloidal grainstone (center) initially underwent minor diagenesisduring which some skeletal grains were dissolved. Fine dolomite crystals then replaced the sediment and preserved the original fabric at an earlystage. Later, dolomite cement filled some of the large moldic pores. Sucrosic dolostones (right) represent peloidal grainstone that was replaced byfine sucrosic dolomite crystals, destroying much of the original depositional texture.
60° N
40° N
20° N
0°
20° S
40° S
60° S Reef Shelf carbonate Deep carbonate Carbonate oil province
> Distribution of carbonate rocks. The black circles denote locations of examples described in this article.
clay minerals, fragments of preexisting rocks andremnants of plants or animals. Carbonate rocksconsist of a more limited group of minerals—predominantly calcite and dolomite. Other miner-als less commonly present in carbonate rocks arephosphate and glauconite; secondary mineralsinclude anhydrite, chert, quartz, clay minerals,pyrite, ankerite and siderite.
These differences result in entirely differentclassification systems for clastic and carbonaterocks, with clastic rocks distinguished by graincomposition and size, and carbonate rocks differ-entiated by such factors as depositional texture,grain or pore types, rock fabric or diagenesis(right).2 Differentiating present-day flow unitsfrom original depositional units is becoming moreimportant than other aspects of classificationbecause optimal well placement depends onunderstanding present-day flow units.
Once deposited, sediments undergo diagene-sis, the postdepositional chemical and physicalchanges that transform the sediment into solidrock. Carbonate diagenesis can significantlymodify pore space and permeability. Carbonaterocks are highly susceptible to dissolution; grainscan be dissolved to form new pore space, anddissolution along fractures and bedding planescan produce large vugs and caves. Clastic dia-genesis normally does not involve a change inmineralogy. Carbonate diagenesis, however,commonly involves replacing the original calciteand aragonite with the mineral dolomite, a pro-cess called dolomitization, which can improvethe hydrocarbon-producing characteristics.
While both clastic and carbonate rocks areusually buried, compacted and cemented, carbon-ate sediments contain significant amounts of themetastable minerals aragonite and magnesiumcalcite; calcite itself is readily dissolved andreprecipitated by percolating pore fluids.Carbonate rocks are, therefore, more likely toundergo dissolution, mineralogical replacementand recrystallization. These effects vary according
to temperature, pore-fluid chemistry and pres-sure. Carbonate diagenesis commonly beginswith marine cementation and boring by organ-isms at the sediment-water interface prior toburial. It continues through shallow burial withcementation, dissolution and recrystallization,and then deeper burial where dissolution pro-cesses known as pressure solution may form suchfeatures as stylolites.3
When confronted with core samples or imagelogs from carbonate rocks, even casual observersnotice the tremendous variety of pore types andsizes, and the irregular distribution of pores.Pores in clastic rocks are predominantly betweenthe grains, or intergranular, and uniformly dis-tributed throughout the rock matrix. Intergranularpores are also present in carbonate rocks.Intragranular porosity may be common within
carbonate grains as a primary pore type, or maydevelop when grains, such as shell fragments,are partially dissolved. Moldic porosity preservesthe shapes of dissolved shell fragments or otherconstituents. Carbonate rocks typically have a farwider range of grain shapes than most siliciclas-tic rocks. Clearly, several types of porosity maycoexist in a carbonate reservoir, ranging frommicroscopic to cave-sized, which makes porosityand permeability estimation and calculation ofreserves extremely difficult.4
Another feature of carbonate rocks is theirsusceptibility to dissolution. At the surface, aswater and carbon dioxide form carbonic acid,dissolution can lead to impressive karst topogra-phy, including sinkholes, caves and intricatedrainage patterns like “disappearing” streams inactive karst systems.5 Inactive karst systems, or
22 Oilfield Review
Mudstone Wackestone Packstone Grainstone Boundstone Crystalline
Less than10% grains
More than10% grains
Grain-supported Lacks mud and isgrain-supported
Originalcomponents werebound together
Depositionaltexture notrecognizable
Mud-supported
Contains mud, clay and fine silt-size carbonate
Original components not bound together during deposition
Depositional texture recognizable
Pore types
Intergranular, Intercrystalline Moldic, Interfossil, Shelter Cavernous, Fracture, Solution-enlarged fracture
> Carbonate rock classification. Carbonate rocks are differentiated by depositional texture,grain types, rock fabric or other factors. The Dunham classification, published in 1962, iswidely used to categorize carbonate rocks according to the amount and texture of grainsand mud. The classification by Embry and Klovan follows the Dunham scheme, but addscategories for rocks formed by organisms that grew together, such as colonies of oysters.Describing pore types further refines rock descriptions; Lucia’s classification is widelyaccepted. (Adapted from Dunham, in Ham, reference 2, and Lucia, reference 2.)
2. Geologists have developed many different carbonaterock classification schemes. Some are general schemes;others are specific to a reservoir, basin or region. For moreon carbonate rock classification:Embry AF and Klovan JE: “A Late Devonian Reef Tract onNortheastern Banks Island, N.W.T.,” Bulletin of CanadianPetroleum Geology 19, no. 4 (December 1971): 730-781.Ham WE (ed): Classification of Carbonate Rocks,American Association of Petroleum Geologists Memoir 1.Tulsa, Oklahoma, USA: AAPG, 1962.Lucia FJ: Carbonate Reservoir Characterization. New York,New York, USA: Springer, 1999.
3. Stylolites are interpenetrating, sutured surfaces that formduring diagenesis.
4. For more about permeability evaluation for reservoircharacterization: Ayan C, Douglas A and Kuchuk F: “A Revolution in Reservoir Characterization,” MiddleEast Well Evaluation Review no. 16 (1996): 42-55.
Baadaam H, Al-Matroushi S, Young N, Ayan C, Mihcakan Mand Kuchuk F: “Estimation of Formation Properties UsingMultiprobe Formation Tester in Layered Reservoirs,”paper SPE 49141, presented at the SPE Annual TechnicalConference and Exhibition, New Orleans, Louisiana,USA, September 27-30, 1998.Kuchuk F: “Interval Pressure Transient Testing with MDT Packer-Probe Module in Horizontal Wells,” paper SPE 39523, presented at the SPE India Oil and GasConference and Exhibition, New Delhi, India, February 17-19, 1998.Kuchuk FJ, Halford F, Hafez H and Zeybeck M: “The Use of Vertical Interference Testing to Improve ReservoirCharacterization,” paper ADIPEC 0903, presented at the9th Abu Dhabi International Petroleum Exhibition andConference, Abu Dhabi, UAE, October 15-18, 2000.
Lenn C, Kuchuk FJ, Rounce J and Hook P: “HorizontalWell Performance Evaluation and Fluid Entry Mechan-isms,” paper SPE 49089, presented at the SPE AnnualTechnical Conference and Exhibition, New Orleans,Louisiana, USA, September 27-30, 1998.
5. Karst was first recognized and described in the Dinariccarbonate platform of Yugoslavia, also known as theKarst region. Karst is found throughout the world.
6. Lucia, reference 2: 7.7. For more on the geologic history of Indonesia:
Netherwood R: “The Petroleum Geology of Indonesia,” in Indonesia 2000 Reservoir Optimization Conference.Jakarta, Indonesia: PT Schlumberger Indonesia,November 2000: 174-227.
Winter 2000/2001 23
paleokarst, may form reservoirs dominated byangular rock fragments produced during cavecollapse. For the oil industry, karst can be amixed blessing—caves can result in catas-trophic bit drops and fluid losses during drilling,but karst also can result in extremely high poros-ity and permeability.
Given the heterogeneity of carbonate rocks, itis not surprising that hydrocarbon production fromthese formations often is heavily influenced bythe presence of faults and fractures, particularlyin older Mesozoic and Paleozoic reservoirs.Experts caution that relationships between poros-ity and permeability in carbonate rocks cannot bedetermined without understanding the pore-sizedistribution (see editorial, “Linking Petrophysicaland Geologic Information: A Task for Petro-physics”).6 Because carbonate reservoirs presentenormous challenges, they have fueled substan-tial research efforts within Schlumberger and thepetroleum industry for decades. These efforts takemany different forms as experts struggle to solvedifficult problems in carbonate reservoirs.
Carbonate Evaluation in IndonesiaIntegrated carbonate evaluation is important in allstages of exploration and production. In 1997, anoperator drilled a well in the Sibolga basin, off-shore northwest Sumatra, to evaluate a carbon-ate buildup prospect identified in seismic data(above). A full petrophysical and stratigraphicanalysis of well logs and seismic data was thenundertaken to understand drilling results andreevaluate the prospectivity of this play.
Biostratigraphic analysis of well cuttings indi-cated the buildup formed in the Middle Miocene,around 13 million years ago, in a forearc settingsimilar to that of today, with consumption ofIndian Plate oceanic crust beneath Sumatraalong the Sunda trench. This was a period ofglobal eustatic rise.7
The well was evaluated using openhole wire-line logs—gamma ray, resistivity, density andneutron—and, because mud circulation problemsduring drilling prevented conventional coring, theFMI Fullbore Formation MicroImager tool alsowas run. Integration of wireline logs, especiallythe FMI image, with seismic data was key todetermining depositional facies. Prior to forma-tion of the carbonate buildup, massive shaleswere deposited in a low-energy offshore environ-ment. Subsequent laminated shale and cross-bedded sandstone were deposited as waterdepths became shallower and depositionalenergy increased. The prograding reef-front suc-cession was produced by smaller buildups thatcoalesced to form one large carbonate platform.Eventually, relative sea level rose rapidly and sub-merged the buildup (below).
The prospect was expected to contain bio-genic gas. Further study of well logs and imagelogs, however, showed that reservoir-quality car-bonate rocks formed almost continuously in theabsence of internal sealing rocks. Top seals onthe reservoir were deposited long after gas gen-eration, so any biogenic gas that was generatedwas not trapped. As a consequence, the com-pany decided against further appraisal and wasable to redirect its resources. Nevertheless, thisexample underscores the usefulness of integrat-ing all available data to develop reasonable 3Dgeological models of reservoirs as early as possi-ble in the exploration process.
Black lagoonal fill
Wave-resistant reef facies
Back reef from storm and talus deposits
M1
M3
SW NE
> Seismic interpretation. This seismic line from the prospect was flattenedon the M3 horizon, presumably a horizontal or nearly horizontal depositionalsurface. The prograding reef-front succession was produced by initialsmaller sized buildups that coalesced to form one large carbonate platform.Eventually, relative sea level rose, submerging the buildup.
MALAYSIA
ASIA
AUSTRALIA
CentralSumatra basin
SouthSumatra basin
Bengkulubasin
Pagar Jatigraben
Singkelgraben
NorthSumatra basin
SINGAPORE
SUMATRA
Mentawai fault zone
Keduranggraben
Pinigraben
Sibolgabasin
Sumatra fault zone
0 100 200 km
0 62 124 miles
VolcanoesVolcanic rocks
Sunda trench
Sumatra forearc basin
> Location of the Sibolga basin, Indonesia.
Carbonate Evaluation in West Texas, USAIn contrast to the previous example from theexploration stage, the Permian Basin of WestTexas, USA, is renowned for vast carbonatereservoirs, many of which are now undergoingsecondary and tertiary recovery. New technologyand methods greatly enhance production byoffering interpreters a better understanding ofhow reservoir heterogeneity affects performanceand which zones contribute to flow.8 Perhaps themost significant contributions come from nuclearmagnetic resonance (NMR), borehole-image andproduction logs.
When using the CMR Combinable MagneticResonance tool in carbonate formations, engi-neers in West Texas adjust the acquisition para-meters to compensate for longer polarizationtimes relative to clastic formations.9 Typical CMRlogging speeds in this region are 90 to 140 ft/hr [30 to 40 m/hr], as opposed to speeds of 300 ft/hr[100 m/hr] in clastic rocks. Increased cutoff valuesfor T2, more than three times the T2 cutoffs usedin sandstones, have been determined from labo-ratory measurements of cores and are applied tospecific fields by local interpreters. These stepsimprove the measurement of porosity, permeabil-ity and fluid saturation in rocks whose pore sizes,shapes and pore-throat connections vary morewidely than in most clastic rocks.
In addition to adjusting log-acquisitionparameters, use of different logging suitesallows more realistic interpretation of carbonatereservoirs. In West Texas dolomite formations,high gypsum content results in overestimation of porosity when using standard crossplottingtechniques. Integrating results from CNLCompensated Neutron Log, Litho-Density andCMR logs provides better estimates of porosityand permeability. If core data are not available,which is the norm, combining these logs with theECS Elemental Capture Spectroscopy sonde forgeochemical logging also can help quantify min-eralogy to obtain more accurate porosity. Addinga borehole image log, such as from the FMI tool,further improves understanding of pores, particu-larly vugs, which commonly are distributed irreg-ularly in carbonate reservoirs (left).
Because of the maturity and marginal eco-nomics of some West Texas fields, operatorsmust minimize data-acquisition costs. Since the
24 Oilfield Review
10,399
10,400
10,401
10,402
10,403
10,404
10,405
0 100
> Contrasts in West Texas carbonate rocks. These FMI images show clean,relatively homogeneous carbonate rock (top) and fractured, vugular carbonaterock with shale filling pores (bottom). ECS data displayed in Track 1 indicatevolumes of carbonate in blue, clay in brown and quartz in yellow.
10,391
10,392
10,393
10,394
10,395
10,396
10,397
0 100
8. For more examples from West Texas: Newberry BM,Grace LM and Stief DD: “Analysis of Carbonate DualPorosity Systems from Borehole Electrical Images,”paper SPE 35158, presented at the Permian Basin Oiland Gas Recovery Conference, Midland, Texas, USA,March 27-29, 1996.
10. Logan D, Strubberg C and Conner J: “New ProductionLogging Sensors Revolutionize Water/CO2 Conformancein the Pumping Wells of West Texas,” paper SPE 59716,presented at the Permian Basin Oil and Gas RecoveryConference, Midland, Texas, USA, March 21-23, 2000.This paper also discusses use of TDT Thermal DecayTime logs with the PS Platform tool to evaluate carbon-dioxide flooding.
9. For more on NMR applications in West Texas: LoganWD, Horkowitz JP, Laronga R and Cromwell D: “PracticalApplication of NMR Logging in Carbonate Reservoirs,”paper SPE 38740, presented at the SPE Annual TechnicalConference and Exhibition, San Antonio, Texas, USA,October 5-8, 1997.
Winter 2000/2001 25
cost of acquiring core may be higher than thecost of a wireline log, interpreters have cali-brated cores and logs to make sure that interpre-tations are robust, building confidence in loginterpretations when core data are unavailable.This is especially important when evaluating thepermeability of waterflooded reservoirs. The abil-ity to distinguish high-permeability zones allowsoperators to plug swept zones and improve flood-ing of unswept zones.
Customized solutions in West Texas includeproduction logging below electrical submersiblepumps.10 In one field, engineers from Schlumbergerand an operating company were able to evaluatefluid entry in several wellbores by adapting the PSPlatform tool for use below the pump. Custom-built G-plates were installed above and below thepump to guide the wireline and pump cables toavoid tangling around the tubing and employing amodified wellhead assembly.
As one producing well was logged with thePS Platform tool below the pump, it becameapparent that oil entered from an interval abovethe reservoir section, and that the zone of inter-est was actually producing water. Evaluationwith the FMI tool revealed two thin, porous zonesuphole that were contributing the oil flow(above). By using a bridge plug to shut off thewater from the waterflooded reservoir section,the operator saved significant water recyclingcosts while increasing the oil production from thezone above. There were additional savings in off-set wells because acid stimulations were nolonger performed in water-prone zones. As aresult of these types of experiences, operatorsare striving for earlier identification of high-per-meability water conduits.
Carbonate Case Studies at Schlumberger-Doll ResearchResearchers at Schlumberger-Doll Research(SDR), Ridgefield, Connecticut, USA, have fol-lowed many different paths, from complextheoretical methods to simpler approaches thatemphasize well-by-well evaluation. The com-mon goal, however, has been to develop inter-pretations that can be incorporated intofield-scale solutions.
Any improvement in recovery from giantcarbonate reservoirs has a tremendous impact onoil and gas supplies. Reservoir heterogeneitycomplicates everything from drilling to well com-pletions, including petrophysical evaluation, sodeveloping a reliable log-based interpretationmethodology is essential for field development.Reservoir heterogeneity prohibits relating poros-ity and permeability directly, as might be donewhen analyzing relatively homogeneous reser-voirs. Therefore, it is crucial to distinguish car-bonate lithologies and rock fabrics to optimize
> Complementary logging results. Production logging in this West Texas well showed that oil entered from zones above the zone of interest, and that thezone of interest was actually producing water. Evaluation with the FMI tool revealed that two thin, porous zones around 4660 ft contributed the oil flow. Thedark lines in the image log are leached bedding planes.
4650
4660
4670
Water rate
Spinner
0 25
Bubble count
counts/sec
cycles/sec
0 30MD
1:400 ftGamma
ray Probe 1 R8
API deg0 1021
0
360
Caliper Y
in.3 6
Caliper X
Holdup
1
0Bubble
in.3 6
Oil rate
B/D0 1500
B/D0 1500
B/D0 4500
B/D0 300Total flow rate
Amplifiedoil rate
Temperature
°F 105
Pressure
psi75 325
Oil holdup
Oil holdup
m3/m30.75 1
WF densityg/cm30.95 1.15
4600
4650
4700
30% Porosity -10%Depth,
ft
production, whether one is deciding how to dealwith a single well or preparing to simulate pro-duction from an entire field.
Work at SDR in the 1990s led to an integratedcarbonate-evaluation methodology for theThamama formation, a lower Cretaceous reser-voir in the Middle East.11 This methodology wasapplied to studies of other carbonate reservoirsin the UAE and West Texas. Recognizing the widevariety of carbonate rocks worldwide, SDRresearchers decided, in 1997, to embark on aseries of additional studies. Several carbonatecase studies have been, or are being, conductedjointly by SDR scientists and engineers with theircounterparts from operating companies.
Investigations of two giant fields, the BombayHigh field offshore India and a field in the MiddleEast, indicate that the variety of rock types andheterogeneity within a given carbonate reservoirlend themselves to customized, formation-specific evaluations, particularly in cases ofextreme diagenetic alteration. Both studies, com-pleted in 2000, draw on techniques that rangefrom conventional petrophysical and petro-graphic analysis to the first application of a new
laboratory NMR method, called decay due to dif-fusion in internal field (DDIF).
Bombay High study—The giant Bombay High field, offshore western India, covers about1200 km2 [463 sq miles] and has more than 600 development wells. Discovered in 1974 byOil & Natural Gas Corporation, Ltd. (ONGC), thefield was brought on production in 1976. Themain pay is the Miocene L-III limestone, a reser-voir with ten hydrocarbon-bearing layers sepa-rated by shale, clay-rich limestone and tightlimestone. The layers are not continuous andhave poor vertical communication. As of April2000, the field produced 297 MMT [327 M tons]crude oil and 110 BCM [3.9 TCF] natural gas, andis now in the mature phase. A redevelopmentprogram has been prepared to improve recovery.
ONGC sought better understanding of reser-voir petrophysics to control water breakthroughin heterogeneous, layered carbonate rocks thathave undergone waterflooding since 1984.12 Theprimary reservoir generally is not fractured, soONGC suspected that a few high-permeabilityzones were contributing to water breakthrough.
The challenge, therefore, was to develop a con-sistent log-based method to identify these high-permeability zones. For the Bombay High study,61 core plugs from the N5-9 well were evaluatedalong with the logs.
Middle East study—Scientists and engineersfrom a Middle East operating company and SDRevaluated the complexities of a giant gas field thatproduces from prolific carbonate rocks. Wirelinelogs and 80 core plugs from one well form theframework for an integrated interpretation.
Researchers applied much of the same ana-lytical methodology in both cases. At the outset,both operators believed that the volume of clay(Vclay) would prove to be the key problem that thestudies should solve. Accurately quantifying theabundance of clay minerals is essential to accu-rate porosity and saturation calculations, whichin turn affect reserve estimates.
Quantitative mineralogical and chemicalanalysis of core samples conducted at SDRenhanced petrophysical analysis of the reservoirs.Mineralogy was evaluated using a proprietaryFourier transform infrared (FT-IR) technique that
26 Oilfield Review
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> Uncertainty in clay content. Because of clay-volume concerns, mineralogy and chemistry of MiddleEastern (top) and Bombay High (bottom) carbonate rocks were analyzed. Gamma ray responses, com-puted from the chemical analysis of thorium (Th), uranium (U) and potassium (K), do not correlate wellwith clay content in either case. A much better correlation can be made with aluminum (Al), and formsthe basis of the SpectroLith clay-volume computation.
Winter 2000/2001 27
relates infrared absorbance spectra to 50 mineralstandards from silicate, carbonate, clay and othermineral families.13 Chemical analysis included X-ray fluorescence, neutron activation and induc-tion-coupled mass spectrometry. All of theseresults were integrated with log data. An impor-tant result of the core analysis was that gammaray logs alone would have indicated an incorrectclay content in both reservoirs (previous page).
Therefore, a method to accurately determinemineralogy without core analysis is criticallyimportant for future reservoir characterization.
The ECS logging tool allows accurate estima-tion of mineralogy, clay concentration and lithol-ogy, and also can be used to evaluate total andeffective porosity and hydrocarbon type.14 The ECStool uses a spectrometer to measure concen-trations of certain elements—calcium, silicon,
sulfur, iron, titanium, gadolinium, sodium andmagnesium—that reflect the concentrations ofspecific minerals in the formation. The data can beanalyzed to compute mineralogy in terms of sand,clay, evaporite and carbonate minerals bySpectroLith lithology processing. In both cases,the SpectroLith-processed ECS results give a morerealistic picture of the mineralogy, as confirmed bymineralogical analysis of cores (above).
> SpectroLith-processed ECS data for accurate mineralogy that can be con-firmed by core analysis. In a Middle Eastern formation (top), the processedECS logs of carbonate, anhydrite, clay and sand content correlate well withcore data (red circles). The Bombay High results (bottom) show good agree-ment between core data and processed ECS data for carbonate, clay, pyriteand sand content, with slight discrepancies resulting from core plugs thatsampled thin shale beds not seen at the resolution of the log.
11. Ramakrishnan TS, Rabaute A, Fordham EJ,Ramamoorthy R, Herron M, Matteson A, Raghuraman B,Mahdi A, Akbar M and Kuchuk F: “A Petrophysical andPetrographic Study of Carbonate Cores from theThamama Formation,” paper SPE 49502, presented atthe 8th Abu Dhabi International Petroleum Exhibitionand Conference, Abu Dhabi, UAE, October 11-14, 1998.
12. Tewari RD, Rao M and Raju AV: “Development Strategyand Reservoir Management of a Multilayered GiantOffshore Carbonate Field,” paper SPE 64461, presentedat the SPE Asia Pacific Oil and Gas Conference andExhibition, Brisbane, Queensland, Australia, October16-18, 2000.
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13. Herron MM, Matteson A and Gustavson G: “Dual-rangeFT-IR Mineralogy and the Analysis of SedimentaryFormations,” paper 9729, presented at the 1997 Societyof Core Analysts Conference, Calgary, Alberta, Canada,September 7-10, 1997.Matteson A and Herron MM: “Quantitative MineralAnalysis by Fourier Transform Infrared Spectroscopy,”paper 9308, presented at the 1993 Society of Core AnalystsConference, Houston, Texas, USA, August 9-11, 1993.
14. Herron SL and Herron MM: “Application of NuclearSpectroscopy Logs to the Derivation of Formation MatrixDensity,” Transactions of the SPWLA 41st AnnualLogging Symposium, Dallas, Texas, USA, June 4-7, 2000,paper JJ.
Another key goal of these integrated studiesis to identify and understand the various poretypes, including micropores, mesopores andmacropores, and the effect of their distribution onproduction (above). Micropores, with pore-throatdiameters less than 0.5 micron, usually containmostly irreducible water and little hydrocarbon.Mesopores, with pore-throat diameters between0.5 and 5 microns, contain significant amounts ofoil or gas. Macropores, with throats measuringmore than 5 microns in diameter, are responsible
for prolific production rates in many carbonatereservoirs, but often provide pathways for earlywater breakthrough, leaving considerable gas andoil behind in mesopores. NMR logging hasimproved evaluation of porosity, pore-size distri-bution and bound fluids (next page).
NMR logging tools, such as the CMR device,use large magnets to strongly polarize hydrogennuclei in water and hydrocarbons as they diffuseabout the pore space in rocks. When the magnet
is removed, the hydrogen nuclei relax. The relax-ation time, T2, depends on the pore-size distribu-tion; larger pores typically have longer relaxationtimes. Tar and viscous oils relax more quicklythan light oil or water. The variations in relax-ation time produce a T2 distribution from whichfluid components and pore sizes are interpreted.
An important outgrowth of the studies wasthe ability to classify pores in the three size cat-egories using NMR. This success resulted fromthe discovery that, in contrast with the earlier
28 Oilfield Review
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> Understanding the distribution of micro-, meso- and macropores, a key goal of integrated reservoirstudies. Photomicrographs and scanning electron microscope (SEM) images of thin sections of BombayHigh carbonate rocks illustrate the three pore types. The enlarged thin section view (top) shows thelocations of the numbered SEM images. Blue epoxy has been injected into the sample to highlight theporosity. SEM images include 400-micron scale bars, except for the 25-micron scale in the bottomright image. The SEM image of Location 1 (middle row, left) reveals a black macropore at lower left,and meso- and micropores in the dark gray region. The SEM image of Location 4 (middle row, center)shows mesopores. The next image (middle row, right) includes the lower left portion of Location 1, asseen by the macropore surrounded by euhedral calcite crystals—crystals whose growth has not beenrestricted by adjacent grains. The bottom left image includes the upper right part of Location 3, butshows leaching around the edge of the dark macropore, and micropores are barely visible as darkdots. The bottom right image is an enlargement showing details of the micropore system in Location 3.
Winter 2000/200129
carbonate rocks studied, T2 decay-time distribu-tions are directly useful for interpretationbecause diffusive coupling is not a problem.Diffusive coupling results when spinning protonsmove between micro- and macropores during themeasurement, blurring the T2 distribution.15
A new technique developed at SDR allowsresolution of the three common pore sizes usingspectra quantified in size rather than relaxation
time. The new method, DDIF, delivers a quan-titative pore-size distribution that is especiallypowerful in carbonate rocks.16 A laboratory mea-surement technique with its own associatedprocessing, DDIF is separate and distinct fromthe conventional NMR T2 experiment. The newinsight derived from DDIF studies is that the con-ventional T2 distributions resemble the DDIF
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> Bombay High samples containing meso- and macropores (top left) and micro-, meso- and macropores(top right). The plots show distributions of pore-throat diameters and T2 distributions for each sample.Pores are assigned to pore types by their pore-throat diameter as measured by mercury injection(top two plots). Pores to the left of the red lines are micropores, those between the red and blue linesare mesopores, and macropores lie to the right of the blue lines. Comparison with the T2 distributions(lower plots) shows that the porosity partitioning can be accomplished using T2 cutoffs, a valuableapplication of NMR logging in carbonates.
15. For more on NMR technology, including permeabilitytransforms and NMR in carbonate rocks: Allen D, Flaum C,Ramakrishnan TS, Bedford J, Castelijns K, Fairhurst D,Gubelin G, Heaton N, Minh CC, Norville MA, Seim M,Pritchard T and Ramamoorthy R: “Trends in NMRLogging,” Oilfield Review 12, no. 3 (Autumn 2000): 2-19.
16. Song YQ, Ryu S and Sen P: “Determining Multiple LengthScales in Rocks,” Nature 406, no. 6792 (July 13, 2000):178-181.
distributions. This confirms that there is no diffu-sive coupling, so T2 distributions are valid fordistinguishing pore sizes (above).
Scanning electron microscope (SEM) imageshelped explain the absence of diffusive couplingin both cases (above right). Thus, in both forma-tions, the T2-distribution shape is similar to pore-size distribution determined by mercury injectionand DDIF. Conventional T2-based NMR analysis,discussed in detail below, was applied to determine both pore-size distributions and per-meability. An important result of the studies ismore realistic calculation of permeability using CMR logs.17
In the Bombay High field, CMR data confirmedgenerally low permeability with numerous high-permeability streaks in leached, macroporous
zones. The Timur-Coates permeability transform,which uses total porosity and the ratio of free-fluid volume to bound-fluid volume to com-pute permeability, was selected to determinepermeability using CMR data because it correctlypartitions the pore network found in theseleached, macroporous limestones. Because high-permeability streaks are so important to produc-tion and because hydrocarbon signal masksmacropores on CMR logs, FMI data were incor-porated into the log processing (next page).
The SDR equation, which relates permeabilityto the logarithmic mean of T2 and total porosity,was used to determine permeability from CMRdata for the Middle East well. In dolostones,more realistic permeability estimates were made
using NMR T2 values from the log and the corerather than using a porosity-permeability rela-tionship alone. Permeability estimates in thelimestones, which had more variable pore sys-tems than the dolostones, also improved, but notas dramatically. The more accurate permeabilitycalculations used a correction factor based onthe temperature sensitivity of NMR T2 values ineach formation.
Three different NMR T2 cutoff values wereused in this well, allowing NMR logs to be usedto determine micro-, meso- and macroporosity.The ratio of NMR T2 values to pore-throat diam-eter determined by mercury injection (NMRT2/throat) in 22 samples also showed three dis-tinct NMR T2/throat classes that correspond tofabric classes observed in thin-section analysis.
30 Oilfield Review
> Pore size analysis by DDIF, NMR T2 distribution and mercury injection. In the top row, DDIF spectra(red) are used to determine whether the NMR T2 distribution (blue) truly reflects the pore-size distributionby comparing the two spectra. The horizontal axis of the T2 distributions has been multiplied by 100 tofacilitate the overlay. For these three samples, the match is excellent. The two first samples are dolo-grainstones; the third is a sucrosic dolomite. The lower plots compare the mercury-injection distributions(blue) with the DDIF distributions (red). Mercury porosimetry uses mercury injection to determine thecapillary pressures of the connected pore space. The plots obtained from these data are interpretedas the pore-throat sizes. On the other hand, DDIF measures the pore openings, including pore bodiesand throats. Superimposing the two results reveals the connectivity of the pore network. For thesucrosic dolomite (right), the overlay reveals a network consisting of pore bodies with a diameter of20 microns connected by throats of 1 to 2 microns. For the two grainstones, the pore-body size is largerand covers a broader range. They share a network of pore throats with a diameter of 2 microns;however, the second sample (middle) exhibits a bimodal system, with a very fine network of porethroats with 0.1 micron diameters.
> SEM image showing a macropore (large blackarea at lower left) within peloidal grainstone(gray area). Micropores appear as speckledpatches in peloids. Inverted-v-shaped cementseparates the intergranular pore from microporesand produces a distinct NMR signature becausethe cement prevents diffusive coupling. The scalebar measures 50 microns.
17. Allen et al, reference 15: 7-8.
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Winter 2000/2001 31
Improved prediction of permeability optimizeswell placement and production, particularly fordirectional or extended-reach wells. The ability todistinguish pore types allows successful comple-tion of producible hydrocarbon zones. The methodalso helps engineers anticipate which layersmight be prone to early water breakthrough.
Integration of ECS and CMR logs with con-ventional log suites and core data resulted in themost rigorous interpretations of Middle Easternand Bombay High carbonate fabrics and diage-netic histories to date. More importantly, thedetailed joint studies provide an improved frame-work for ongoing interpretation problems in bothregions. The study groups recommend that newwells be evaluated similarly to the wells in thejoint studies. The optimal logging suite includesCMR and ECS logs in addition to routine resistiv-ity, gamma ray, density and neutron logs.
Confidence in log-based interpretation willcontinue to grow as more wells in these fieldsand other fields that produce from similar forma-tions are evaluated. Greater confidence in single-well interpretation is critical to ongoing reservoircharacterization and simulation because it is noteconomically viable to acquire core samples fromevery well. Integrated core-log studies providesignificant benchmarks for analysis of field wellslacking cores.
Both studies promoted close collaborationbetween research and operations personnel thatstrengthened working relationships, makingfuture joint research more likely. The improvedunderstanding of the reservoirs resulting fromthe research efforts can be applied to operationsimmediately. Based on research findings, toolsdeveloped for oil reservoirs can be tailored foruse in evaluating gas-filled rock.
Some results of carbonate case studies at SDRcan be carried over to clastic reservoir studiesbecause there are analogies between carbonaterocks and certain clastic reservoirs. For example,ongoing work in sandstones confirms the presenceof micropores associated with grain-coating claysand partially dissolved grains. Clearly, researchersand operations personnel may benefit from shar-ing nonconfidential results of their work.
Ongoing studies in the Middle Eastern reser-voir include seismic imaging with the single-sensor Q system to better characterize thereservoir and optimize drilling targets.
Benefits of the Bombay High study includegreater understanding of the L-III reservoir, par-ticularly heterogeneity and its effects on fluidtransport; development of a rigorous petrophysi-cal approach; and evaluation of the applicability
of the new methodology to older, less extensivedata sets. ONGC has recognized the importanceof ECS and CMR data for clay estimation. Theseresults will be incorporated into future produc-tion strategies.
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> Comparison of L-III log and core data for identification of high-permeability streaks. The lithology in the first track—clay, quartz, calcite and dolomite—is computed using ELAN analysis software withECS data as the key input. The fluids are reported as oil that has not been moved by invasion (green),oil that was moved (orange), irreducible water contained in micropores (blue with black dots) andmobile water (white). NMR data help distinguish irreducible and mobile water. The second track is the porosity broken down into clay-bound water from ECS data, microporosity from CMR data andmeso- and macropores from CMR and FMI logs. The third track contains the T2 distributions from theCMR log. The solid blue permeability curve in Track 4 is calculated from ELAN volumes. The light bluedots are permeability measured on core plugs. The black line is permeability measured with 1-cmsampling on the core slab face using a minipermeameter. The macroporosity computed from FMI data(shown in Track 6) is displayed in red in Track 5. Light blue dots indicate macroporosity determined by mercury injection in core samples. The black line represents vugular porosity measured on theface of the slabbed core.
Integrated Carbonate Evaluation at the ONGC-Schlumberger Joint Research CenterCarbonate reservoirs in India present importantinterpretation challenges to scientists and engi-neers working at the Joint Research Center(JRC), a combined effort by the Oil and NaturalGas Corporation, Ltd., (ONGC) and Schlumberger.The JRC, located in New Delhi, was establishedin the 1980s to investigate formation evaluation,reservoir description, production, completion andreservoir-monitoring problems experienced byONGC and to find solutions to those problems.Several noteworthy carbonate reservoirs arelocated offshore Mumbai, India, including theNeelam field, which JRC personnel have studiedsince its discovery and first production in 1990.
At the JRC, petrophysical, geophysical andgeological evaluations of carbonate reservoirsprovide the basis for an integrated reservoir solu-tion. The ultimate goal is to maximize oil recov-ery and production efficiency by understandingand modeling the reservoir. This approach alsominimizes the number of well interventions andthe number and location of wells required so thatall commercially viable oil pools are drained. Bybuilding a numerical simulation model of thefield, geoscientists and engineers can extrapo-late field behavior over time and evaluate “whatif” scenarios, such as how a given workover pro-gram might affect overall field performance and
production, or whether the failure to drill specificdevelopment wells might leave compartments ofundrained hydrocarbon.
In the case of a mature field like Neelam, thefirst phase in building a simulation model is tocalibrate it to reproduce the historical productionbehavior of the reservoir—history matching.Since this stage conditions the reservoir model tothe dynamic data, such as well-production ratesand changes in pressures and saturations, thehistory-matched model becomes a much morerepresentative description of the reservoir thanthe input static model.
To correctly model flow behavior in carbonatereservoirs, it is essential to understand the per-meability profile. Standard log data—density,neutron, sonic, gamma ray, SP and resistivitylogs—evaluated by conventional methods all toooften indicate a homogeneous reservoir. Porosityvariations are not a reliable indicator of perme-ability variations because changes in carbonatetexture affect permeability more than changes inporosity affect permeability. The time-honoredmethod of using core data to derive a porosity-permeability relationship associated with a par-ticular reservoir fails when reservoir-rock texturevaries. Although the technique is fundamentallycorrect, it should be carried out separately foreach carbonate depositional rock type or texture.In fact, previous studies of the Neelam field haveshown that permeability increased as porosity
decreased—a difficult conclusion for petrophysi-cists to reconcile with their interpretations.
Many carbonate reservoirs contain localizedor extended layers of mud-supported rock, wherepermeability is appreciably reduced, but com-plete barriers to vertical fluid migration are rare.During the millions of years of reservoir evolu-tion, fluids segregated, resulting in a water tableat the bottom, a transition zone where both oiland water volumes are movable, and an oil zoneat the top, where the water is entirely capillary-bound and only the oil is movable. Pressures alsoequalize in the reservoir over this period.
It is only by careful inspection of core data, orthrough innovative evaluation of NMR or bore-hole image logs, that the texture of the carbonatereservoir becomes apparent as distinct zoneswith varying degrees of carbonate-mud supportand fluid-transport properties. Grainstone, oftenthe least porous, generally yields the highest per-meability among carbonate rock types. As mudcontent increases, resulting in packstone orwackestone, total porosity usually increases, butpermeability is perhaps 10 to 100 times lowerthan in grainstone due to the increased impor-tance of microporosity in associated muds.
These texture differences do not necessarilycreate true fluid-flow barriers over geologic time.However, when reservoir fluids are subjected to“instantaneous” withdrawal from the forma-tion—for example production over perhaps 5 to20 years as opposed to the millions of years ittook the reservoir to form—the resulting pres-sure pulses create separate flow units within thereservoir separated by those zones of significantpermeability reduction. This usually results inlarge pressure differences between flow unitsand complete breakdown of the smooth water-to-oil transition with decreasing depth. Fingers ofedge water propagate laterally, at any depth, intothe most permeable sections.
To complicate matters further, the permeabilityof a carbonate reservoir often is severely affectedby tectonic and diagenetic phenomena. For exam-ple, extremely high-permeability layers, called“super-k” layers, commonly result from diageneticalteration. Most of the available data for theNeelam reservoir imply that super-k layers are cre-ated by dissolution and leaching of the rock fabricby meteoric water during periods of low sea level,when the carbonate was exposed to water fromthe atmosphere and at the Earth’s surface.
Having an accurate permeability descriptionsignificantly accelerates the history-matchingprocess and greatly improves the reliability ofpredictions from the history-matched model.
32 Oilfield Review
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> Texture and permeability analysis in an openhole log from the Neelam field. Track 1 displays ELANeffective reservoir porosity output, including immovable oil (green), movable oil (orange), movablewater (white) and capillary-bound water (light blue). Track 2 adds lithology analysis to the effectiveporosity output, scaled from 1 to 0. Gray represents shale; mud-supported limestone is gray-blue;grain-supported limestone is light blue; and other carbonate material is shown in deep blue. Track 3displays CMR T2 signal distributions, which correlate with carbonate textures (photomicrographs at left).
Winter 2000/2001 33
Because history matching is a complex, multi-variate process, it is sometimes possible toachieve what appears to be a satisfactory matchof historic data with an inaccurate model of thereservoir permeability distribution. In this case,the model will deliver inaccurate predictions.Only by unraveling the general reservoir perme-ability distribution can a realistic and usablereservoir-simulation model be built.18
Geoscientists and engineers at the JRCdecided to focus on mapping permeability using four complementary approaches. Whileeach approach begins at a wellbore, the resultsfrom each well must be integrated into a three-dimensional model of the field to deliver maxi-mum value to the operator. These approachesinclude the following:• NMR analysis to evaluate rock texture and
permeability profiles• cased-hole saturation logging to compare
original fluid saturations with saturationsafter some period of production to develop adepletion profile
• use of proportion curves and other geostatisti-cal tools to highlight hidden correlations thatcan be confirmed in key wells by either of thetwo previous methods
• geostatistical analysis of water breakthroughin historical well production data to evaluatehigh-permeability layers that conduct reser-voir or injection water.
The geostatistical techniques are still in experi-mental stages.
Texture and permeability analysis with open-hole logs—During field development or infilldrilling, operators have the opportunity to acquirenew openhole data. In the past, carbonate geol-ogists relied on image logs to reveal carbonate
textures, from which they infer permeability.More sophisticated techniques now are beingadded to image-log analysis to assess perme-ability. Confirming findings shown previously in alaboratory setting and from computer modelingby Ramakrishnan and others, JRC geoscientistsobserved that the width of the borehole NMR T2
signal distribution is strongly correlated to car-bonate lithology.19 Petrographic and core analy-ses corroborate JRC findings (previous page).20
This information can be used to calibrate theNMR permeability response to obtain an accu-rate, continuous permeability profile.
Previously, deriving permeability from NMRwas challenging because of the variable and ill-defined T2 cutoff between capillary-bound andfree fluids. The JRC-developed method first usesthe Schlumberger-Doll Research permeability for-mulation, usually referred to as kSDR. This rela-tionship, also used in the Middle East studydescribed earlier, defines permeability as a func-tion of porosity and the log-mean value of theNMR T2 distribution independent of a T2 cutoff.JRC scientists observed a well-defined depen-dence of the premultiplier in this relationship onrock texture, so they introduced a texture-relatedterm into the KSDR relationship. They confirmedthe accuracy of the method by comparing thetrend of NMR-derived permeability with brine-corrected core permeability data. The agreementbetween the texture and permeability estimatesfrom this technique and the results of an exten-sive core study is reasonable given the uncer-tainty in the permeability results caused bycarbonate heterogeneity.
Meaningful reservoir production forecastsrequire an accurate knowledge of the respec-tive volumes of free oil and free water, so JRC
engineers obtained free water by inverting theTimur-Coates permeability relationship and equat-ing it to the texture-related permeability measure-ment. This splits the total water—defined simplyas effective porosity minus hydrocarbon volume—into free- and capillary-bound waters.
Saturation in carbonate reservoirs cannot bederived from a simple Archie relationship. It iscommon to encounter oolithic molds or solutionvugs that affect the cementation factor m used inthe Archie relationship.21 For years, carbonateenthusiasts have known that a “variable m”approach is required. The difficulty resides inproperly partitioning the total porosity betweenprimary, matrix and vugular porosity.
A method first introduced by Brie and others in1985 makes use of an acoustic-scattering modeldeveloped previously by Kuster and Toksöz to eval-uate this partitioning.22 The technique uses a totalporosity from density, neutron, or both logs, andcompressional and shear velocities from soniclogs. An iteration technique adjusts the amount ofvugular porosity necessary to minimize the errorbetween expected theoretical sonic compres-sional and shear transit times and measured val-ues. Once partitioning of the porosity is evaluated,an equivalent approximation for electric propertiesprovided by the Maxwell-Garnett model is used toassess the effect of conductive or isolated inclu-sions on the cementation factor.23 A variable mvalue is obtained to use in ELAN Elemental LogAnalysis calculations to obtain a much more accu-rate volume of hydrocarbon. While other studieshave used variable values for m, this is perhapsthe first study in which the method has been vali-dated against individual core measurements of min the laboratory (above).
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> Cementation factor. Values of the cementation factor, m, derived from well-logdata using the Kuster-Toksöz model and core plugs measured in the laboratory,vary from 1.95 to 2.20. Laboratory measurement of m confirms that log-derivedvalues are reasonable and ultimately result in more accurate predictions ofhydrocarbon volume.
18. A full discussion of reservoir simulation is beyond thescope of this article, but will be covered in a futureOilfield Review article.
19. Ramakrishnan et al, reference 11.20. Olesen JR, Dutta D and Sundaram KM: “Carbonate
Reservoirs Evaluation with Advanced Well-Log Data,”presented at the 4th International Petroleum Conferenceand Exhibition, New Delhi, India, January 9-12, 2001; and also Extended abstract, presented at the AAPGInternational Conference and Exhibition, Bali, Indonesia,October 15-18, 2000.
21. Ooids are small, round grains of calcium carbonatelayers around a sandy nucleus. Oolithic molds are thespherical holes that remain when ooids dissolve.
22. Brie A, Johnson DL and Nurmi RD: “Effects of SphericalPores on Sonic and Resistivity Measurements,” Trans-actions of the SPWLA 26th Annual Logging Symposium,Dallas, Texas, USA, June 17-20, 1985, paper W.Kuster GT and Toksöz M: “Velocity and Attenuation ofSeismic Waves in Two-Phase Media: Part I, TheoreticalFormulations, Part II, Experimental Results,” Geophysics39, no. 5 (October 1974): 587-618.
23. Maxwell-Garnett JC: “Colours in Metal Glasses and inMetallic films,” Philosophical Transactions of the RoyalSociety of London 203 (1904): 385.Sen PN, Scala C and Cohen MH: “A Self-Similar Modelfor Sedimentary Rocks with Application to the DielectricConstant of Fused Glass Beads,” Geophysics 46, no. 5(May 1981): 781-795.
The petrophysical evaluation resulting fromthe combination of the improved capillary-bound,free-water and oil-volume derivation has beencompared with the results of an extensive, MDTModular Formation Dynamics Tester-derived pres-sure-profile analysis and well-test data (above).
Analysis of depletion profiles—In developedfields, operators commonly acquire new datathrough casing.24 In these cases, JRC members
have taken advantage of the RSTPro answer-product line to improve remaining oil-saturationestimates from the RST Reservoir Saturation Tooldevice to a level of accuracy that allows directcomparison with original openhole saturation.25
This permits derivation of a depletion profile thatclearly defines three kinds of reservoir zones:those with no apparent depletion, which areprobably low-permeability, mud-supported rocksthat separate flow units within the reservoir;
partially depleted zones that consist of “normal”reservoir rock; and zones of extreme depletion,which may be super-k layers or zones containingmassive solution channels.
A demonstration of these three zones comesfrom the Bassein reservoir in the Neelam field,where the zones were correlated over a distanceexceeding 6 km [3.7 miles]. In every well studied,a combination of the RST depletion profile and aborehole temperature survey acquired duringproduction highlights producing zones in perfo-rated intervals and reveals a reservoir separatedinto three major flow units (next page, left). Alarge amount of oil is still present in the upper-most unit, but virtually no production occurs fromthis unit because pressure depletion here is moresevere than in the lower units.
To improve cased-hole determination ofremaining oil volume in carbonates using the RSTtool, it is crucial to understand RST sensitivity tocompletion, especially cement, conditions. In sili-ciclastic rocks, cement thickness has a smalleffect on the length of the segments of the RSTsaturation-evaluation quadrilateral displayed incrossplots of carbon/oxygen ratios (C/O) fromnear and far detectors (NCOR versus FCOR). The quadrilateral and the crossplotted C/O ratiosare used to determine fluid saturations (nextpage, right).
Borehole geometry, formation lithology,porosity and the carbon density of the hydrocar-bon define the end points of the saturation-evaluation quadrilateral. The lower left corner,WW, is where both the borehole and the forma-tion are water-bearing. Travelling clockwise, theWO point indicates water-bearing borehole andoil-bearing formation. At the upper right is theOO point, oil in both borehole and formation.Finally, the OW point represents oil in the bore-hole and water in the formation. The exact posi-tion of these four points is obtained in controlledlaboratory conditions.26
In carbonates, the entire evaluation quadri-lateral is translated due the additional carbonand oxygen in the carbonate matrix. The amountof translation of the evaluation quadrilateral isrelated to the amount of carbonate rock sur-rounding the tool and also the distance betweenthe tool and the carbonate material.
Intuitively, the effect will be greatest in asmall borehole, with the tool and the rock matrixseparated only by the casing. As hole size andcement thickness increase, the tool is lessaffected by the carbonate rock. At the limit, for a
34 Oilfield Review
> Pressure profile at the top of the transition zone. Improved petrophysical evaluation techniquespredict capillary-bound, free-water and oil volumes more realistically and have been compared withthe results of an extensive, MDT-derived, pressure profile analysis and proven against a set of well-test data. In this example, CMR-derived fluid predictions (right) were confirmed by oil production during well tests.
> Pressure profile at the top of the water zone. Conventional log calculations using a constant valuefor m indicated an oil-bearing zone, but pressure-profile evaluation using both the MDT Modular Formation Dynamics Tester tool and well-test results proved that the zone is water-bearing, aspredicted by the JRC petrophysical evaluation methodology (right).
True
ver
tical
dep
th, m
Reservoir pressure, psi3500
XX00
XX50
X100
X1503550 3600 3650 3700 3750
Oil-water transition pressuregradient 1.294 psi/m, equivalent to0.910-g/cm3 fluid density
Formation water pressure gradient1.436 psi/m, equivalent to 1.01-g/cm3
fluid density, or 22 ppk salinity
Test 2: 1930 BWPD with1/2-in. choke; 23.4 ppk
Test 1: 1500 BWPD with1/2-in. choke; 23.4 ppk
True
ver
tical
dep
th, m
Reservoir pressure, psi
X170
X120
XX70
XX20
3200 3250 3300 3350 3400 3450 3500
Test 10: Most prolific producer,but water only, salinity 23.4 ppk.
Test 9: Before acidization,1/4-in. choke: 140 BOPD,24 BWPD, channelingsuspected as water ratechanges with choke size.After acidization producedwater only.
Test 7: Before acidization, 1/2-in. choke, flownot measurable. After acidization, 1/2-in. choke,1715 CMGPD, 514 BOPD, 37.5° API, 91 BWPD, 29.3 ppk.
Test 11: Produced water only,salinity 24.5 ppk.
Test 8: Before acidization, 1/2-in. choke1745 CMGPD, 858 BOPD, 38° API, WC<1%. After acidization, 1/2-in. choke,4084 CMGPD, 2593 BOPD, 38° API, WC <1%
Packstone
Boundstone
Wackestone
0.50
0.45
0.40
0.35
0.30
FCOR V ILL0.25
0.20
0.15
0.10
0.05
0.00
0.1
0.00.00 0.10 0.20 0.30 0.40 0.50
NCOR0.60 0.70 0.80 0.90 1.00
WO
WW
OO
OW
NLM2-2NLM2-4
NLM4-2
NLM5-9North
3
2
1
Winter 2000/2001 35
cement thickness larger than the radius of inves-tigation of the tool, the carbonate rock has noeffect on the measurement because the toolsamples only the cement.
In the past, if an openhole caliper was avail-able, it was incorporated in the RST data set toevaluate cement thickness from the differencebetween openhole radius and casing outer radius.Use of caliper data assumes that the borehole hasnot been enlarged from the time openhole logswere run to the time casing was cemented, thatthe casing was perfectly centered in the boreholeand that the borehole was perfectly round ratherthan oval-shaped. The latter assumptions arehighly unlikely, especially in deviated wells. WithRSTPro technology, and the acquisition of anadditional RST pass in sigma mode, it is possible
to compute an optimized cement-sheath thick-ness that will result, after diffusion correction, ina minimal discrepancy between formation cap-ture cross-section (sigma) measurements derivedfrom the far and near detectors.
This cement thickness and the outer casingdiameter can be used to generate an “RSTcaliper” for input to the RST oil-volume evalua-tion module. Previous RST logs of carbonaterocks offshore India exhibited remaining oil pro-files that were difficult to justify. The new tech-nique has produced credible logs since itsintroduction early in 2000. Changes in saturationprofile of as much as 20 saturation units com-monly occur between evaluations, with or withouttaking optimized cement thickness into account.
> Depletion-profile analysis. In these fourwells, a combination of the RST depletionprofile and a borehole temperature deriva-tive acquired during production shows thethree major flow units in the reservoir.The base of Zone 1 is the original oil-watertransition. Zone 2 includes the major pro-ducing horizons. Reserves remain in Zone 3,but reservoir simulation suggests that majorpressure depletion has occurred.
> Overlay of the RST saturation-evaluation quadrangle with near-detector C/O ratio (NCOR) to far-detector (FCOR) data from India.Color coding in the Z-axis represents shale volume (VILL); red isclean limestone, blue is 10 percent shale. The data were recordedin a 22-porosity unit limestone reservoir, in an 8.5-in. borehole with7.0-in. casing. The borehole geometry, formation lithology andporosity, along with the carbon density of the hydrocarbon, fullydefine the end points of the characterization locus. The data pointscluster along the WW-WO line, indicating a water-filled borehole.The formation oil saturation varies from 0 to 40%.
24. For more on production logging: Akhnoukh R, Leighton J,Bigno Y, Bouroumeau-Fuseau P, Quin E, Catala G,Silipigno L, Hemingway J, Horkowitz J, Hervé X,Whittaker C, Kusaka K, Markel D and Martin A: “KeepingProducing Wells Healthy,” Oilfield Review 11, no. 1(Spring 1999): 30-47.
25. Olesen JR and Carnegie A: “An Improved Technique forReservoir Evaluation Through Casing,” paper IRS2k-O228,presented at the Improved Recovery Symposium,Institute of Reservoir Studies, Ahmedabad, Gujarat,India, July 27-28, 2000.
26. More than 3000 combinations of hole sizes, lithologies,porosities, borehole and formation saturations havebeen measured. Interpolation between endpoints isobtained by nuclear modeling of the tool, formation andborehole conditions, with model endpoints calibratedwith laboratory data.
Geostatistical analysis—Innovative use ofstatistical tools at the JRC extends NMR analysisand RST results from key wells to the entire fieldmodel, which previously comprised only conven-tional log data and downhole production data.These new techniques involve proportion curvesand water-conduit tracking.
A vertical proportion curve plots a histogram atevery stratigraphic level within the formation(right). In this example, logs of porosity categoriesare displayed with depths relative to the top of thereservoir—the conforming surface. The verticalsampling interval is 1 meter [3 feet] in these wells.The RST survey discussed earlier was run in anearby well. The logs have been projected onto anorth-south line shown on the map. The verticalproportion curve is generated by showing the rel-ative percentages of the different porosity cate-gories at every stratigraphic level. The curvebecomes narrower with increasing depth becausethe wells have different depths of penetration.Nevertheless, it can be inferred that the formationprobably consists of two relatively high-porosityzones separated by a low-porosity zone.
This example demonstrates that the proportion-curve technique uses grouped or categorical datarather than continuous data. Traditionally, verti-cal proportion curves have used depth-dependentlithofacies data to understand depositionalcycles and to constrain geostatistical realiza-tions.27 But, as shown at the JRC, these curvescan have strong application in diagnosing reser-voir flow behavior and relating this behavior tothe reservoir characterization developed fromopenhole and cased-hole saturation logs.
To understand this technique more clearly, theexample can be taken a step further by including avertical proportion curve derived from productionlogs (right, lower right of figure). Productivity, orflow rate, is categorized as high, medium, low orno flow. At the top of the reservoir, flow rates arehigh, which implies that a thin layer of high per-meability exists at the top of the formation.Subsequent analysis showed that this is a super-klayer. Farther down, there are two other majorzones of high flow rate mixed with lower flowrates. Those zones should have medium to highpermeability. These observations support infer-ences made on the basis of RST data describedearlier, since the proportion curves capture theaverage behavior of the region.
Comparing the production log and porosity pro-portion curves shows that porosity alone is anincomplete descriptor of permeability in the regionand, consequently, that better openhole perme-ability-based descriptors are needed—such asthose derived from MDT or NMR data.
The proportion-curve technique has beenapplied elsewhere in the reservoir, and to otherforms of dynamic and static data, to derive severaluseful results rapidly and efficiently. For example,it is possible to map throughout the field the lat-eral and vertical extent of high-permeability zones
that have flowed water for an extended period bycombining an openhole gamma ray with a cased-hole gamma ray run later in the life of a well intoa proportion curve. Comparing openhole gammaray logs to density-neutron curve separationenables detection of weathered zones wheremeteoric water has created super-k layers throughdiagenetic alteration.
Proportion curves allow quick, efficient analy-sis of vast amounts of data, an important consid-eration when interpreting and synthesizing datafrom an entire field, which may include openhole,
36 Oilfield Review
> Creation and application of proportion curves. Porosity data from ten wells were sampled at 1-m [3-ft]vertical intervals and categorized to form porosity proportion logs (upper left). Well locations andthe north-south projection line are shown in the map (upper right). The porosity proportion logs arecombined to form a porosity proportion curve (lower left). The bottom of the curve narrows becausethe wells have different penetration depths. A similar curve can be generated using production logsfrom the wells (lower right). Zone 1 includes only high productivity values; Zones 2 and 3 have somehigh flow rates mixed with lower flow rates.
27. For more about proportion curves and depositionalcycles: Jain AK and Carnegie A: “Value AdditionThrough Stochastic Evaluation of Gamma Rays—A Geostatistical Approach to Geological Modeling andCharacterization of the Reservoir,” presented at theAAPG International Conference and Exhibition, Bali,Indonesia, October 15-18, 2000.For more about proportion curves and geologicalrealizations: Klauser-Baumgartner D and Carnegie A:
“Geostatistical Modeling of Delta Front Parasequencesby Indicator Kriging,” Information Processing andModeling in Geology, 10. Kontaktwochenende,Sedimentologie, Aachen, 1995.
28. For more on the water-conduit tracking method:Carnegie A: “Techniques to Optimize the Efficiency ofHistory Matching in Integrated Studies,” paper 402,presented at the Improved Oil Recovery Symposium,Institute of Reservoir Studies, Ahmedabad, Gujarat,India, July 27-28, 2000.
29. For more on the RRT method: Russell SD, Akbar M,Vissapragada B and Walkden G: “Small-ScaleHeterogeneity and Permeability Estimation fromDipmeter and Image Logs for Reservoir Rock Typing:Aptian Shuaiba Reservoir of Abu Dhabi,” Bulletin of the American Association of Petroleum Geologists(in press).
D1
D2
D3
Porosity < 8%
Porosity 8-16%
Porosity 16-24%
Porosity > 24%
D4
D9
D8
D7
D6
D5
C2
Porosity proportion logs Wells supplying the logs
N-S
pro
ject
ion
line
No flow
Low productivity
Medium productivity
High productivity
Zone 1
Zone 2
Zone 3
Winter 2000/2001 37
cased-hole and historical production data fromseveral hundred wells. Proportion curves can begrouped to gain local insight about specific partsof a field. They also offer a high level of immunityto incorrect or low-quality data, since the “noise”created by such data sets tends to cancel itself,and since the amount of high-quality data farexceeds the amount of questionable data withinthe entire data set. A proprietary PC-based soft-ware package, refined at the JRC, performs inter-active database management, computation, and2D and 3D visualizations of these proportioncurves. The package is compatible with theGeoFrame Application Builder program, whichfacilitates database access.
Water-conduit tracking method—Detection ofhigh-permeability conduits, such as faults orsuper-k layers that conduct reservoir or injectionwater, can be improved by performing a networkanalysis of water breakthrough times on historicalwell-production data. A PC-based software pack-age written at the JRC helps users detect thewater path interactively. The water-breakthroughinformation comes from historical well-produc-tion data loaded into a production-management
database. This tool allows quicker, more objectivediagnosis of the progress and evolution of waterbreakthrough over an entire field than traditionalmanual analysis.28 This method, known as water-conduit tracking (WCT), makes assessing thevalidity of multiple scenarios more efficient thanin the past.
By conducting the vital analyses of rock tex-tures, permeability, depletion profiles and pro-duction data, and judiciously integrating all theresults with other field data using geostatisticaltechniques, the JRC is creating models thatresult in more realistic reservoir simulations.These simulations provide more reliable guid-ance for development and production decisionsthan analyses performed in isolation.
Evaluating Carbonate Heterogeneity in Abu DhabiLocal operations teams augment the contributionsof formal research efforts to understand carbonaterocks. Scientists and engineers in Abu Dhabi, UAE,have developed new techniques to evaluate het-erogeneous carbonate reservoirs by integratinggeological, openhole and production-log data.
Characterization of the small-scale hetero-geneities within reservoir rocks has led to a clas-sification of 17 reservoir rock types (RRT) in theShuaiba formation. Reservoir rock types are basedon lithofacies, wireline log data, core porosity andpermeability, capillary pressure and pore-size dis-tributions derived from mercury-injection analy-ses, and production data.29 RRTs can be used tobetter correlate zones within reservoirs in theabsence of cores.
An oil field in Abu Dhabi has been producingfrom the Lower Cretaceous Shuaiba formationsince 1962. Within the field, the Shuaiba formationvaries from shallow-water shelf to deepwaterslope sediments, with four distinct reservoir facies.RRTs range from nonproductive rocks to those withup to 30% porosity and 20-Darcy permeability(below). These significant heterogeneities must beconsidered when planning well trajectories, wellcompletions and production strategies.
RRTs are defined on the basis of reservoirquality, distribution and productivity, but are prod-ucts of their depositional environment and dia-genetic history. RRTs observed in cores and logsfrom two wells in the field have been correlated
RRT 6 RRT 7
RRT 14 RRT 15
RRT 8
> Shuaiba heterogeneity. RRTs range from rudists—extinct mollusks similar to oysters—in lime mud (top left) tomixed rudists in a grainy matrix (top center) to diagenetically altered, debris-filled rudstone (top right). A pencilor fingertip in each photograph indicates the scale. RRTs from the northern part of the field comprise rudstone(bottom left photomicrograph) and fine-grained packstone or packstone (bottom right). The field of view of thephotomicrographs is 4 mm by 6 mm.
38 Oilfield Review
> Shuaiba RRTs and permeability indicator derived from core and log data. Photographs (far right) in thiscomposite plot from a well in a field in Abu Dhabi show the heterogeneity of three of the distinct RRTs. Permeability characterized by analysis of Stratigraphic High-Resolution Dipmeter Tool (SHDT) data (Track 8)shows close agreement with log and core data.
SHDT perm
indicatorCore RRTs
SHDT/log facies
Raw SHDT channels
GRDepth,
ft
XX950
X1000
X1050
X1100
X1150
X1200
X1250
X1300
Petrophysical interpretation
SHDT conductivity
Proportion of
heterogeneityCore perm
0.2 2000
Winter 2000/2001 39
with logs in uncored wells; this correlation allowsmore accurate permeability estimation in thosewells than with use of log data alone. The RRTstudy contributes significantly to the field devel-opment because the operator, Abu DhabiCompany for Onshore Oil Operations (ADCO), canuse realistic permeability estimates and upgraded3D geological models to optimize field drainage,thereby maintaining and prolonging production.
One innovative RRT characterization methodrelies on careful integration of conventional welllogs, such as gamma ray, neutron and density,
with high-resolution dipmeter and image logs.Heterogeneities in the form of conductivity varia-tions are quantified using specialized software,including BorTex and RockCell applications, toidentify RRTs and generate permeability indica-tors (previous page).30 In extremely heteroge-neous carbonates, permeability derived usingthis methodology resolves heterogeneity betterthan 1-in. core plugs or minipermeameter data(above). The higher resolution and increasedborehole coverage of imaging devices providemore accurate differentiation of RRTs than
dipmeter logs alone and facilitate identificationof flow paths between vugs and large pores.Because dipmeter and image logs are morewidely available than core, RRT analysis is apowerful tool for evaluating wells that lack core samples.
Another successful technique to evaluateporosity in the Shuaiba formation uses boreholeimages to map primary and secondary porosity.
> Integrated permeability data. Core plugs from 246 one-foot intervals (left) and 586 minipermeameter measurements at 2- to 3-in. intervals (center) from a wellin Abu Dhabi show significant scatter because of the extreme small-scale heterogeneity. On the other hand, the SHDT-derived permeability indicator (right)shows a clear trend that closely correlates with RRTs found in cores. Each color in the cored interval represents a distinct RRT of the Shuaiba formation.
30. A full discussion of BorTex and RockCell software isbeyond the scope of this article. For more information:http://www.geoquest.com/pub/prod/index.html.
XX900
XX950
X1000
X1050
X1100
X1150
X1200
X1250
X1300
XX900
XX950
X1000
X1050
X1100
X1150
X1200
X1250
X1300
XX900
XX950
X1000
X1050
X1100
X1150
X1200
X1250
X13000.1 1
Dept
h, ft
Core permeability, mD Minipermeameter core permeability, mD SHDT permeability indicator, mD10 100 1000 10,000 0.1 1 10 100 1000 10,000 1 10 100 1000 10,000
The wellbore azimuthal porosity spectrum revealsextreme porosity heterogeneity, which, in turn, isrelated to permeability (above).31
While RRT studies aid long-term reservoircharacterization and simulation studies, theresults also can impact field development in theshort term. For example, recognition of differentRRTs in a horizontal Shuaiba well allowed theoperator to optimize production rates.32 A hori-zontal oil well drilled in 1997 initially produced
6000 BOPD [953 m3/d] water-free for fourmonths, then abruptly lost pressure and was shutin. The operator needed to determine whetherthe decrease in reservoir pressure, migration offines or water loading stopped oil flow.Interpretation of high-quality PL Flagship produc-tion logging data, coupled with RRT analysis thatincorporated geological and openhole data, con-firmed that the horizontal section penetrated twovastly different RRTs (next page, left).
Engineers and geoscientists discovered that alow-permeability RRT along the central segmentof the wellbore affected flow behavior. By under-standing the sensitivity to choke settings, theoperator optimized well performance by select-ing a lower setting that gave an even drawdownalong the length of the well despite the lateralreservoir heterogeneity. This has resulted in thestable production of thousands of barrels per dayof dry oil. This reservoir-management strategy isbeing applied elsewhere in the reservoir to opti-mize placement of additional wells.
Future Research EffortsClearly, significant work remains for those explor-ing and exploiting carbonate reservoirs. Althoughthe complexity and heterogeneity of carbonaterocks present enormous interpretation and oper-ational challenges, the examples presented inthis article underscore the need for integration ofall available data and prudent selection of evalu-ation tools.
Schlumberger is addressing carbonate issuesmore aggressively by establishing a CarbonateResearch Center (CRC) at King Fahd University ofPetroleum and Minerals (KFUPM) in Dhahran,Saudi Arabia (next page, right). The proximity ofthe center to the prolific carbonate reservoirs ofthe Middle East, key operators and selectregional universities will facilitate intraregionalcollaboration. Innovative information-technologysolutions for virtual teamwork will accelerateresearch progress and dissemination of suc-cesses worldwide (see “From Reservoir Specificsto Stimulation Solutions,” page 42 ).
Key areas of activity for the CRC include landseismic-data acquisition, NMR interpretation,water management and well stimulation in car-bonate reservoirs. Research efforts will comple-ment rather than duplicate work performed inother research facilities. For example, Middle Eastcarbonate case studies will be performed in theCRC rather than SDR as appropriate. Because ofthe proximity to classic carbonate field locations,
40 Oilfield Review
> Shuaiba porosity evaluation. A new technique uses FMI images (Track 1 of upper log) to map primaryand secondary porosity by generating histograms of porosity at each depth (Tracks 2 and 3). In thelower figure, the lime mudstone at 3 feet is relatively homogeneous and microporous, as shown inthe photograph and photomicrograph to the right of the lower log. The floatstone, a type of matrix-supported limestone with some large grains, which is located above 5 feet, has extreme porosity heterogeneity, as shown in photograph and photomicrograph to the right.
31. Akbar M, Chakravorty S, Russell SD, Al Deeb MA,Saleh Efnik MR, Thower R, Karakhanian H, Mohamed SSand Bushara MN: “Unconventional Approach to ResolvingPrimary and Secondary Porosity in Gulf Carbonates fromConventional Logs and Borehole Images,” paper 0929,presented at the 9th Abu Dhabi International PetroleumExhibition and Conference, Abu Dhabi, UAE, October15-18, 2000.
32. Russell SD, Al-Masry Y, Biobien C and Lenn C:“Optimizing Hydrocarbon Drainage in a Heterogeneous,High-Permeability Carbonate Reservoir,” paper SPE59427, presented at the SPE Asia Pacific Conference onIntegrated Modelling for Asset Management, Yokohama,Japan, April 25-26, 2000.
3 ft
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.90.0
0.10 0100Porosity, % Porosity, % 100
0.90.00.10.20.30.40.50.60.7
Depth,ft
FMI dynamic image FMI porosity histogramp.u. 050
FMI/log porosity
Secondary porosity
p.u. 040
Log porosity
FMIporosity
2
3
4
5
6
7
8
Winter 2000/2001 41
an endeavor specific to the Dhahran facility will befield testing new tools and modification of existingtools to achieve the best possible results in car-bonate rocks.
Much of the emphasis so far has been on oilreservoirs, but long-term strategic emphasis ongas production is pushing even the largest oilproducers in the Middle East to pursue develop-ment of carbonate gas reservoirs. Deeper gasplays present significant interpretation, explo-ration and production challenges.
Although this article has focused on data atthe scale of a wellbore, there are larger scales atwhich Schlumberger and operators are evaluat-ing carbonate reservoirs. Interpretations fromlogs and cores are being incorporated in field-scale reservoir simulations. The simulationsallow reservoir models to be extended into thefourth dimension, time, to better predict fieldresponse and optimize performance.
New seismic acquisition methods, such as the Q single-sensor technology that has been used for data acquisition in the MiddleEast, will address imaging challenges at an even larger scale. Preliminary results from these tests suggest that significant improve-ment in data quality will advance our under-standing of carbonate reservoirs and, whenproperly integrated with other data, lead togreater success in carbonate exploration, devel-opment and production. —GMG
> Evaluating RRTs to optimize production. RRT analysis confirmed thatthe horizontal section of the well penetrated two RRTs (top). Wirelinelogs (bottom) confirm the variations in gamma ray, porosity and resistivityof the two RRTs. The invasion inferred from separation between deepand shallow resistivity curves in RRT 14 indicates higher permeabilitythan in RRT 15.
> The new carbonate research environment.The Schlumberger Carbonate Research Centeris located on the campus of King Fahd Universityfor Petroleum and Minerals (KFUPM) inDhahran, Saudi Arabia.
GR14
15
14
Deep
Shallow
PHIE
0.2 2000ohm-mShallow resistivity
Departure, ft
Dept
h, ft
sub
sea
8000 6000 4000 2000 0
0.2 2000ohm-mDeep resistivity
0 50APIGR
0 0.4ft3/ft3
Effective porosity MD:5000ft
XX000
XX500
XX000
X1500
45-fttotal
RRT 14
Well C Well A
16-fttotal
X700
X720
X740
X760
X780
X800
X820
X840
X860RRT 15
42 Oilfield Review
From Reservoir Specificsto Stimulation Solutions
Ali O. Al-Qarni Saudi Aramco Udhailiyah, Saudi Arabia
Brian AultRoger Heckman Sam McClure Ultra Petroleum, Inc. Englewood, Colorado, USA
Stan Denoo Wayne RoweEnglewood, Colorado
David Fairhurst San Antonio, Texas, USA
Bruce KaiserHouston, Texas
Dale Logan Midland, Texas
Alan C. McNally Louis Dreyfus Natural Gas Inc. Midland, Texas
Mark A. Norville Milton R. Seim Kerns Oil and Gas Inc. San Antonio, Texas
Lee RamseyAl Khobar, Saudi Arabia
For help in preparation of this article, thanks to UsmanAhmed, Kamel Bennaceur, Leo Burdylo and Mo Cordes,Sugar Land, Texas, USA; Tom Bratton, Mike Donovan and Steve Neumann, Houston, Texas; Paul DeBonis,Englewood, Colorado, USA; and Joe Lima, Farmington, New Mexico, USA.AIT (Array Induction Imager Tool), CMR (CombinableMagnetic Resonance), CMR-Plus, CNL (CompensatedNeutron Log), DataFRAC, DESIGN-EXECUTE-EVALUATE, DSI (Dipole Shear Sonic Imager), ECS (Elemental CaptureSpectroscopy), ELAN (Elemental Log Analysis), FMI(Fullbore Formation MicroImager), FracCADE, Litho-Density,NODAL, Platform Express, PowerJet, PowerSTIM, QLA, RFT (Repeat Formation Tester) and UltraJet are marks of Schlumberger.
Comprehensive data acquisition, interpretation and modeling provide a thorough
understanding of basins and fields, a prerequisite for successful well completions.
With more complete information, teams of experts develop, refine and apply
improved models to design perforation, completion and development strategies
that enhance productivity.
Between two and three billion dollars US arespent annually to fracture more than 20,000wells worldwide.1 However, less than 1% of frac-turing treatments are optimally designed to max-imize production and recovery. In spite ofincreasing demand for stimulation services, theGas Technology Institute, formerly Gas ResearchInstitute, Chicago, Illinois, USA, reports that two-thirds of hydraulically fractured wells in theUnited States do not respond as expected andfail to meet operator objectives.2 The same istrue in other parts of the world. One reason citedfor this poor performance is lack of an optimiza-tion process. Operators, therefore, are continuallystriving to improve stimulation practices.
In the early 1980s, it seemed as though everywell needed a hydraulic fracturing treatment;reputations and résumés were built based onpounds of proppant pumped—many “records”were set. Later, the industry found that, as withmost things, there was a point of diminishingreturns, and optimization became a key word.
During the past two decades, there has beensome optimization of well stimulations, but notnearly enough. Even today, the tendency is to relyon fracturing treatments that have always beenperformed the same way in a particular area.This means that stimulation design utilizing com-prehensive data and detailed designs are not yetcommon practice.
In addition to enhancing oil production frommarginal reservoirs, well stimulation is becomingincreasingly important because of growing inter-est in natural gas, which often is found in lowerpermeability zones. Formations with low to mod-erate permeability may require hydraulic fracturingto produce at economic rates. Even for reservoirswith higher permeabilities, stimulation is an effec-tive way to improve production or acceleraterecovery, especially during periods of rising oil andgas prices or when project economics require a
quick return on investment. Stimulation technol-ogy is also applied as a preventive measure toavoid or delay productivity-related problems likesand production, movement of formation fines,scale deposition and organic deposits.3
These applications are especially importantoffshore where remedial well-intervention costsover the productive life of a well or reservoir canbe extremely high, often prohibitive. In manycases, stimulation is one of the main costs ofcompleting a well. Advances in three-dimensional(3D) simulation make reservoir characterizationand stimulation design more efficient, but acquir-ing input for these models continues to challengegeologists, petrophysicists and engineers whodesign drilling, completion or stimulation pro-grams. A vast amount of data and many variablesmust be considered, some of which are critical forpredicting potential production rates, reservesand recovery factors that are used to determinewell-completion or stimulation strategies.
The accuracy of petrophysical models and afew key reservoir characteristics, such as perme-ability, porosity, fluid saturation, magnitude anddirection of tectonic stresses, and other rockmechanical properties, dramatically impact fielddevelopment decisions. Many of these parame-ters, even those with significant influence on com-pletion and stimulation designs, are all too oftenbased on standard correlations, averages, esti-mates or even assumptions. Rather than rely onlimited data sets, rock catalogs, prior experienceand local practices, which may lead to inaccura-cies, miscalculations and completion inefficien-cies, optimized stimulation designs require themost complete and reliable data possible.
Advanced formation evaluation tools allow in-situ analysis and provide high-resolution, con-tinuous data acquisition across zones of interestto quantify important reservoir parameters and
1. During hydraulic fracturing treatments, fluid is injected at pressures above formation breakdown stresses to create a crack, extending in opposite directions from a well. These fracture wings—half-length—propagate in a preferred fracture plane (PFP) perpendicular to theleast rock stress. Held open by a proppant, theseconductive pathways increase effective well radius,allowing linear flow into the fracture and to the wellbore.Naturally occurring or resin-coated sand and high-strength bauxite or ceramic synthetics, sized by screeningaccording to standard U.S. mesh sieves, are used as proppants to maintain fracture conductivity.
2. Hydraulic Fracturing Survey of Industry Practices.Chicago, Illinois, USA: Gas Research Institute, 1995.
3. Economides MJ and Nolte KG: Reservoir Stimulation, 3rd ed. West Sussex, England: John Wiley & Sons Ltd, 2000.
Winter 2000/2001 43
improve predictive modeling. These direct mea-surements and knowledge from other sources,such as core, pressure and production data, andin-situ tests like DataFRAC minifracture treat-ments, provide correlations to complement andverify empirically derived values. Improved inter-pretation techniques and innovative formats forprocessing data are paving the way for bettermodels and simulations by supplying values thatwere previously unknown or difficult to determineaccurately, especially for low-permeability andheterogeneous reservoirs. In some cases, this typeof detailed data even helps identify productivezones that might otherwise be overlooked.
To select and apply the best stimulation tech-nologies and completion solutions, both operat-ing and service companies must use a full rangeof skills and expertise. Collaboration is essentialbecause operators bring reservoir knowledge andfield experience, and integrated service providerssupply the latest fit-for-purpose technology andexpertise derived from work in a variety of fieldsand basins. In addition to improved stimulationresults and enhanced well performance, pro-ducers desire cost-effective services delivered in a timely fashion. For this reason, established
data-processing services and a knowledge-management infrastructure are indispensable forreal-time data exchange between widely dis-persed office and field locations.
Comprehensive data acquisition coupled withWeb-based information technology improvesreservoir characterization to aid in stimulationoptimization. With proper knowledge manage-ment, experiences and lessons from previouswell completions are readily accessible. Infor-mation is distributed efficiently so multidisci-plinary teams are able to work together even atgreat distances, which means quicker turnaroundtimes for reservoir characterization. As a result,advances in formation evaluation and fracturingtechnology developed over the past 20 years canbe applied faster and more effectively than everbefore, often at reduced cost.
Stimulation optimization takes many forms,from slight modification of fracturing designs toapplication of new techniques or complete over-haul of field-development schemes. In Egypt forexample, development costs were reduced 42% inone field by changing from a 23-well infill-drillingprogram to 13 hydraulically fractured wells.
The potential exists to greatly improve comple-tion designs, optimize stimulation treatments andenhance production. Hydraulic fracturing of more
permeable formations, a proven technique inVenezuela and Prudhoe Bay, Alaska, USA, remainsuntried in other parts of the world. Refracturing tooptimize recovery is another stimulation applica-tion that is the subject of ongoing research.
This article focuses on stimulation optimiza-tion using the PowerSTIM stimulation and com-pletion process to develop field- or basin-specificmodels and apply customized well-completionsolutions. A proven engineering methodologyand unique Web-base workflow are the essenceof this initiative. Case histories illustrate howthis approach capitalizes on stimulation opportu-nities and improves financial results by fullyutilizing data captured while drilling, evaluating,completing and producing wells.
Finding the Right Solutions Optimized stimulations require a step change in delivery of formation evaluation, reservoircharacterization, and stimulation and completionservices. The PowerSTIM initiative provides aworkflow and tools for reengineering well com-pletions and stimulation treatments. It combinesopenhole and cased-hole reservoir characteriza-tion, drilling and measurements, completion andstimulation technologies to provide a fresh look atreservoirs. This methodology focuses on well pro-duction and field development, integrating petro-physical expertise and reservoir knowledge withdesign, execution and evaluation (above).
The PowerSTIM approach focuses on buildingfield- or basin-specific predictive models todeliver timely, customized completion recom-mendations. This approach helps teams ofexperts gather, process and evaluate as muchinformation as possible about a reservoir to opti-mize stimulation and completion designs.Experience and lessons learned are evaluatedand incorporated to close the optimization loop.
A thorough process internal to Schlumbergerand unique Web-based intranet tool combineanswers provided by wireline log data, well tests,core analysis and in-situ testing with stimulation
designs to maximize their benefits. PowerSTIMmethods generate more value than applying ser-vices and processing results separately. Takentogether, improved assessment of permeability,porosity, water saturation, mechanical rock prop-erties, stress profiles and net pay form the basisof specific solutions for a particular reservoir orfield development.
The PowerSTIM workflow can be divided intotwo stages. The first stage concentrates on a fewwells—three to five—in a field (next page, top).Through improved data acquisition, detailed anal-ysis and by working closely with the operatingcompany, operator and Schlumberger expertsdevelop a reservoir model that accurately predictskey parameters and forecasts production. Once aninitial model is established, emphasis shifts toidentifying technologies for improving well perfor-mance. Geologists, petrophysicists and reservoiror production engineers use this local model tomake completion and stimulation recommenda-tions for various stages in a field’s productive life.
In the second stage, geoscientists and engi-neers refine the reservoir model and completiondesigns to quickly deliver integrated stimulationsolutions for future wells (next page, bottom). Inmany cases, project teams deliver updatedmodels within hours of logging operations. This“in-time” approach makes the PowerSTIM
methodology an integral part of completion plan-ning rather than an afterthought. Knowledgegained from acquiring, interpreting and format-ting comprehensive data sets using the latestlogs, cores and in-situ tests, and state-of-the-artstimulation technology is key to the success ofthese projects.
One such technology is nuclear magnetic res-onance (NMR).4 Logging platforms such as theCMR Combinable Magnetic Resonance toolexcite hydrogen atoms in formations by settingup a magnetic moment, relaxing it and measuringthe time it takes atoms to realign. This NMRrelaxation time, T2, is dependent on pore size andporosity, which is related to permeability. The T2
distribution is used as an indication of porosityand permeability. Smaller pores have shorterrelaxation times; larger pores have longer relax-ation times. Laboratory analysis of core sampleshas identified a useful relaxation time for freeversus bound fluids. For typical sand-shalesequences, this cutoff is 33 msec. Pores withrelaxation time beyond this cutoff contain pro-ducible fluids.
Sonic tools like the DSI Dipole Shear SonicImager sonde excite formations with acousticwaves and measure resulting compressional and
44 Oilfield Review
> Stimulation optimization. The PowerSTIM methodology begins with a basic data set, incorporates geologic and reservoir considerations, and developsoptimized stimulation designs by acquiring and processing comprehensive data sets. Extensive post-stimulation evaluation provides feedback for continualimprovement of well completions, stimulation and field development. By comparison, standard fracturing services include only treatment design usingbasic, often limited, data and on-site execution with little post-job evaluation. Additional data acquisition, processing and interpretation are not included.
• Understand geological models• Evaluate input to geological models• Define structural and stratigraphic plays• Interpret depositional environment
• Design and analyze pressure buildup test• Analyze any flowing pressure and rate data• Make performance forecasts
• Recommend perforation interval• Design DataFRAC minifracture service• Optimize casing and tubulars• Perform reservoir simulation• Design artificial-lift or velocity string• Use appropriate new technology
• Pilot-test fracture fluids• Use local interpretation models and reservoir characterization expertise• Optimize fracture treatment
• Perform quality assurance and quality control• Ensure design criteria are met• Supervise implementation of pumping schedule• Analyze diagnostic tests
• Analyze log and core data• Analyze offset wells• Develop local interpretation models• Apply fit-for-purpose technology• Develop 3D fracture data set• Finalize prefracture reservoir characterization
• Analyze post-treatment data• Perform post-treatment production data analysis• Design and analyze post-treatment pressure transient test
Geologicconsiderations
Reservoirconsiderations
Welltesting
Well-completiondesign
PowerSTIM optimized stimulation solutions
Stimulationdesign
On-siteexecution
Post-jobevaluation
Standard fracturing services
DESIGN-EXECUTE-EVALUATE services
Winter 2000/2001 45
shear transit times.5 Transit times are convertedinto rock properties such as shear modulus,Young’s modulus and Poisson’s ratio.6 Theseinferred parameters could be further improved bycorrelation with direct measurements from coreand in-situ formation tests. Radial, or azimuthal,sonic data also can be measured to derive pre-ferred fracture plane (PFP) direction. These dataare useful for ensuring reservoir drainagethrough proper well placement. The FMI FullboreFormation MicroImager microresistivity tool isused to identify faults, natural fractures, sec-ondary porosity and borehole breakout. If presentand evident on FMI logs, borehole breakoutsoccur perpendicular to the principal stress direc-tion and help confirm DSI data.
These advanced sonic and NMR logging tech-nologies combined with core analysis, or in-situformation tests and well production testing pro-vide more accurate reservoir characterization forbetter fracture simulation and design. Designprograms like the 3D FracCADE model predicthydraulically induced fracture geometry (height,length and width) using formation parameterssuch as shear modulus, Young’s modulus,Poisson’s ratio, permeability, overburden stressand pressure. Several new tools have beendeveloped to help link formation evaluation, andpetrophysical and production analysis to stimula-tion and completion design.
For example, the ZoneAID program is a uniquezone-by-zone layering routine to identify and eval-uate individual zones in a layered formation. Thisanalysis tool is a critical link between formationevaluation data and the FracCADE program. TheFracVIZ program is a visualization tool to betterunderstand fracture geometry, fracture orientationand fracture barriers as well as their relationshipto reservoir size. Production-data analysis usingthe Production Data Fracture Interpretation Tool(PROFIT) program determines fracture half-lengthand conductivity, and effective permeability ofstimulated formations without shutting wells infor analysis.
The PSPLITR program uses production logdata to correctly allocate production to eachcompleted interval and ensure quantitative anal-ysis to reliably estimate production and evaluatefracture characteristics in commingled, multi-stage reservoirs. Well productivity is evaluatedby NODAL analysis, a technique that considersperforations, tubulars and surface facilities bytreating each pressure interface as a node withseveral variables.7 These tools and techniquescome together within the PowerSTIM environ-ment to generate innovative solutions.
> Reservoir characterization, the first stage of the PowerSTIM process.
> Optimizing stimulation and completion solutions, the second stage of the PowerSTIM process.
4. Allen D, Crary S, Freedman B, Andreani M, Klopf W,Badry R, Flaum C, Kenyon B, Kleinberg R, Gossenberg P,Horkowitz J, Logan D, Singer J and White J: “How toUse Borehole Nuclear Magnetic Resonance,” OilfieldReview 9, no. 2 (Summer 1997): 34-57.Allen D, Flaum C, Ramakrishnan TS, Bedford J, Castelijns K,Fairhurst D, Gubelin G, Heaton N, Cao Minh C, Norville MA,Seim MR, Pritchard T and Ramamoorthy R: “Trends inNMR Logging,” Oilfield Review 12, no. 3 (Autumn 2000):2-19.
5. Brie A, Endo T, Hoyle D, Codazzi D, Esmersoy C, Hsu K,Denoo S, Mueller MC, Plona T, Shenoy R and Sinha B:“New Directions in Sonic Logging,” Oilfield Review 10,no. 1 (Spring 1998): 40-55.
6. Shear modulus is an elastic material constant that is theratio of shear stress to shear strain. Young’s modulus isan elastic material constant that is the ratio of longitudinalstress to longitudinal strain. Poisson’s ratio is an elasticconstant that is the ratio of latitudinal to longitudinalstrain, or a measure of material compressibility perpen-dicular to applied stress, that can be expressed in termsof measured properties, including compressional- andshear-wave velocities.
7. Bartz S, Mach JM, Saeedi J, Haskell J, Manrique J,Mukherjee H, Olsen T, Opsal S, Proano E, Semmelbeck M,Spalding G and Spath J: “Let’s Get the Most Out of ExistingWells,” Oilfield Review 9, no. 4 (Winter 1997): 2-21.
• Existing data and local knowledge• Fracturing database• Petrophysical model library• Production database• Offset-well data• Client database• Basic data set
• Acquire new optimized data• Apply previous models• Calibrate data to core values• Revise current models• Optimize completion design• Apply innovative technology• Predict production performance • Perform post-job evaluation• Match production history• Refine models
• Acquire new optimized data• Apply refined models• Optimize completion design• Predict production• Verify models• Perform post-job evaluation• Refine models• Apply optimized stimulation and completion solutions
• Acquire comprehensive data set• Calibrate data to core values• Develop new models• Optimize completion design• Evaluate technical solutions• Predict production performance • Perform post-job evaluation• Match production history • Refine models
Well A Well B Well CPowerSTIM
stage 1
Field- or reservoir-specific model applied from stage 1
• Openhole logs• Petrophysical evaluation using interpretation models
• Geological, stratigraphic and structural evaluation
• Analyze well- test data
• Completion design• Completion optimization• Cementing evaluation
• Perforating design• Fracture stimulation design
• Predicted fracture properties• Production prediction
• Technical recommendation
• DataFRAC minifracture diagnostics
• Revise fracture stimulation design
• Evaluate fracture fluid flowback data
• Actual treatment parameters• Post-fracture pressure analysis • Match revised fracture properties• Analyze post-treatment production data• Complete evaluation by PowerSTIM team
• Recommendations for future development• Action plan• Update database• Full-cycle engineering and evaluation on every well• Revise models and database• Apply optimized solutions to next well
Completion Design
Summary and recommendationsExecution EvaluationReservoir
characterizationPowerSTIM stage 2
Step 1 Step 2 Step 3
Predicting Permeability Permeability impacts completion decisions anddictates optimal fracture designs. High-perme-ability formations may not need stimulation forenhanced productivity while low-permeability—tight—zones may require massive hydraulic frac-turing treatments (right). It is also important,however, to remember that stimulation of high-permeability formations is still a viable optionwhen sand production and formation fines move-ments are concerns.
Traditional methods of measuring or calculat-ing permeability do not always yield representa-tive values and may be costly, time-consuming orrisky. Core samples provide valuable informationto fill in gaps, but sample a statistically smallportion of the zone of interest. Pressure-builduptests and production-history matching supplyaverage permeability for perforated zones, but noinformation about adjacent formations or shales.In some cases, pay intervals may even have to bestimulated first just to flow and test.
To improve Lobo formation completions inSouth Texas, USA, Conoco looked to othermethods of acquiring reliable permeabilitydata.8 Accurate permeability measurementsfrom individual layers in sections with multiplepay zones are critical for predicting fracturegeometry, selecting treatment systems (prop-pants and fluids) and determining job executionparameters (pumping rates and pressures).Previously, the Lobo asset group obtained per-meability values from sidewall core and pres-sure-buildup data, relying on standard log dataand permeability correlations.
Several companies offer permeability loggingservices, so one option was to develop a methodfor estimating permeability from continuouswireline measurements. To assess this option,CMR logs were run in wells where permeabilitydata were also acquired by rotary sidewall cores.This project provided essential elements for anintegrated team of geoscientists and engineersto address stimulation optimization for a reser-voir with variable permeability by applying thePowerSTIM process.
Permeability, stress profile and net pay arekey reservoir parameters. The objectives of this evaluation were to calibrate log- and core-permeability data so a reliable local model couldbe built to predict permeability, quantify reservoirstress profiles and identify net pay, especiallypotential low-productivity zones. The modelneeded to be valid across the Lobo trend andavailable for real-time delivery. The final loggingprogram had to be more cost-effective than othermethods of acquiring permeability data.
The initial evaluation consisted of threewells. The logging program for the first wellincluded AIT Array Induction Imager Tool mea-surements, Litho-Density log, CNL CompensatedNeutron Log data and a gamma ray log for corre-lation. Additional logs were recommended toprovide permeability measurements, identify netpay and construct stress profiles for input to theFracCADE design program.
The CMR tool was used to determine pore-sizedistribution and relationship to permeability. TheECS Elemental Capture Spectroscopy tool providedclay typing and additional petrophysical analysis.Rotary cores taken with a Mechanical SidewallCore Tool (MSCT) system provided control for cali-brating CMR measurements. To be more represen-tative of in-situ conditions, core permeabilitieswere corrected for net overburden pressure.
The PowerSTIM team relied on ELANElemental Log Analysis output and DSI data toobtain mechanical rock properties and stressprofiles. To ensure good wellbore-to-formationcommunication, wells were perforated withdeep-penetrating PowerJet charges.9 Bottom-hole pressures were obtained while perforating.The best available methods were evaluated tomatch CMR permeability with core data.
Initially, permeability was computed using astandard Timur-Coates permeability equation with33-msec cutoff for T2 in sandstone, and equation
exponents based on experience in South Texas. Ingas zones, however, CMR porosity can be pes-simistic. Both CMR permeability predictions andconventional permeability-porosity relationshipsshowed mixed results when compared with corevalues (next page, top). The Lobo project team rec-ommended more core points to provide a betterpermeability baseline for correlation, but boreholeconditions prevented adequate data from beingobtained in the second well.
Additional rotary cores obtained in a thirdwell allowed testing and refinement of severalpermeability models. The initial Timur-Coatesequation did not work adequately in this welleither. Correlation between CMR-derived perme-abilities and core values was better in the lowerzone than in the top and middle zones (next page,bottom). After correction for net overburden, cor-relations using a Timur-Coates equation modifiedfor low-permeability reservoirs were encourag-ing, but still not acceptable.
46 Oilfield Review
30
Marginaleconomics
Fracturelength
Fractureconductivity
Naturalcompletion
25
20
15
10
5
00.0001
Core
por
osity
, %
Core permeability, mD
0.001 0.01 0.1 1.0 10.0 100.0 1000.0
Core point
> The impact of permeability on completion and stimulation decisions. Formations havedifferent optimal stimulation requirements. At the lowest permeabilities, economics aremarginal. For slightly higher permeabilities, fracture length becomes the crucial designparameter. At still higher permeabilities, fracture conductivity is the dominant characteristic.The highest permeability formations may not need a stimulation treatment at all.
8. Kerchner S, Kaiser B, Donovan M and Villereal R:“Development of a Continuous Permeability MeasurementUtilizing Wireline Logging Methods and the ResultingImpact on Completion Design and Post CompletionAnalysis,” paper SPE 63259, presented at the SPE AnnualTechnical Conference and Exhibition, Dallas, Texas, USA,October 1-4, 2000.
9. Behrmann L, Brooks JE, Farrant S, Fayard A,Venkitaraman A, Brown A, Michel C, Noordermeer A,Smith P and Underdown D: “Perforating Practices ThatOptimize Productivity,” Oilfield Review 12, no. 1 (Spring2000): 52-74.
Winter 2000/2001 47
Porosity
Neutron porosityvol/vol0.3 0.0
Density porosity
vol/vol0.3 0.0Total CMR porosity
Crossover CMR free fluid
vol/vol0.3 0.0
Free fluid CMR permeability, mD
Density porosity
vol/vol0.3 0.0CMR bound fluid
vol/vol0.3 0.0Pseudopermeability
equation-porosity relations0.002 20.0
StandardTimur-Coates equation0.002 20.0
Sidewall-cores corrected fornet overburden
. Initial log permeability versus Lobo formationsidewall core values. In track Three, permeabili-ties calculated from CMR Combinable MagneticResonance log data using a standard Timur-Coates equation with typical experience-basedSouth Texas exponents and a 33-msec cutoff forT2 (dashed purple curve) did not agree with side-wall core permeabilities (purple dots) in the firstwell of the Lobo reservoir characterization study.The correlation was acceptable in some zones,but not in others.
Porosity
Neutron porosity
vol/vol0.3 0.0Density porosity
vol/vol0.3 0.0Total CMR porosity
Crossover CMR free fluid
vol/vol0.3 0.0
Free fluid CMR permeability, mD
Density porosityvol/vol0.3 0.0
CMR bound fluidvol/vol0.3 0.0
Sidewall-cores corrected fornet overburden
Net-overburden correctedTimur-Coates equation0.002 20.0
StandardTimur-Coates equation0.002 20.0
Low-permeabilityTimur-Coates equation0.002 20.0
, Refining log permeability versus Lobo formationsidewall core values. More rotary sidewallcores were obtained in the third well of thisstudy to test and refine the permeability model(purple dots). The standard Timur-Coates equationdid not work in this well either (dashed purplecurve). Correcting CMR permeabilities for netoverburden improved the CMR permeabilityprediction in the lower zone, but not in the othertwo zones (green curve). A version of the Timur-Coates equation for low-permeability reservoirsmatched core permeability values in the topand middle lobes, but underestimated the lowestzone (black curve).
>
>
These new data were used to tailor the Timur-Coates equation specifically for the Lobo forma-tion. A modified CMR-permeability equation was developed using a Timur-Coates equationwith exponents provided by Schlumberger-DollResearch, Ridgefield, Connecticut, USA. Effectiveporosity from the ELAN output was used insteadof gas-corrected CMR density porosity because itwas corrected for gas, shale and invasion. Usinga 90-msec cutoff for T2 to take advantage of oil-base mud invasion characteristics in the Loboformation refined the ratio of free-fluid volume(FFV) to bound-fluid volume (BFV). Permeabilitiescalculated using this revised equation showacceptable agreement with core values through-out the well (above).
Conoco was concerned that, depending onlithology, the CMR tool would require ongoingcalibration to provide accurate reservoir per-meabilities. Laboratory measurements of T2,permeability and porosity were made on coresfrom several Lobo fields to determine if this wasthe case. To provide further validation, CMR data
from the first well were reprocessed using themodified permeability expression (below).Positive results gave Conoco confidence toremove sidewall cores from the logging programin the study area.
Net-pay calculations were also improved(next page). Standard QLA well-log analysisusing three criteria—porosity greater than 12%,water saturation less than 70% and shale vol-ume less than 50%—identified 42 ft [13 m] ofpay. An ELAN gas-resource profile using thesame criteria identified 50% more pay, or 65 ft[20 m]. The increase was a result of computingdifferent porosities and shale volumes based onadditional ECS and CMR data.
Other wells have been logged and com-pleted using CMR permeability as input. Fol-lowing reservoir characterization, Conoco andSchlumberger used more accurate and consistentpermeability, fluid saturation, net pay and stresspredictions from the Lobo-specific model todesign optimized fracture stimulations thatreduced well completion costs and significantlyincreased gas production.
48 Oilfield Review
Sidewall-cores corrected fornet overburden
Porosity
Neutron porosity
vol/vol0.3 0.0Density porosity
vol/vol0.3 0.0Total CMR porosity
Crossover CMR free fluid
Highporosity
Highporosity
Lowporosity
vol/vol0.3 0.0
Free fluid CMR permeability, mD
Density porosityvol/vol0.3 0.0
CMR bound fluidvol/vol0.3 0.0
StandardTimur-Coates equation0.002 20.0
Lobo-specificTimur-Coates equation0.002 20.0
Porosity and permeability do not correlate
> Optimizing log permeability versus Lobo formation sidewall core values. A Timur-Coates equation modified specifically for the Lobo formation using exponents providedby Schlumberger-Doll Research provided a better core-log correlation throughout thethird well of the Lobo permeability evaluation (red curve).
Porosity
Neutron porosityvol/vol0.3 0.0
Density porosity
vol/vol0.3 0.0Total CMR porosity
Crossover CMR Free fluid
vol/vol0.3 0.0
Free fluid
Density porosity
vol/vol0.3 0.0CMR bound fluid
vol/vol0.3 0.0
0.002 20.0
0.002 20.0
CMR permeability, mD
Sidewall-cores corrected fornet overburden
StandardTimur-Coates equation
Lobo-specificTimur-Coates equation
> Validating optimized log permeability versus Lobo formation sidewall core values. In Track 3,log data from the first well in this project were reprocessed using the Lobo-specific equationto validate the permeability model (red curve). Except for one core point near the bottom ofthe log section, the new expression provided a much better match between sidewall coreand CMR permeabilities (purple dots).
Winter 2000/2001 49
> Comparison of net pay analyses. Standard QLA well-log analysis using standard criteria of porosity greaterthan 12%, water saturation less than 70% and shale volume less than 50% identified 42 ft [13 m] of pay (top).With the same criteria, an ELAN gas-resource profile computed 50% more pay, or 65 ft [20 m], but with shale volumes obtained from the ECS Elemental Capture Spectroscopy tool for geochemical logging (bottom).
Permeability
Quartz
Clay-bound water
Calcite
Originalmodel shale
indicator
Chlorite
Illite
Porosity
Irreducible water
Net pay equals 65 ft using ELAN model with CMR and ECS datap.u.0.5 0
Gamma ray
API0 200 10 0.001
Permeability Effective porosity
mD 00.2 p.u.
Volume
100%0
Densityporosity
Neutronporosity
Effectiveporosity
Gas volume
Effective porosity
Net pay equals 42 ft using 12% porosity, 70% water-saturation and 50% shale-volume cutoff criteria
Gamma ray ResistivityPay
Neutron porosity
Density porosity
Shalevolume
Gasvolume
Apparent water
resistivity
Interactive Reservoir Characterization The PowerSTIM approach applies the best avail-able resources to understand wells, fields andbasins, and presents tailored recommendationsin a technically sound, easily understood format.A comprehensive log, or data montage, is built tocommunicate formation evaluation, well analy-sis, reservoir characterization and completion orstimulation designs along with predictions,results and post-treatment evaluations (right).This tangible hard copy embodies the valueinherent in an intangible methodology.
For the operator, this life-of-the-well montageis an invaluable reference for quick access towell information. The PowerSTIM montage con-tains detailed reservoir characterization andcompletion information in a continuous mode.The cover shows well location and relevant back-ground data. Inside, a section of mud log or open-hole log, core analysis and other test dataidentifies pertinent zones. A wellbore configura-tion shows the completion design and perforationrecord. Additional sections present stimulationdesigns, productivity analyses, production logsand actual production data. As a project pro-gresses, the PowerSTIM team updates the mon-tage, which can be used later to assess futurewell completions.
A Web-based intranet tool provides a frame-work for collaboration among various technicaldisciplines as well as between the operator,Schlumberger and third parties. Beginning withan entire montage presentation, team memberscan zoom in on any area of the montage for amore detailed view (next page, top). This tool ispart of Schlumberger’s internal Internet hub, aWeb-site accessible only by authorizedSchlumberger personnel. PowerSTIM teams canuse the intranet tool at any time, from any loca-tion with an Internet connection. For example,data uploaded from a field location in the MiddleEast by one team member can be accessed inminutes by a product center providing support orby another team member in an office in Houston.
The PowerSTIM intranet tool allows rapidintegration of knowledge from several disparatesources to facilitate faster, easier and betterteamwork. Project data uploaded by engineersare automatically incorporated into the PowerSTIMmontage, which is built in a fraction of the time itwould take each engineer just to handle and printvarious components separately. A montage iscompleted almost as soon as all data are col-lected. In fact, completion recommendationshave been delivered before logging trucks leavea location.
A stimulation-optimization project beginswhen the project manager, or coordinator, selects
experts from around the world who are notified bye-mail and assigned specific tasks. PowerSTIMteams should include petrophysicists, reservoirand production engineers and stimulation design-ers. This team should be involved as early as pos-sible in the drilling and completion designprocess. Optimization success is related directlyto project inception and timing. Projects that havea PowerSTIM team in place during the planningstages are usually extremely successful.
Once data are collected and analyzed, thePowerSTIM team designs a customized comple-tion based on improved reservoir characterization.Again, because of the intranet tool, these effortscan take place miles from the source of data.Completion designs and completion results canbe compiled and integrated into the montage, sothat the entire history of a stimulation treatmentis in one simple document. Running Monte Carloand other economic simulations for critical wellparameters also factors in risk.10
From initiation of a new project through post-completion, post-stimulation analysis, the intranettool essentially creates a virtual office for stimu-lation-optimization projects. Team members sepa-rated by hundreds or thousands of miles interactand exchange data efficiently and effectively todeliver completion and stimulation solutions “online” and “in time” to meet client needs. A fulland complete record is produced, just as if teammembers were working side by side.
PowerSTIM workflow using the intranet toolis an excellent example of how truly integratedsolutions are implemented by drawing on world-wide technical expertise, fit-for-purpose technol-ogy and knowledge-management systems withstate-of-the-art information technology. Quicklydelivering completion recommendations is a keycontribution of this workflow. Applications ofPowerSTIM methodology and tools for candidaterecognition and selection include: • optimize profitability in heterogeneous reservoirs
where conditions vary • improve performance of marginal fields • overcome completion problems or failures in
areas where others are succeeding • reengineer completions to maintain or exceed
past production at costs that are lower, thesame or higher, but economically justifiable.
Improving a Marginal Development Kerns Oil and Gas Inc., San Antonio, Texas, usedthe PowerSTIM methodology to optimize comple-tions in the Olmos and San Miguel tight-gasformations of South Texas, which include theCatarina S.W. and Dos Hermanos fields.11 Wellsin these fields are drilled and completed to pro-duce from either or both formations. Improvingstimulation results could justify additional devel-opment of this marginal area, but acquiring per-meability data to evaluate pay zones and designfracture treatments is difficult.
50 Oilfield Review
Well-completiondesign
Well testingReservoirconsiderations
Geologic considerations
Post-job evaluation
On-site job execution
Stimulation design
Stimulation design
Completion design
Reservoir characterization
Descriptiveheader
SummaryPost-job evaluation
On-site job execution
> PowerSTIM montage. The PowerSTIM montage is a large, paper report similar to a well log thatfully documents and completely displays relevant well data and interpretations from multiple sources.
Winter 2000/2001 51
The Olmos formation in this region consists ofindividual sands 5 to 50 ft [1.5 to 15 m] thick overan interval of several hundred feet. Many indi-vidual sands will not produce naturally andpermeability has to be assumed, leading to mis-calculations of fluid leakoff into the formation.Published permeabilities for laminated Olmossands range between 0.07 and 10 mD with avariation based on local experience of 0.04 mD toseveral millidarcies. The Olmos formation haslow compressive strength, and shales fracture aseasily as sands. Values for Young’s modulus inanother Olmos field range from 1.7 to 2.0 millionpsi [12 to 14 thousand MPa]. In the area whereKerns operates, hardness calculated from DSI logdata indicates that Young’s modulus is between1.2 and 4.8 million psi [8 to 33 thousand MPa].San Miguel sands vary from 0.1 to 33 mD.
To improve completion techniques and wellperformance, a PowerSTIM team studied severalwells and modified the completion techniques.First, high-performance UltraJet charges replacedstandard perforating charges to increase forma-tion penetration. Next, porosity and permeabilitymeasurements from CMR logs and mechanicalproperties from DSI logs were used to improvestimulation candidate selection. The team usedCMR-derived permeability to determine leakoffand design fracture stimulations. Accurate com-pressional and shear data to derive Young’s mod-ulus and Poisson’s ratio were obtained from DSIlogs. FMI microresistivity images helped addressfracturing fluid leakoff and well placement byidentifying fault planes and preferred fractureplane (PFP) orientation. Cost per well for perfo-rating increased $4,000 and stimulation costs
were $20,000 higher, but initial productionincreased from about 400 to 1000 Mscf/D[11,500 to 29,000 m3/d].
In the first well, CMR-derived permeabilitieswere compared with estimated well production(above). The zone of interest with 17% porosity and
Descriptiveheader
ReservoirCharacterization
CompletionDesign
Execution Fracturedesign
Summary
. PowerSTIM intranet tool. The entirePowerSTIM montage is built dynamicallyon the PC desktop to deliver integratedsolutions “in time” for ongoing projects.
> Nuclear magnetic resonance (NMR) permeability. The CMR CombinableMagnetic Resonance log confirmed a spontaneous potential interpretationof sand gradation fining upwards, with the most permeable zone—4 mD—at the bottom. If the well had been perforated in the top 6 ft [1.8 m] as origi-nally planned, initial production would have been low, and an unnecessarystimulation treatment would have been performed.
10. Bailey W, Couët B, Lamb F, Simpson G and Rose P:“Taking a Calculated Risk,” Oilfield Review 12, no. 3(Autumn 2000): 20-35.
11. Fairhurst DL, Marfice JP, Seim MR and Norville MA:“Completion and Fracture Modeling of Low-PermeabilityGas Sands in South Texas Enhanced by MagneticResonance and Sound Wave Technology,” paper SPE59770, presented at the SPE CERI Gas TechnologySymposium, Calgary, Alberta, Canada, April 3-5, 2000.
>
Bound fluid
MD1:120
ft
Gamma ray
API 1500
Timur-Coates
mD 100.001
CMR free fluid T2 cutoff=33 msec
ft3/ft3 00.3 30000.3
SP PermeabilityTotal CMR porosity T2 distribution
ft3/ft3 00.3
Density porosity
ft3/ft3 00.3
X150
X160
X170
X180
4 mD
Perforatedinterval
10-ohm-m resistivity calculates as 52% watersaturation. A RFT Repeat Formation Tester toolindicated 0.05-mD permeability at the top of thiszone; the bottom was not tested. A modifiedTimur-Coates permeability equation was used tocalculate 4-mD permeability from CMR data forthe lower 3 ft [0.9 m] of this section. The T2 dis-tribution supported an interpretation of sandgrains fining upward. The most permeable sectionwas in the 3 ft at the bottom of the sand. Neutronand density curves indicated significant porosity,but data were suspect because the section over-lies lignite and washed-out hole data. Late T2
decay times, however, were not due to hole con-ditions, so the lower section was tested.
This well was perforated over a 10-ft [3-m]interval indicated by gamma-ray data to be sand.Analysis using NODAL techniques for 4 mD and
3-ft net pay predicted production of 600 Mscf/D[17,000 m3/d]. Actual production was 700 Mscf/D[20,000 m3/d] without stimulation, a reasonablematch. The CMR permeability helped identify asection that might have been bypassed. The com-pletion interval and well productivity were antici-pated from the NMR-T2 distribution and resistivityprofile. Using permeability criteria, not densityporosity, resulted in an economical natural comple-tion, and a fracturing treatment was not needed.
Pore-size and permeability estimates fromCMR data were used for the second well, whichhas two thick Olmos sands located close together(below). The low-permeability sand was testedand stimulated because it met the standard 12%density porosity and 12% resistivity cutoffs, butresults were poor. This zone was plugged and theupper Olmos sand was completed successfully.
A CMR-based interpretation, which indicated thatthe lower sand has higher porosity and lower per-meability than the upper sand, would have rec-ommended that engineers intentionally bypassthe lower interval, saving stimulation and well-testing expense.
Permeability data are extremely important inall stimulation designs. Two FracCADE 3D frac-ture stimulation designs were compared for athird well (next page, top). The first design wasmodeled using permeability based on previouslocal knowledge, side-wall core estimates andpublished data for the region. The resulting frac-ture design length is less than required for opti-mal stimulation. To get the required fracturelength and width, the treatment size would haveto increased, which results in a more expensiveand, at the same time, less efficient stimulation.The possibility of a premature screenout is alsomuch higher with overdesigned jobs.12 The sec-ond design used permeability estimates from aCMR-Plus log to optimize the fracture designsand minimize potential job execution problems.
In a fourth well, DSI data were used in con-junction with CMR data. Young’s modulus fromsonic data varied from 2.5 to 4.5 million psi [17 to 31 thousand MPa] in sands and from 2.0 to 2.5 million psi [13 to 17 thousand MPa] inshales. Permeability varied from 0.003 to 0.5 mD in the sands. The fracture design for thiswell indicated a half-length of 800 ft [244 m].Interference was a concern because of an offsetwell about 1000 ft [305 m] to the northwest. Sonicdata show stress orientations even when boreholeovality, or breakout, is not apparent from FMIimages, so DSI logs were acquired to determinePFP direction. Determining fracture orientation canalso optimize well placement and gas recovery.
Directional data from DSI sonic logs and FMIimages were used to determine proper wellplacement and ensure effective reservoirdrainage. Most sands in this region have a NE-SW fracture orientation, but there is somevariation. The FMI data corroborated this direc-tion. The fracture direction was away from theoffset well, so pumping was initiated. No con-nectivity or interference was detected.
Another issue in these fields was inadequatebit performance. It was taking 20 days to drillwells, and the resulting poor borehole conditionswere adversely affecting formation evaluation and reservoir characterization. Overgauge andwashed-out holes caused log measurements to be unreliable, wasted cement and hindered zonalisolation. A synthetic rock-properties log wasdeveloped to select and run proper bits.13 A stableReed-Hycalog bit design cut drilling time to only
52 Oilfield Review
. CMR log and density log fortwo Olmos sands. An interpre-tation using density porosityalone indicated that the lowersand was more desirable.However, higher permeabilityin the upper zone correlateswith long relaxation times inthe NMR T2 distribution (top).Production results confirm thisCMR interpretation. The lowerzone was plugged after unsuc-cessful perforation and stimu-lation (bottom). The upper zonewas completed successfully.An interpretation based ondensity porosity alone indi-cated that the lower sand wasthe more desirable zone forproduction—an erroneousconclusion.
>
Bound fluid
Permeability
MD1:120
ft
Gamma ray
API0 150
CMR free fluid
ft3/ft30.3 0
Timur-Coates perm.
mD0.001 10
Total CMR porosity
ft3/ft30.3 0
Density porosity
ft3/ft30.3 0
T2 cutoff=33 msec
T2 distribution
30000.3
Resistivity less than 12 ohm-m Density porosity less than 12%
Stimulated and producing
X820
X830
X840
X850
0.1 mD
10 p.u.
Bound fluid
Permeability
MD1:120
ft
Gamma ray
API0 150
CMR free fluid
ft3/ft30.3 0
Timur-Coates perm.
mD0.001 10
Total CMR porosity
ft3/ft30.3 0
Density porosity
ft3/ft30.3 0
T2 cutoff=33 msec
30000.3
T2 distribution
Resistivity greater than 12 ohm-m Density porosity greater than 12%
Stimulated and plugged
X150
X160
X170
X180
0.01 mD
12 p.u.
Winter 2000/2001 53
10 days, improved log quality to help identify addi-tional pay and resulted in better cement jobs withless cement. This additional step ensured accuratedata for optimizing future well completions.
Well completions in this area are now more successful (right). Previously bypassed sandsthat once appeared marginal are adding signifi-cantly to total production. Data-acquisition costsincreased by $20,000 per well; perforating costsincreased $4,000; and stimulation charges rose$30,000. However, comprehensive data acquisi-tion and optimized completions have more thandoubled production rates. Average well productionincreased from about 1 MMscf/D [29,000 m3/d],and finding costs dropped by a factor of three,from $1.47 to $0.48/Mscf.
Solving Stimulation-Related Problems Fracturing the Morrow formation in southeastNew Mexico, USA, is problematic. Morrow gassands in this region are low-pressure and poten-tially water-sensitive, with permeabilities rang-ing over three orders of magnitude. The bestwells are completed naturally; those in lowerquality reservoir rock are fractured. To addresswater-sensitivity and avoid screenout while fracturing, a common practice is to pump low-viscosity foamed fluids with low proppant con-centrations that yield narrow fractures with lowconductivity. Operators in this area approachstimulation treatments cautiously and generallyaccept less than optimal results.
Most completions are planned using threegeneric guidelines: wells are completed withoutfracturing if zones produce at economical rates;fracture stimulation is a last resort if productionfalls below the economic limit; and aqueousfluid systems are avoided because of a water-sensitive formation. Although not universal,these approaches represent the thinking ofmany operators involved in Morrow develop-ment during the past 20 years.
Early attempts to fracture the Morrow withwater-base systems were marginally successful.Studies suggested that poor results were due towater-sensitive clays or capillary-pressureeffects that reduce reservoir permeability as aresult of exposure to fracturing fluids. Low reser-voir pressure exacerbates the latter. These issues
12. A screenout is caused by proppant bridging in the frac-ture near a wellbore, which halts fluid entry and fracturepropagation. If a screenout occurs early in a treatment,pumping pressure may become too high and the job maybe terminated before an optimal fracture can be created.
13. Besson A, Burr B, Dillard S, Drake E, Ivie B, Ivie C, Smith Rand Watson G: “On the Cutting Edge,” Oilfield Review 12,no. 3 (Autumn 2000): 36-57.
> More accurate fracture modeling. Permeability estimates used in the first FracCADEdesign (top) are higher than the CMR log-derived permeability that was used for thesecond design (bottom), resulting in a shorter fracture half-length—600 ft [183 m] versus 800 ft [244 m]. Other rock properties were held constant for both cases.
> Completion and field development optimization. During a 35-well drilling program, productionperformance was optimized through high-performance perforating, improved formation evalua-tion and log interpretation with advanced logging technologies, and proper fracture design anddrilling-bit selection.
35-Well Development Drilling Program (Groups of 5-Wells)
High-performanceperforating and improved
log interpretation
PowerSTIM reservoir characterizationand optimized fracture stimulations
Optimized bits
1998 1999 2000
Gas
rate
, Msc
f/D
3000
2500
2000
1500
1000
500
< 0.0 lbm/ft2 0.0-0.10.1-0.20.2-0.30.3-0.40.4-0.50.5-0.60.6-0.70.7-0.8> 0.8
X4800
X4900
X5000
X5100
X5200
X4800
X5000
Dept
h, ft
Dept
h, ft
X5100
X5200
X4900
3600 4400Stress, psi Fracture width at wellbore, in. Fracture half-length, ft
800-ft half-length
Greatest concentration of proppant
Greatest concentration of proppant
0.1 0 0.1 0 400 800 1200
3600 4400Stress, psi Fracture width at wellbore, in. Fracture half-length, ft
0.1 0 0.1 0 400 800 1200
< 0.0 lbm/ft2 0.0-0.10.1-0.20.2-0.30.3-0.40.4-0.50.5-0.60.6-0.70.7-0.8> 0.8
600-ft half-length
Proppantconcentration
Proppantconcentration
were addressed by pumping treatments ener-gized with nitrogen [N2] or carbon dioxide [CO2],and using methanol in fracturing fluids. However,stimulation results with these foamed systemshave been inconsistent.
In higher permeability zones where near-wellbore damage is bypassed, small fracturingtreatments using foams are successful, but inlower permeability zones where fracture lengthis critical to stimulation success, these systemsdo not provide consistent economical results.These treatments address water-sensitivity, butlow viscosity, high friction pressure and chemicalrequirements increase screenouts and cost. Earlyscreenout and low proppant concentrations leavewells producing at less than full potential.
Fracture-treatment designs that develop ade-quate hydraulic width and transport higher con-centrations and volumes of proppant were neededto maximize production, but this required reliablevalues for reservoir parameters and rock proper-ties. With a comprehensive understanding of thereservoir, stimulation specialists can design fluidsand proppants to create hydraulic fractures thatdeliver high conductivity and optimal results.
Louis Dreyfus Natural Gas Inc. drilled theETA-4 well in March 2000. Pressure data werenot available, but a bottomhole pressure of2000 psi [13.8 MPa] was measured in an offsetMorrow well. Wireline logs identified a homoge-neous, high-quality, 10-ft [3-m] Morrow zone withabout 14% porosity and 20% water saturation.Rotary sidewall cores verified the log interpreta-tion. A zone of this quality should produce natu-rally, but high permeability and low pressuremake the interval susceptible to drilling damage.Significant separation between resistivity curvesconfirmed deep invasion, so to overcome near-wellbore damage, the operator wanted to designa fracture stimulation in advance.14
Reservoir quality in the ETA-3 well, com-pleted two months earlier, was similar, but withhalf as much pay. This well was perforated andfracture stimulated down 5-in. casing with CO2
foam and high-strength, man-made ceramicproppant. Surface treating pressure was 5000 psi[34 MPa]; maximum proppant concentration was4 ppa; and there were indications of possiblescreenout near the end of the job. Post-stimula-tion production stabilized at 1.7 MMscf/D
[49,000 m3/d] and 500-psi [3.4-MPa] flowingtubing pressure (FTP) at surface.
Because reservoir quality was equivalent andpay was twice as thick, the operator expectedETA-4 to be an excellent well. However, produc-tion after perforating was only 500 Mscf/D[14,000 m3/d] with 220-psi [1.5-MPa] flowing cas-ing pressure (FCP), which was equivalent to anextremely damaged completion with a positive 45skin. The next step was to determine optimal frac-ture length using actual reservoir parameters (left).
To take full advantage of this quality reser-voir, the operator wanted to design a more con-ductive fracture using higher than conventionalproppant concentrations. However, because theoffset fracture treatment indicated a possiblescreenout at 4 ppa, this would not be easy.Premature screenout limits production rates afterfracturing and are common in the Morrow for-mation. Stimulation engineers consider near-wellbore tortuosity to be a factor that needs tobe addressed to minimize the likelihood of ascreenout (below). Properly oriented perforationsmitigate tortuosity effects. The maximum stressdirection, or preferred fracture plane (PFP), is per-pendicular to borehole breakout as indicated byFMI log data and oriented NW to SE in the ETA-4 well. This information was used to orient per-forating guns in the direction of maximumformation stress using a Wireline OrientedPerforating Tool (WOPT) system.
Higher proppant concentration—6 versus 4 ppa—to increase fracture width was possiblebecause oriented perforating reduces the risk ofa premature screenout caused by near-wellboretortuosity. At 6 ppa, the FracCADE programshows a fracture half-length of 300 ft [91 m] anda width of 0.15 in. [3.8 mm], twice as wide as
54 Oilfield Review
> Formation damage, or skin, effects and optimal fracture length. A NODALanalysis indicates that with zero skin, the ETA-4 well should produce 3.3 MMscf/D[94,500 m3/d] at 500-psi [3.4 MPa] flowing tubing pressure (top). An optimizedfracture would reduce skin to –4 and result in production above 5.5 MMscf/D[157,500 m3/d]. Maximum net present value (NPV) is achieved with a 200-ft[61-m] fracture half-length (bottom).
Pinch points
Preferred fractureplane (PFP) Maximum-stress
direction
Minimum-stressdirection
> Tortuosity and fracture initiation. When a frac-ture initiates randomly, there is a good chancethat it will propagate into the reservoir along aplane other than the maximum stress direction, orpreferred fracture plane (PFP). The fracture thenturns to follow the path of least resistance andalign with the PFP, causing pinch points that mayact as a choke and lead to premature screenout.
2000
1500
1000
500
01000
Pres
sure
, psi
Net
pre
sent
val
ue (N
PV),
$100
0
2000 3000 4000Gas rate, Mscf/D
5000 6000 70000
Skin = 0 Skin = 45 Skin = 4 Outflow
710
720
730
740
750
760
0 100Fracture half-length, ft
200 300 400 500
FracCADE output
NODAL output
Production time =1 year
Winter 2000/2001 55
a 4-ppa design (right). This treatment appears tobe overdesigned, but local experience suggeststhat to realize an effective 200-ft [60-m] conduc-tive fracture, a 300-foot design target is notunreasonable considering the potential for frac-ture conductivity damage after fracture closure and production begins.
Treatment pressures highlight the impact oforiented perforations on job execution (belowright). Pump rates for the two stimulation treat-ments are identical at 30 bbl/min [4.7 m3/min],but the conventional fracture stimulation showsa treating pressure of 5000 psi [34 MPa], whilepressures with oriented perforation rangebetween 3000 and 4000 psi [20 and 27 MPa].Another important indicator is pressure responseafter pumping stops. On the conventional job, ittakes 15 minutes for pressure to drop below3000 psi, suggesting that net pressure wasincreasing and this job was close to screenout.For the oriented fracture, pressure stabilizesalmost immediately, suggesting higher proppantconcentrations could be placed. Advances in fluidsystems and optimized fracture designs make itpossible to use either foam or water-base sys-tems to stimulate the Morrow formation.
Early production history for ETA-4 indicated a successful stimulation. Post-fracture produc-tion was 3.5 MMscf/D [1 million m3/d] with1280-psi [9-MPa] FTP compared with 500 Mscf/Dwith a flowing casing pressure of 220 psi beforestimulation. Because the goal was to bypassdrilling damage, skin is a good indicator of frac-turing success. Prestimulation production of 500 Mscf/D suggests a skin of 45, while a post-stimulation rate of 3.5 MMscf/D [99,000 m3/d]suggests that skin was reduced to -4.
Analysis shows that with 4 ppa maximumproppant concentration and 0.06-in. [1.5-mm]fracture width, the ETA-4 well would produce 2.2 MMscf/D [63,000 m3/d] at 1280-psi FTP. Iffracture width is increased to 0.15 in., productionrises to 3 MMscf/D [85,000 m3/d] with 1280-psiFTP. The well actually produced more, suggestingcreation of a slightly wider fracture. Orientedperforating allowed proppant concentration andfracture width to be increased, eliminated pre-mature screenout and the need to clean out wellsafter fracturing, and resulted in an additional 1.3 MMscf/D [34,000 m3/d]. The payout for incre-mental perforating costs was only three days.
14. Logan WD, Gordon JE, Mathis R, Castillo J and McNallyAC: “Improving the Success of Morrow Stimulations theOld-Fashioned Way,” paper SPE 67206, presented at theSPE Production Operations Symposium, Oklahoma City,Oklahoma, USA, March 24-27, 2001.
> Fracture designs for the ETA-4 well. Although fracture half-length and height are similar,fracture width using 4 ppa (top), as on offset well ETA-3, is half of that for 6 ppa (bottom).
7000
8000
5000
6000
3000
4000
1000
0
2000
35
40
25
30
15
20
5
0
10
84 87
Pres
sure
, psi
Pum
p ra
te, b
bl/m
in
90 93 97 100 103Pump time, min
106 109 113 116 119 122 125 129
Treating pressure—conventionalTreating pressure—oriented
Conventional—4 ppa
Oriented—6 ppa
Pump rateETA-4 well,oriented
Pump rateETA-3 well,conventional
> Comparison of conventional and oriented fracture treatments. The biggestdifference is in surface treating pressure. While proppant concentrationsteps from 1 to 4 ppa on the ETA-3 well and from 1 to 6 ppa on the ETA-4 well,treating pressures are significantly lower on ETA-4 than on ETA-3 as a resultof orienting the perforations with the maximum stress direction, or preferredfracture plane.
Dept
h, ft
Stress, 1000 psi Fracture width at wellbore, in. Fracture half-length, ft
Proppantconcentration
8 9 10 0.1 0 0 200 400 6000.1
< 0.0 lbm/ft2
0.0-0.10.1-0.2 0.2-0.3 0.3-0.4 0.4-0.5 0.5-0.6 0.6-0.7 0.7-0.8 > 0.8
X1900
X2000
X2100
X2200
X2300
X2400
Greatest concentrationof proppant
300-ft half-length
Dept
h, ft
0.1 0.10 0 200 400 600
< 0.0 lbm/ft2
0.0-0.1 0.1-0.2 0.2-0.3 0.3-0.4 0.4-0.5 0.5-0.6 0.6-0.7 0.7-0.8 > 0.8
X1900
X2000
X2100
X2200
X2300
X24008 9 10
Stress, 1000 psi Fracture width at wellbore, in. Fracture half-length, ft
Proppantconcentration
Greatest concentrationof proppant
400-ft half-length
Reengineering CompletionsUltra Petroleum, Inc., one of the most activeoperators in the prolific Jonah gas field ofSublette county, Wyoming, USA, wanted toreduce completion time and costs, and increaseproduction. This field is a fault trap within alarger gas accumulation in the Green River basin.Production comes from the Upper CretaceousLance formation, a thick sequence of stacked,interbedded channel sands, overbank silt-stones and floodplain shales from about 8900 to13,500 ft [2713 to 4111 m]. The Lance formationis gas-charged, but noncommercial throughoutmuch of the basin. Shallow overpressure makesgas production economical in the Jonah field.
Low permeability and multiple pay zonesacross large sections make it difficult to com-plete wells economically. The gross horizon is2800 to 3600 ft [853 to 1097 m] thick with morethan 100 separate sands that range from 2 to 30 ft [0.6 to 9 m] thick. Pay intervals consist ofboth individual 10- to 15-ft [3- to 4.5-m] zonesand stacked channel sequences more than 200 ftthick. Porosity is between 6 and 12%, and per-meability ranges from 0.001 to 0.5 mD. Each sandrequires stimulation to produce viable rates.
The completion interval is grouped intostages with 50 to 200 ft [15 to 61 m] of gross paythat are fractured separately. If there are toomany stages, costs increase significantly andreturn on investment is reduced. With too fewstages, some sands are not stimulated ade-quately and production is compromised. The fun-damental problems faced by engineers aredetermining which sands to complete and whichto skip; how many sands to include and how togroup them in stages; and how to isolate betweenstages after fracturing.
Historically, using limited-entry diversion overlonger intervals to control costs minimized thenumber of fracturing treatments. Stages rangedfrom 100 to 450 ft [30 to 137 m] with 20 to 28 per-forations per stage. After an interval was stimu-lated, the well was flowed to clean up andrecover fracturing fluids, and to test productivity.One to two weeks later, fractured intervals wereisolated with mechanical plugs—retrievable ordrillable—or sand plugs, and the next intervalwas perforated and fractured. This process con-tinued until the well was completed. Typically,three to six intervals were fractured per well overabout five weeks. These traditional completionsoften cost as much as drilling the well, take a lotof time and yield disappointing results.
The PowerSTIM methodology was applied tothis complex reservoir with impressive results
56 Oilfield Review
XX000
XX100
XX900
XX800
MDft
Inadequatelystimulated pay
intervals
Little to noproductioncontribution
Flow rateB/D
GRPass 2
API0 200
0 lbm/ft2 6
GRFracture width, in.
TotalScandium
TotalIridium
Cased-hole
Sandconcentration
Formation
Scandium
Iridium
Formation
Scandium ScandiumIridium
Iridium
> Diversion in original completions: radioactive tracers and production logs. With limited-entry tech-niques, some intervals were found to be unstimulated. In this example, six pay sands over 300 ft [91 m]of gross interval were fractured through 24 perforations. The lower zones are inadequately stimulated(left) and contribute little or no production. If an interval did not take fluid at the beginning of a treatment,perforation erosion in other sands eliminated the backpressure necessary for diversion.
3 of 4 sand layerstreated with
proppant fracture
No productioncontribution fromstimulated sand
layers
XX400
XX500
XX300
XX200
Flow rateB/D
GRPass 2
MDft
API0 200
0 lbm/ft2 6
GRFracture width, in.
TotalScandium
TotalIridium
Cased-hole
Sandconcentration
Formation
Scandium
Iridium
Formation
Scandium ScandiumIridium
Iridium
>Why sands at the top of perforated and fractured intervals fail to produce. Production logging quantifiedcompletion efficiencies throughout the field, and when combined with tracer surveys, provided clues tothe reason some zones did not produce. In original completions, tracer surveys indicated proppant inthe upper sands of most intervals where production logs often show no production. Analysis indicatedthat proppant placed during fracturing was overdisplaced in the formation during the setting of sandplugs for the next stage.
Winter 2000/2001 57
and far-reaching impact on completion success.Once again, the key was to develop a model toestimate permeability, rock properties and indi-vidual sand-layer productivity.
The first phase of the project incorporatedwork done by Amoco, now BP, and Schlumbergerto relate core and transient pressure tests to logresponses.15 After traditional completion tech-niques developed by other operators were ana-lyzed, several problems were apparent.Radioactive tracer surveys showed that manytargeted sands were not stimulated, and produc-tion logs indicated only about 60 to 70% of paysands were producing gas (previous page, top).
Sand plugs were difficult to place and oftenended up missing or too low, resulting in costlyprocedures to reset them. Production logsrevealed that there was no production from manyupper sands in a fractured interval, but radioac-tive tracers show proppant was placed in thesezones (previous page, bottom). Many wells hadthe same problem, indicating that proppant fromthe near-wellbore region may be displaced whensand plugs are set before fracturing subsequentstages. Tracer surveys also indicate fracturecontainment, but net-pressure plots show con-siderable fracture-height growth. Even withlimited-entry perforating for diversion, there may be connectivity between perforated andunperforated sands.16
The Jonah reservoir lacked complete charac-terization. Fracturing jobs rarely screened out andsand plugs for zonal isolation frequently end updisplaced into the formation, suggesting thepotential to optimize stimulation designs byincreasing treatment size. To make completionand stimulation decisions, the PowerSTIM teamneeded to evaluate key parameters, includingstress gradients for fracturing model geometryand proppant selection, Young’s modulus forfracture width, leakoff for fluid optimization, and reservoir pressure for staging strategy andfluid requirements.
The greatest challenge was deciding how toacquire additional data without compromisingprofitability. This was accomplished by carefullyplanning strategic logging programs, minifrac-ture treatments and pressure-transient analysis.Fracture gradient, Young’s modulus and leakoffparameters for fracture fluids were determinedfrom minifracture treatments using the DataFRACservice (above). Dipole sonic logs were used tobuild near-wellbore stress models, calibratestress profiles for sand-shale sequences anddetermine preferred fracture direction. Thesedata confirmed stress values from minifractures.
Fracturing models using more reliable stressmeasurement in sands and bounding layers,coupled with better Young’s modulus values,
provided improved fracture height and widthestimates. Cores were analyzed to understandfluid compatibility and verify rock mechanicalproperties. Early geologic interpretations assumedthere were natural fractures with high fluid leakoffthroughout the field, but image logs did not showsignificant natural fractures in the heart of thefield. Lower than expected fluid leakoff fromDataFRAC analysis and absence of natural frac-tures allowed pad volumes to be decreased, whichreduced costs.17
The geological complexity of this field requiresa method for completing multiple horizons in a single day without sand plugs or mechanicalisolation. This approach allows shorter intervalsto be stimulated, increases production efficiencyand improves development economics. Diversionwith limited-entry techniques and sand plugsresults in poor completion efficiencies. Mechan-ical isolation with bridge plugs or packers wascomplicated, costly and risky to retrieve by eitherconventional workover or coiled tubing.
To stimulate wells more effectively, theoperator decided to fracture smaller verticalintervals and perform multiple treatments in asingle day. The ideal diversion technique wouldallow cleanup of fracture intervals withoutneeding to wash out sand or retrieve packers.By designing a tip-screenout, net pressure
15. Christensen CJ, Cox DL, Lake EA, Dolan VB, Crisler JDand Lima JP: “Optimized Completion Techniques inJonah,” paper SPE 62853, prepared for presentation atthe SPE/AAPG Western Regional Meeting, Long Beach,California, USA, June 19-23, 2000.
16. Limited entry involves low shot densities—1 shot perfoot or less—across one or more zones with differentrock stresses and permeability to ensure uniform acid orproppant placement by creating backpressure and limit-ing pressure differentials between perforated intervals.
> Jonah field minifractures. The DataFRAC service is used to determine fluid leakoff coefficients,fracture-closure pressure and height growth, and Young’s modulus. A zone with good permeabilityand barriers away from adjacent intervals is selected to allow mechanical isolation. Pressure draw-down and buildup tests determine permeability, pressure and skin, or damage. A downhole memorygauge is used to acquire bottomhole pressures. A series of step-rate pump and flowback tests with2% potassium chloride water measures fracture-closure pressure. A small volume of fracturing fluid is pumped to determine its leakoff coefficient. Resulting net pressure is used to determine fractureheight and Young’s modulus.
The objective is to maximize stimulation efficiency andresults without mechanical isolation like drillable bridgeplugs and retrievable packers. Rubber ball sealers canbe used to seal open perforations and isolate intervalsonce they are stimulated so that the next interval can be treated. Because perforations must seal completely,hole diameter and uniformity are important.
17. The pad stage of a hydraulic fracturing treatment doesnot contain proppant, and is the volume of fluid thatcreates and propagates the fracture.
Determiningclosure values
Determiningleakoff coefficient
Redesignedfracture
Calculating Young’smodulus and
height growth
00 50 100 150 200 250
1
2
3
4
5
6
7
Trea
ting
and
botto
mho
le p
ress
ure
(BHP
) pre
ssur
es, 1
000
psi
Treatment time, min
8
9
10
0
15
30
45
60
75
90
105
Prop
pant
con
cent
ratio
n, p
paSl
urry
and
flow
back
rate
, bbl
/min120
135
150
Treating pressure, psi Calculated BHP, psi Slurry rate, bbl/min Proppant concentration, ppa Flowback rate, gal/min
generated during stimulation is used to divertsubsequent fracturing treatments into the nextinterval (below).18
After fracturing, wells are flowed to recoverat least one casing volume of fluid and allow thefracture to close. The next interval is then perfo-rated and fractured. This process is repeateduntil the entire producing horizon is stimulated.As many as 11 fracture stages have been per-formed in 36 hours, reducing the time required tocompletely stimulate a well from five weeks toless than four days, and increasing producing payto more than 90%.
In previous wells where limited entry wasused, an entire wellbore might have only 120 per-forations. With the new completion technique, a single fracture interval can have 120 per-forations. A well may have 1200 over the entire
interval to reduce the risk of leaving pay unstim-ulated. The new completion techniques increasethe maximum number of stages from 5 to 12intervals per well. Fracturing designs includedhigh proppant concentrations at the end of atreatment to maintain created width and maxi-mize net pressure after fractures close.
Tubulars were the final aspect to be analyzed.Initially, well designs used 4- or 5-in. casing toaccommodate high pump rates for fracturinglarge intervals. Because Jonah wells typicallyproduce water and condensate, unloading fluidsis critical to maintain production. After cleanup, 23⁄8- or 27⁄8-in. tubing was run into wells underpressure using a snubbing unit. With shortertreatment intervals and improved fluids, fracturesnow can be placed effectively at lower pump rates,making 3-in. tubing feasible for casing (above).This tubular configuration defers tubing installa-tion by several years and eliminates productionlimitations associated with small tubular sizes.
58 Oilfield Review
XX400
XX500
XX600
MDft
XX300
XX200
GRPass 2
API0 200 0 lbm/ft2 6
GR
Fracturewidth, in.
TotalScandium
TotalIridium
Cased-hole
Sandconcentration Formation
Scandium
Iridium
Formation
Scandium Scandium Iridium
Iridium
> Improved diversion. New completions, with 40% less gross interval perstage, allow pay sands to be treated more effectively. This radioactivetracer survey indicates successful placement of two separate fracturesless than 100 ft [30 m] apart without sand plugs or retrievable packersand drillable bridge plugs for positive isolation.
4 1/2
2 3/8
2 3/8 in 4 1/2
3 1/2
1 1/2
1 1/2 in 3 1/2
4.01.995 3.21 2.992 1.31 2.588
2850 675
1825 1525
290 1150
1287 1232 1267 1262 1200 1251
Tubular size, in. Equivalent insidediameter (ID), in. Rate, Mscf/D Bottomhole flowing
pressure (BHFP), psi
> Comparison of minimum gas rates to keep wellbore fluids unloaded. Based on a con-densate yield of 10 bbl/MMscf and a water yield of 3 bbl/MMscf with 700-psi [4.8-MPa]wellbore pressure, a well with 3-in. inside diameter (ID) casing continues to produce atabout half the rate of 4-in. ID casing before loading with fluid.
18. In standard fracturing, the fracture tip is the final areathat is packed with proppant. A tip-screenout designcauses proppant to pack, or bridge, near the end of thefracture early in a treatment. As additional proppant-laden fluid is pumped, the fracture balloons because it can no longer propagate deeper into the formation. This technique creates a wider, more conductive pathwayas proppant is packed back toward the wellbore.
Winter 2000/2001 59
With this background in mind, a specificPowerSTIM project was undertaken to furtherreduce completion time and costs in UltraPetroleum’s Jonah wells without jeopardizingproduction. Four reservoir rock types were identi-fied, and correlations were developed to computepermeability with CMR logs on key wells,Platform Express logs on infill wells and RSTmeasurements in wells where adverse hole con-ditions prevent openhole data acquisition.Similar correlations were developed to computerock mechanical properties—stress, Poisson’sratio and Young’s modulus. A unique zone-by-zone layering routine was developed to identifyand evaluate each of the hundreds of distinctlayers in the Lance formation. This routine even-tually evolved into the ZoneAID program.
A method was then developed to combineformation evaluation and stimulation designresults to predict production. This powerful toolallows a PowerSTIM team to quickly evaluatemultiple completion scenarios and determinewhich combination results in maximum produc-tion for the lowest cost. Currently, the time from
receiving log and well data to generating ratepredictions for all sands is only about four hours.Schlumberger delivers a PowerSTIM montage,including individual stimulation designs, produc-tion forecasts and economic evaluations for asmany as 17 fracturing stages, to Ultra Petroleumwithin forty-eight hours. Efforts are continuing toreduce this turnaround time even more with thehelp of the intranet tool.
However, the process does not stop here. Inperhaps the most important step, key wells areroutinely evaluated by running production logsafter three to six months to measure contribu-tions from each sand, make sure each sand wasadequately stimulated and assess productionpredictions. Production logs and production his-tories are evaluated using PSPLTR and PROFITsoftware to make sure fracture-model conductiv-ity and geometry are actually attained. By con-stantly evaluating and refining the optimizationprocess, the PowerSTIM methodology canachieve remarkable accuracy.
Like many of today’s lean and aggressiveenergy companies, Ultra Petroleum relies on newtechnology, innovative solutions and collabora-tive working relationships for technical servicesand support, innovative technologies and newintegrated solutions. New completion techniquesbased on extensive data collection reduced timeto first production and completion costs whileincreasing production and recovery factors.
Eliminating sand plugs and other positive iso-lation between fractured intervals, and extensiveflowback periods after each treatment savedmoney and almost four weeks of completion time(below). Reduced pad volumes, improved fluidand proppant selection, and optimized tubulardesign decreased costs. Overall completion costswere cut by 50%. Finding costs decreased from$0.45 to $0.23/Mscf.
When data are normalized for permeabilityand thickness, production from new completionsis 8% more than from original completions and30% higher than wells of other area operators,primarily from improved completion efficiencies.
SpudDrillingLoggingCementing
PerforatingFracturingFlowbackSetting plugs
TestingRetrieving plugsWell shut-inSales
August 1997
May 1998
February 1999
66 days
52 days
39 days
> Continual completion improvements. Over an 18-month period, time to first productionwas reduced by about 27 days, or four weeks, primarily by completing Jonah field wellswithout sand plugs or other forms of mechanical isolation.
Data also indicated an increase in estimated ulti-mate recovery (EUR) for new completions (above).
Completion Optimization Understanding reservoir characteristics across payzones in a well, an entire field and within a basinresults in optimized stimulation treatments andcompletion techniques that reduce costs, maximizeproduction and increase hydrocarbon recovery. ThePowerSTIM initiative uses an integrated approachto develop the required models for generatingtechnical solutions or well-completion strategiesthat are transportable from field to field and com-pany to company.
The positive impact and established record ofstimulation optimization in the mature onshorefield developments of North America are helpingto extend acceptance of the PowerSTIM method-ology into other areas both onshore and offshore,including regions like the Middle East.
A joint Saudi Aramco and Schlumberger pro-ject is now under way in the Hawiyah field, SaudiArabia, to eliminate sand production and maxi-mize production from the reservoir to meet thegas-deliverability target for this field. The projectinvolves stimulation optimization for a group of10 wells. Rather than undertake this effort inter-nally, Saudi Aramco chose to utilize thePowerSTIM approach and form a team of expertsto develop stimulation and completion solutions.
The PowerSTIM project manager is a SaudiAramco representative. A Schlumberger projectcoordinator heads joint technical and operational
teams. The technical team is made up of petro-phycists, geologists, reservoir engineers and stim-ulation engineers from each company who workwith the Saudi Aramco engineers that areassigned to designated wells. The operationalteam comprises Schlumberger field managersfrom wireline, well testing, cementing and stim-ulation, and coiled-tubing service segments whowork closely with Saudi Aramco site foremenand senior engineers.
The first stage of this project—model devel-opment—was completed in early 2001. A com-prehensive data set was developed to improvecompletion strategies and designs. Mechanicalrock properties, hydrocarbon resource profilesand sand-prediction models were either devel-oped or improved. In addition to optimal fracturedesign through integration of all available basin,field, reservoir and well data, this PowerSTIMproject is generating and documenting best prac-tices. Screenless completion guidelines wereadapted and distributed for use along with sand-control guidelines for well flowback.
Petrophysical models were initially applied tofour wells. In February 2001, the first well wasstimulated using recommendations based onreservoir and completion models developed bythe PowerSTIM team. Early results were extremelyencouraging. The project schedule calls for theremaining wells to be stimulated during the firsthalf of 2001.
Collaboration on this project proved to beextremely beneficial, particularly between SaudiAramco and Schlumberger, with interaction andworkflow continuing to improve. Staff from eachcompany appreciate the ability to contributeknowledge, experience and ideas to improvestimulation treatments and the well-completionprocess. Both companies benefit from thereduced engineering cycle time that is the resultof expediting the learning process, emphasizingadded value and targeting incremental produc-tion potential.
Stimulations based on estimates or on aver-age reservoir properties may result in hydraulicfractures of insufficient length and width withexcessive vertical height. Innovative methods toreliably establish the key parameters required forreservoir characterization, modeling and designprogram input overcome traditional limitationsinherent in acquiring these data.
Optimized stimulation treatments use continu-ous wireline measurements from advanced well-logging technology, core analysis, in-situ testing,and better data management, processing andinterpretation coupled with fit-for-purpose frac-turing technologies to ensure more contained,conductive conduits deeper into formations.
The PowerSTIM initiative is about full-cycleengineering, quality data and in-time delivery ofcustomized solutions. Procedures based on dataacquired throughout a field, region-wide experi-ence and application of careful reservoir evalua-tion are having beneficial effects on fielddevelopment. In addition, better evaluation offormation characteristics allows fracturing fluids,proppants and volumes to be optimized.Schlumberger is positioned to provide datameasurement, integration, formatting and pre-sentation, as well as interpretation expertise,technical design and evaluation, operations qualitycontrol and global support.
The way solutions are developed and commu-nicated within Schlumberger and externally tooperators is changing as the industry moves awayfrom static, or flat, documents and reports. Real-time information processing, data evaluation andlife-of-the well reports are becoming as importantas the answers and solutions they generate. Thelatest information technology (IT) systems andknowledge-management technologies are provid-ing us with methodologies and Web-based toolslike the PowerSTIM intranet tool and montage formaking informed decisions in a collaborative, vir-tual work environment. Through Internet meetingsand videoconferences, regional data houses andvisualization centers, we can work togetherwithout being in the same office. —MET
60 Oilfield Review
16,000
14,000
12,000
Estim
ated
ulti
mat
e re
cove
ry (E
UR),
MM
scf
Relative completion costs
10,000
8000
6000
4000
2000
00.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0
Offset control wells
Optimizedcompletion
model
Previouscompletion
model
> Recovery versus relative completion costs. Compared with wells completed conventionally,the average Ultra Petroleum, Inc. well in the Jonah field completed using the new techniqueshas a significant increase in estimated ultimate recovery (EUR) per completion dollar spent.
Tom Adams is director of Information Management & Technology (IM&T) at Kerr-McGee Oil & Gas Co. inOklahoma City, Oklahoma, USA. He provides leader-ship for the IM&T organization within Kerr-McGee,which is undergoing corporate transformation. Hebegan his career in 1983 with Sun Oil Company as areservoir engineer, working in locations throughoutthe western United States. In 1993, he joined OryxEnergy Co. in Dallas, Texas, USA, as US Acquisitionsand Trade coordinator. His experience there includedcorporate and operational planning, serving as man-ager of strategic and operational planning, and thenas director of worldwide marketing, trading and trans-portation. He also served as Oryx director of commer-cial opportunities, planning and marketing. In 1999he joined Kerr-McGee to direct portfolio managementand planning. The following year, he became managerof corporate transformation and assumed his currentposition. Tom is a registered professional engineerand has a BS degree in petroleum engineering fromThe University of Texas at Austin, and an MBA degreefrom Southern Methodist University in Dallas, Texas.
Mahmood Akbar, who is based in Abu Dhabi, UnitedArab Emirates (UAE), has been a division geologistwith Schlumberger since 1992. He is involved in fieldstudies and in interpreting and solving problems inIran, UAE and Yemen. After joining the company in1985, he was based in Islamabad as a district geologistfor Pakistan and later in Oman. While in Pakistan, heworked as a petrophysicist in complex formations inaddition to his geologic responsibilities. Mahmoodreceived BS and MS degrees in applied geology fromthe Institute of Geology, University of the Punjab,Lahore, Pakistan.
Jack A. Albers, Portfolio & Planning Manager forBurlington Resources, Houston International Division,is responsible for all portfolio and planning activitiesin the division. He also assists the CorporateExploration Decision Board with management of thecorporate exploration portfolio. He started withAmoco Production Company in 1978 as a reservoirand drilling engineer for the US Gulf Coast. In 1981 hemoved to Texas International Petroleum Company inOklahoma City, Oklahoma, as staff reservoir engineerfor the midcontinent. One year later, he became chiefreservoir engineer for Funk Exploration, Inc., respon-sible for reserve estimation and economic analysis ofdrilling projects. In 1985 he started a reservoir engi-neering consulting firm, specializing in reservoirstudies and economic evaluations. After two years, hejoined Louisiana Land & Exploration as senior petro-leum engineer, responsible for reserves and economicanalysis of Anadarko basin properties. From 1989 to1991, he was with Ramco Oil & Gas, Inc. in Tulsa,Oklahoma, as engineering manager of acquisitions. Hespent the next six years in engineering and staff posi-tions with Louisiana Land & Exploration in NewOrleans, Louisiana, USA. In 1997 he joined BurlingtonResources in Houston as engineering advisor, respon-sible for economic analysis of all wildcat explorationprospects in South Louisiana, and all strategic plan-ning and budgeting for the division. Since assuminghis current post in 1999, he has been a member of theteam responsible for implementing updated economicanalysis and modern portfolio management tech-niques throughout the company. Jack has a BS degreein chemical engineering from Ohio State University inColumbus, USA.
Ali H. Alghamdi currently heads the Saudi AramcoReservoir Description division in Dhahran, SaudiArabia. He worked in the Well Testing unit beforebecoming the head of the Gas and ExplorationPetrophysical unit. He began his career as a juniorfield engineer for Schlumberger Wireline & Testing in1984. After training in Brunei, he worked as a loggingengineer in Malaysia, Abu Dhabi, Saudi Arabia, TheNetherlands, and finally in Aberdeen, Scotland. From1992 to 1993, he was recruiting and training managerfor the Middle East region. He then joined SaudiAramco, and from 1994 to 1996, was Saudi Aramcooperations manager in charge of openhole, cased holeand testing operations in Saudi Arabia. Ali obtained aBS degree in petroleum engineering from King FahdUniversity of Petroleum and Minerals in Dhahran.
David Allen is a petrophysics advisor at Schlumberger-Doll Research, Ridgefield, Connecticut, USA, wherehe leads the research effort in carbonate case studies.After receiving a BS degree in physics and a BA degreein economics from Beloit College in Wisconsin, USA,he joined Schlumberger as a field engineer in 1979.From 1995 to 1997, David was the chief petrophysicistfor Schlumberger Wireline & Testing. He received BestPaper awards from the SPWLA for a 1987 paper oninvasion and a 1997 paper on resistivity anisotropy.
Ali O. Al-Qarni, Team Leader of the Gas IntegratedTeam at Saudi Aramco, is based in Udhailiyah, SaudiArabia. He is responsible for all upstream activitiesfor the Hawiyah Jauf project and also for a numberof other gas plants in the Udhailiyah area. He beganhis career with Saudi Aramco in 1987 as a produc-tion engineer in Abqaiq, Saudi Arabia. Since then hehas been a workover engineer, a reservoir engineerand a production engineer overseeing surface facili-ties. Ali is a graduate of the University of SouthernCalifornia in Los Angeles, USA, with a BS degree inpetroleum engineering.
Brian Ault is an operations manager with UltraPetroleum, Inc. in Englewood, Colorado, USA. Beforejoining Ultra in October 1997, he spent five years withThe Western Company of North America, five yearswith Meridian Oil/Burlington Resources and two yearsas a consultant on well completions and productionoptimization. He has worked in the Rocky Mountainregion, San Juan basin, Texas and Oklahoma. Duringhis 15 years in the industry, he has held positions incorporate planning, reservoir management, drilling,completion, production, regulatory permitting andreporting. Brian has a BS degree in petroleum engi-neering from Marietta College in Ohio.
Michael Back is business leader for the CapitalPlanning* program with the Value Management groupfor Merak projects in Calgary, Alberta, Canada. Hismain duties include leading and managing softwaredevelopment, project planning, training and imple-mentation of Capital Planning software with clients,and working with the Merak marketing group todefine key marketing initiatives for the CapitalPlanning program. He joined Merak in 1998 afterthree years at Imperial Oil (Exxon). Initially, he waswith the software support and consulting group and
spent a year doing product support, training and con-sulting with clients. He then moved to the ValueManagement software development team in a qualityassurance role and worked with the Capital Planningteam to ensure the highest product quality for theproduct launch in June 2000. He assumed his currentposition in October 2000. Michael earned a BS degreein mechanical engineering from McGill University inMontreal, Quebec, Canada, and an MS degree in engi-neering (advanced technology management) fromUniversity of British Columbia in Vancouver, Canada.
Andrew Carnegie is a reservoir engineer at the Oil &Natural Gas Corporation, Ltd. (ONGC)-SchlumbergerJoint Research Center (JRC) in New Delhi, India.Since joining Schlumberger in 1989, he has workedfor both Wireline & Testing and GeoQuest, in severalsubdisciplines of petroleum engineering and reser-voir characterization, with assignments in the FarEast, Middle East and Australia. Before joiningSchlumberger, he worked for Cap Scientific (1982 to1985) as a mathematician specializing in torpedoand submarine hull design. He also worked for Intera(1985 to 1989) as a reservoir engineer. Author ofmany papers, Andrew earned a BS degree (Hons) inapplied mathematics, and a PhD degree in mathe-matical physics, both from Queen Mary College,University of London, England.
R. D. Chourasiya Chief Geophysicist (Wells), for Oil& Natural Gas Corporation, Ltd. (ONGC), is based inMumbai, India. He is currently working in the BombayHigh field and is involved in the preparation andimplementation of the Bombay High North redevelop-ment scheme. He joined ONGC in 1979 as a juniorgeophysicist. He has had assignments in Bombay(1980 to 1989) and in Assam (1989 to 1993). He hasworked extensively as a log analyst in the limestoneand sandstone reservoirs of western offshore andnortheastern areas of India. He holds an MS degreein physics from University of Saugar, Sagar, MadhyaPradesh, India.
Ellen Coopersmith is founder and president ofDecision Frameworks in Houston, Texas. She workswith companies to build decision capability throughdecision-analysis training. Ellen worked at Conoco Inc.for 16 years, where she held numerous technical andsupervisory positions in the E&P department beforetaking on the directorship of decision analysis in thecompany. She earned a BS degree in petroleum engi-neering from Colorado School of Mines in Golden,and is a member of SPE and the Decision AnalysisAffinity Group.
Graham Dean is a petroleum engineer who works onupstream acquisitions, strategy and planning forCentrica plc, a major gas producer and supplier inEngland. Prior to his three years with Centrica, heworked for Amerada Hess, Britoil and Schlumberger.Graham received an engineering degree at theUniversity of Cambridge in England. He is a former vicepresident of the London Petrophysical Society andholds a patent to measure and use sea and earth tidesto monitor compaction and subsidence of oil fields.
Contributors
Winter 2000/2001 61
Stan Denoo joined Schlumberger in 1971 after gradu-ating from the University of Wyoming in Laramie,USA, with a degree in mechanical engineering. Heworked as a field engineer in the western US, a syner-getic engineer in the New Orleans, Louisiana comput-ing center and a sales engineer in Oklahoma. He wasalso a member of the interpretation development staffin Houston, and served with the product developmentgroup in Denver, Colorado. Now based in Englewood,Colorado, Stan is currently lead petrophysicist for theSchlumberger Western States division.
Dhruba Dutta, is a petrophysicist at the Oil & NaturalGas Corporation, Ltd. (ONGC)-Schlumberger JointResearch Center in New Delhi, India. There he isresponsible for processing, support, interpretationand development of in-house software for improvedevaluation in carbonates as well as clastic rocks. Aftercompleting a PhD degree in 1993, he was a researchassociate at the Department of Geology and Geophysics,Indian Institute of Technology, Kharagpur, where hewas involved in research in two-dimensional DC electri-cal resistivity modeling and inversion. He has beenwith Schlumberger since 1997. Dhruba earned a BSdegree (Hons) in geology from the University ofCalcutta, India; an MS degree in applied geophysicsfrom the Indian School of Mines, Dhanbad, India; anda PhD degree in borehole geophysics at the IndianInstitute of Technology in Kharagpur.
David Fairhurst, Sales Development Engineer basedin San Antonio, Texas, is responsible for sales ofSchlumberger formation evaluation and productionservices, such as the CMR* Combinable MagneticResonance tool, in south Texas. Previously, he was incharge of sales of Schlumberger production servicesin south Texas. He joined the company as a productionservices field engineer in Evanston, Wyoming, aftergraduation from the University of Minnesota atMinneapolis-St. Paul, USA, with a BS degree in elec-trical engineering. David also has an MBA degreefrom the University of Pittsburgh, Pennsylvania, USA.
Roger Heckman is a reservoir engineer for UltraPetroleum, Inc. in Englewood, Colorado. In this posi-tion, he is responsible for reserve estimates, produc-tion forecasts, budget preparation, prospectassessments, formation evaluation, computer model-ing and economic analysis. He began his career in1972 as a field engineer for Schlumberger afterreceiving a BS degree in petroleum engineering fromthe University of Kansas in Lawrence, USA. Roger hasdiverse technical, supervisory and management expe-rience in all aspects of reservoir engineering, includ-ing reserve reports for partnerships, independentreserve consultants and financial institutions, fielddevelopment studies, operator and investor due-diligence, acquisition and divestiture, workover andproduction enhancement.
Michael Herron is a scientific advisor working onapplications of geochemical and statistical methodsfor reservoir interpretation problems at Schlumberger-Doll Research, Ridgefield, Connecticut. Prior to join-ing Schlumberger in 1982, he studied the chemicalstratigraphy of polar ice cores as part of his doctoralwork at the State University of New York, Buffalo,USA, where he received a PhD degree in geologicalsciences. Mike also has a BA degree in chemistry fromthe University of California in San Diego.
John I. Howell III is the Founder and President ofPortfolio Decisions, Inc. (PDI), a consulting practicedesigned to help oil and gas companies improvebusiness performance by balancing risk, profit andgrowth. PDI works with senior decision-makers andtechnical staff, helping them understand their rolein the portfolio-management process. After earningBS and MS degrees from Stanford University inCalifornia, John held technical, supervisory and man-agerial positions in exploration and production duringhis 21 years with Shell Oil. He developed and imple-mented portfolio management techniques for E&Pbusinesses, and for the last 18 years, he has beenprimarily involved in quantitative decision making,strategic planning and change management. An activemember of the SEG and SPE, he has lectured on port-folio management as a strategic tool at both domesticand international conferences. He has also managedthe Lamont Portfolio Management Consortium forEnergy Companies, a collaborative effort betweenColumbia University and J. I. Howell & Co. that intro-duced new business technologies to planning depart-ments and operating divisions of domestic andinternational energy companies.
Bruce Kaiser is a Schlumberger alliance coordinator,working in-house with Conoco, Inc. in Houston,Texas. He coordinates PowerSTIM* projects and pro-vides technical support for asset teams developingthe Lobo trend in South Texas. Bruce started hiscareer with Schlumberger in 1979, as a field engineerin Sacramento, California. From 1981 to 1985, heworked as recruiting engineer and then as trainingcenter manager in Denver, Colorado. From 1985 to1986, he was a wireline district manager inCalifornia. As a borehole seismic specialist, Brucecoordinated the Western States division special wire-line services team from 1986 to 1990. He spent thenext six years coordinating introduction of thePlatform Express* logging platform in North Americaas field service manager in Bakersfield, California.From 1996 to 1997, he worked as evaluation servicestraining coordinator in Austin, Texas. Bruce receiveda BS degree in aerospace engineering from Tri-StateUniversity, Angola, Indiana, USA.
Dale Logan, who is based in Midland, Texas, is cur-rently Data & Consulting Services Manager for USLand-Central region. His main responsibilities includesetting direction for his group and marketing solu-tions initiatives for the Permian Basin and SouthTexas. Since joining the company in 1981, he has hadmany assignments in Texas, New Mexico and Canada,involving engineering, log analysis, sales engineeringand operations management. Before assuming hiscurrent position in 2000, he was the Wireline inter-pretation development manager for the Central Statesdivision in Midland, Texas. He has been involved innuclear magnetic resonance (NMR) since 1986 andhas written several papers on applications of NMRtechnology in formation evaluation. Dale has a BSdegree in electrical engineering from McGillUniversity in Montreal, Quebec, Canada.
F. Jerry Lucia is a senior research fellow at theBureau of Economic Geology, The University of Texasat Austin. His technical expertise includes origin anddistribution of carbonate strata, petrophysics andpetroleum geology. Before joining the Bureau in 1985,he was a consulting geological engineer for Shell OilCompany assigned to the head office staff. He retiredin 1985 after 31 years as a geological engineer withexperience in research and operations. He is currentlyworking on new techniques and methods for charac-terizing carbonate reservoirs to improve recovery fromexisting oil fields through the integration of geological,petrophysical, engineering and production data. Theproject areas include the Permian Basin and theMiddle East. A prolific author, Jerry earned a BSdegree in engineering and an MS degree in geology,both from the University of Minnesota at Minneapolis.
Jeffrey W. Lund, Vice President of Business Servicesfor Kerr-McGee Oil & Gas Co., Houston, Texas, isresponsible for managing the company’s portfolio ofassets, providing guidance on acquisitions and devel-opment of oil and gas properties worldwide, and man-aging technical development and research activities.Jeff began his career in 1969 with Amoco and joinedClark Oil Producing Co. six years later. He worked forSouthland Royalty Company as district geologist andregional exploration manager from 1978 to 1986,when Southland was acquired by BurlingtonResources. He was southern region exploration man-ager for Burlington until 1991. He joined AshlandExploration in 1991 advancing to vice president ofexploration and land. He joined Kerr-McGee in 1998in his current position. Recipient of many awardsfrom the Houston Geological Society, he served as thatsociety’s president from 1997 to1998. Currently, he ispresident of the Gulf Coast Association of GeologicalSocieties, and general chairman for the 2002American Association of Petroleum Geologists con-vention. He obtained a BS degree in geology fromCase Western Reserve University, Cleveland, Ohio;and an MS degree in geophysics and an MBA fromthe University of Houston, Texas.
Sam McClure is a petroleum engineer for UltraPetroleum, Inc. in Englewood, Colorado. Prior to join-ing Ultra in August 1998, he spent four months as anintern with Dowell Schlumberger. During his threeyears in the oil and gas business, he has worked inreservoir management, drilling, completion, produc-tion, and regulatory permitting and reporting. Samholds a BS degree in petroleum engineering from theUniversity of Wyoming in Laramie.
Alan C. McNally, who is based in Midland, Texas, isPermian Basin district engineering manager for LouisDreyfus Natural Gas Inc. He manages drilling, produc-tion engineering and personnel for one of the busiestonshore areas in North America, currently operatingthe three most active US drilling rigs based on footagedrilled per year. Prior to his eight-year tenure withLouis Dreyfus Natural Gas Inc., Alan served as techni-cal engineering manager for BJ Services, Inc. in thePermian Basin. He has a BS degree in mechanicalengineering from Texas Tech University in Lubbock.
62 Oilfield Review
Jason McVean, who is based in Calgary, Alberta,Canada, is project manager for Merak risk products,such as the Decision Tree* system, and coordinateswith the Capital Planning team. He joined Merak in1997. Jason earned BS and MS degrees in astrophysicsfrom University of Calgary.
Richard Netherwood is interpretation support geolo-gist for Schlumberger in Jakarta, Indonesia. Afterreceiving a BS degree (Hons) in geology from theRoyal School of Mines, Imperial College, London,England, in 1981, he joined BP in London as a con-tract geologist for the Far East Exploration group.He resigned in 1982 to attend University of Reading in England, researching rifting in the Gulf of Suez and mixed carbonate and clastic depositional systemsin the Miocene of the Gulf of Suez and southeastSpain. He was posted to Indonesia with Gearhart-GeoConsultants in 1986. There he completed a widerange of sedimentologic and stratigraphic reservoirand regional studies, mainly in Indonesia, but also in the UK, Morocco, Egypt, Malaysia, China, Japan, the Philippines and Australia, with Gearhart, CoreLaboratories Inc. and his own consulting group—P.T.Rocktech Sejahtera. In 1996 he joined SchlumbergerWireline & Testing in Jakarta as Indonesia area geolo-gist, responsible for client education in dipmeter andimaging tools, image-log interpretation and training of Indonesia national geologists in sedimentology andsequence stratigraphy using image logs and cores.Author of many papers, he is an active participant inthe Indonesian geological community, and is a mem-ber of the Indonesian Petroleum Association AnnualConvention Committee for 1999, 2000 and 2001.
Mark A. Norville, Vice President, Exploration andDevelopment, has been with Kerns Oil & Gas, Inc. inSan Antonio, Texas, since 1998. He works on develop-ing and exploring more than 20,000 acres in south andwest Texas, and reviews and evaluates projects forKerns’s participation. He began as a district geologistfor Clayton Williams Energy in San Antonio (1980 to1985) and spent the next 12 years as manager ofexploration for Stallion Oil Company, also in SanAntonio. He currently serves as president of the SouthTexas Geological Society. Mark holds a BS degree ingeology from Texas A&M University in College Station.
Jean-Rémy Olesen has been director of the Oil &Natural Gas Corporation, Ltd. (ONGC)-SchlumbergerJoint Research Center (JRC) in New Delhi, India,since 1998. He directs the activities of the center andis involved in all aspects of applied research there. Hiscurrent areas of interest are carbonate evaluation anddevelopment of new interpretation methods and soft-ware prototyping. Previously, he was interpretationdevelopment manager, Schlumberger China S.A.,based in Beijing, People’s Republic of China (1995 to1998). Jean-Rémy was graduated from the FederalInstitute of Technology, Lausanne, Switzerland, withan MS degree in electrical engineering. He joinedSchlumberger in 1974 as a field engineer. After numer-ous overseas field and staff assignments, he special-ized in petrophysics and spent part of his career at theSchlumberger Houston Engineering Center, involved inthe development of nuclear logging devices. He holdsseveral patents in the field of nuclear logging.
Lee Ramsey is currently on assignment toSchlumberger Middle East, in Al Khobar, Saudi Arabia.As PowerSTIM project coordinator, he heads both thetechnical and operation teams who are developingsolutions for stimulating and controlling sand produc-tion in the Jauf project. He began his career withDowell as a field engineer in 1974 in Williston, NorthDakota, USA, and has held various positions in opera-tions, engineering and marketing in the United Statesand Canada. He recently headed the PowerSTIM ini-tiative in North America as product champion, andwas nominated for the chairman’s “Performed bySchlumberger” award. Lee attended Kansas StateUniversity in Manhattan, where he received a BSdegree in geology.
Wayne Rowe, who is based in Englewood, Colorado,is currently production enhancement manager for the Schlumberger US Land-Western GeoMarket ofNorth and South America. He began his career withSchlumberger in 1981, as a field engineer in Duncan,Oklahoma, and Fort Morgan, Colorado (1981 to 1985).From 1985 to 1993, he was a sales engineer. He workedas an alliance manager from 1993 to1998. Wayne holdsa BS degree in civil engineering from the University ofColorado in Boulder.
S. Duffy Russell is a senior production geologist withExxonMobil Production Company in Houston, Texas,where he works on the development of carbonatereservoirs in West Texas. He began his career in 1979as a geophysicist with Amoco Production Company inNew Orleans, Louisiana. There he was responsible forexploration prospect identification in the Gulf ofMexico. In 1981 he joined Mobil Oil Corporation as aproduction geologist, responsible for the developmentof offshore fields. He held various technical and super-visory positions in wellsite operations and exploration,and in 1989 began work on new international venturestudies of the Middle East, Russia, and the NorthwestShelf of Australia. From 1992 to 2000, he worked as asenior reservoir geologist with Abu Dhabi Company forOnshore Oil Operations (ADCO) in Abu Dhabi, UAE.His recent work has been focused on outcrop-, core-and log-based studies of geological heterogeneity andreservoir characterization as applied to 3D modelingof carbonate reservoirs. Duffy has a BS degree in geol-ogy from North Carolina State University in Raleigh;and an MS degree in geology from Duke University in Durham, North Carolina, USA. He has recently completed research for a PhD degree in carbonatesedimentology at the University of Aberdeen inScotland. He is a recipient of a 1970 National ScienceFoundation award and a member of Sigma XiScientific Research Society.
Kamlesh Saxena, Interpretation DevelopmentGeologist for Schlumberger in Mumbai, India, isresponsible for job planning, log quality control, andinterpretation development and marketing ofFormation MicroScanner* and ECS* ElementalCapture Spectrometry tools. He is also involved inplanning and execution of single and multiwell studiesand is coordinating the ONGC-Schlumberger CarbonatePetrophysical study. He joined Schlumberger in 1983as a geologist at the Kuala Lumpur Log InterpretationCenter in Malaysia. The following year he became
senior geologist at the India Log Interpretation Centerin New Delhi. From 1992 to 1994, he was countrymanager for GeoQuest Data Services in Ankara,Turkey. Before assuming his current position, he wasdivision geologist for GeoQuest in Abu Dhabi, UAE(1994 to 1999). Kamlesh earned a BS degree in geol-ogy, geography and chemistry from Osmania University,Hyderabad, Andhra Pradesh, India; and a Master ofTechnology degree in applied geology from Universityof Saugar, Sagar, Madhya Pradesh, India. Among hisaccomplishments is starting data services centers inNew Delhi and in Ankara.
Milton R. Seim, Vice President of Operations for KernsOil & Gas, Inc., is responsible for all the company’sengineering personnel and operational functions. He isalso vice president of Diamondback Drilling, MesquiteWell Service and Kerns Development Company. Hebegan his career with Mobil Oil Corp. in 1970, workingin production and drilling engineering. From 1979 to1993, he was division production manager for ForestOil Corp. in Denver, Colorado, and in Corpus Christiand Midland, Texas. He joined Kerns in 1995. Miltonhas a BS degree in natural gas engineering from TexasA&I University in Kingsville.
David Stief, Solutions Manager for Schlumberger USLand-Central, is based in Midland, Texas. He joinedthe company in 1979 as a wireline field engineer. Sincethen he has worked in sales, interpretation develop-ment and data and consulting services in various northand West Texas locations. Dave received a BS degree inmechanical engineering from the University ofMissouri at Rolla, USA.
Erling Storaune is marketing director, DeepwaterSolutions for Aker Maritime, Inc. in Houston, Texas.He joined Aker in 1980 and has worked mainly in engi-neering and project construction. He was engineeringmanager and project manager for offshore projects inNorway. He also served as project manager at AkerGulf Marine in Corpus Christi, Texas, from 1991 to1995. Before assuming his current post, he was execu-tive VP at Spars International in Houston (1995 to2000). Erling has an MS degree in mechanical engi-neering from the Norwegian Institute of Technologyin Trondheim.
Badarinadh Vissapragada, a senior petrophysicistwith Schlumberger Data & Consulting Services in theGulf GeoMarket, is based in Abu Dhabi, UAE. He isinvolved in a petrophysical study of the Shuaibareservoir of the Abu Dhabi Company for OnshoreOperations (ADCO). After completing postgraduatestudy in geophysics, he began his career in 1992 withthe Oil & Natural Gas Corporation (ONGC). He workedfor 13 years for ONGC at Mumbai offshore, Assam(Nazira) and in the western region (Baroda). In 1995he joined the Oil and Gas division of Reliance Industriesof India at Mumbai and worked for two years in theReliance-Enron joint-venture project, in the Panna-Mukta and Tapti oil fields. Since joining Schlumbergerin 1997, he has completed many field-wide petrophysi-cal studies for ADCO. Badarinadh obtained an MSdegree in geophysics, and an MS technology degreefrom Andhra University, Waltair, Andhra Pradesh, India.
Winter 2000/2001 63
An asterisk (*) is used to denote a mark of Schlumberger.
Handbook of Petrochemicals and Processes, 2nd Ed.G. Margaret WellsAshgate Publishing CompanyOld Post RoadBrookfield, Vermont 05036 USA1999. 494 pages. $160.00 ISBN 0-566-08046-X
The book provides brief descriptions ofthe processes used to manufacture 76petrochemicals, their properties, thegrades available and their applications.Also included is information abouthealth and handling and major licensors.
Contents:
• Acetaldehyde; Acetic Acid; AceticAnhydride; Acetone; Acetylene;Acrolein; Acrylic Acid; Acrylonitrile;Acrylonitrile-butadiene-styrene(ABS) Resins; Adipic Acid; Ammonia;Aniline; Benzene; Benzoic Acid; Ben-zyl Chloride; Bisphenol A; Butadiene;Butyl Acetate; Butyl Alcohol; Capro-lactam; Carbon Tectrachloride;Chlorobenzene; Chloroform; Cumene;Cyclohexane; Cyclohexanol & Cyclohexanone; Epichlorohydrin;Ethanolamines; Ethyl Acetate; EthylAlcohol; Ethylbenzene; Ethyl Chlo-ride; Ethylene; Ethylene Dichloride(EDC); Ethylene Glycol; EthyleneOxide; Ethyl Ether; 2-Ethyl HexylAlcohol; Formaldehyde; Formic Acid;Glycerol; Hexamethylenediamine(HMDA); Isopropyl Alcohol (IPA);Maleic Anhydride; Methy Alcohol;Methylamines; Methyl Chloride;Methylene Dichloride; Methyl EthylKetone (MEK); Methyl Isobutyl Ketone(MIBK); Methyl Methacrylate (MMA);Methyl Tert-Butyl Ether (MTBE);Nitrobenzene; Perchloroethylene; Phe-nol; Phthalic Anhydride; PolyethyleneHigh Density (HDPE) & PolyethyleneLinear Low Density (LLDPE);Polyethylene Low Density (LDPE);Polypropylene (PP); Polystyrene &Expanded Polystyrene; Polyvinyl
Chloride (PVC); Propylene; PropyleneGlycol; Propylene Oxide; Styrene;Terephthalic Acid (TPA) & DimethylTerephthalate (DMT); Toluene; 2,4-Tolylene Diisocyanate (TDI) &Diphenylmethane Diisocyanate(MDI); Trichloroethylene (TCE);Urea; Vinyl Acetate; Vinyl Chloride(VCM); Xylene
• Transportation of Dangerous Goods
• Transportation
• Health and Safety
• Other Organizations
• Indexes
The book is well written.
It is a well-organized, easy-to-usehandbook and is so recommended toanyone needing ready access to infor-mation on petrochemical processes.
Larsen JW: Energy & Fuels 14, no. 2 (March/April
2000): 517.
Groundwater in Geologic ProcessesSteven E. Ingebritsen and Ward E. SanfordCambridge University Press40 West 20th StreetNew York, New York 10011 USA1999. 341 pages. $32.95ISBN 0-521-66400-4
This book describes the importance ofsubsurface water and other fluids inmany geologic processes such as forma-tion of hydrocarbon reservoirs and oredeposits. Its goal is to combine physicaland mathematical theory with practicalexamples and real-world data.
Contents:
• Groundwater Flow
• Solute Transport
• Heat Transport
• Regional-Scale Flow and Transport
• Ore Deposits
• Hydrocarbons
• Geothermal Processes
• Earthquakes
• Evaporites
• Diagenesis and Metamorphism
• References, Index
The authors...have done an excel-lent job in describing the basic physicsof fluid in the earth and how to applythis knowledge to gain deeper under-standing of geologic phenomena. Indoing so, they have produced a bookthat should be a priority for any geosci-entist’s library.
...there is the pleasure of reading ahigh-level text that doesn’t skimp oncomplex details but still is descriptive,interesting, and easy to follow.
Green WR: The Leading Edge 19, no. 8 (April 2000):
912-913.
Unsteady-State Fluid FlowE.J. HoffmanElsevier Science B.V.Sara Burgerhartstraat 25P.O. Box 211100 AE Amsterdam, The Netherlands1999. 473 pages. $266.50ISBN 0-444-50184-3
In addition to introductory material onpetroleum-bearing formations andreservoirs, the book outlines empiricalmethods for correlating and predictingunsteady-state fluid behavior. Alsoincluded is a more theoretical presenta-tion based on classical partial differentialequations for flow through porous media.
64 Oilfield Review
NEW BOOKSComing in Oilfield Review
Improving the Virtual Reservoir.Reservoir simulation technology iskeeping pace with improvements indrilling and production capabilities,while the interface with the user has become simpler. Case studiesillustrate how advanced simulatorsare handling realistic wells andcomplex hydrocarbon fluid com-positions. A fast flow-streamsimulator tracks fluid-flow paths in the reservoir.
Building a Knowledge-SharingCulture. Today, E&P companies arecataloging best practices and lessonslearned in knowledge repositoriesfor access by technical employees.These initiatives are designed toextract the most added value fromthe massive amounts of data andinformation available. This articledescribes the steps necessary tocreate and sustain an oilfieldknowledge-sharing culture thatimproves organizational productivityand efficiency.
Knowledge-ManagementRoundtable. Oil and gas companieshave diverse approaches to buildingknowledge-management infras-tructures and cultures. For this article,we assembled experts from six E&Pcompanies who discuss their experi-ences in establishing knowledge-management programs, what hasbeen learned to date and what thefuture may hold.
Resistivity Behind Casing. Sixdecades after its first description,this long sought-after measurementis now a reality. This article discussesthe history and development of thestate-of-the-art wireline loggingdevice that completes the suite ofcased-hole formation-evaluationmeasurements. Examples illustratethe value of cased-hole resistivityfor identifying bypassed pay and forproduction and reservoir monitoring.
Contents:
• Petroleum Reserves and Their Estimation
• Pressure/Production Behavior Patterns
• Pressure/Production Decline Correlations
• Concepts of Flow
• The Classic Differential Equations for Flow Through Porous Media
• Integral Forms for DescribingUnsteady-State Flow
• Two-Phase and Multiphase Flow:Gas, Oil, and Water
• Steady-State: Productivity Tests
• An Evaluation of Unsteady-State Solutions for Drawdown and Transition
• Gaseous Unsteady-State Radial FlowBehavior from the Calculated Resultsof Bruce et Al.
• A Critique of Boundary Conditions,Degrees of Freedom and Darcy’s Law
• The Results of Bruce et Al in Terms ofIntegral Forms
• The Computation of Reserves and Permeability from Stabilized Flow-Test Information
• Approximate Solutions During Drawdown and Long-Term Depletion
• Representation of Water Drives
• Production-Decline Behavior
• Afterword
• Glossary, Symbols, Index
[The book] provides empirical andclassical methods for correlating andpredicting the unsteady-state behaviourof petroleum reservoirs...and analysisof unsteady-state behaviour....
...[the book] provides simplifica-tion based on successive steady-stateprofiles which permit application to thedepletion of both closed...and openreservoirs, and serves to distinguishdrawdown, transition and long-termdepletion.
Barfoot L: Journal of Canadian Petroleum Tech-
nology 39, no. 4 (April 2000): 22-23.
Introduction to SeismologyPeter M. ShearerCambridge University Press40 West 20th StreetNew York, New York 10011 USA1999. 260 pages. $74.95ISBN 0-521-66023-8
In this concise introduction to the theory of seismology, each chapter inthe book outlines the basic concepts,supplemented by student exercises and problems.
Contents:
• Introduction
• Stress and Strain
• The Seismic Wave Equation
• Ray Theory: Travel Times
• Inversion of Travel Time Data
• Ray Theory: Amplitude and Phase
• Reflection Seismology
• Surface Waves
• Source Theory
• Earthquake Prediction
• Miscellanea
• Appendices, References, Index
Illustrations are particularlyclean and well designed.
...it successfully achieves theauthor’s goal of a book designed specifi-ally for upper-division undergraduatesand first-year graduate students.
Pollack HN: Choice 37, no. 8 (April 2000): 1500.
Applied Geothermics for Petroleum EngineersI. M. KutasovElsevier Science B.V.Sara Burgerhartstraat 25P.O. Box 211100 AE Amsterdam, The Netherlands1999. 347 pages. $142.00ISBN 0-444-82887-7
This text presents methods of utilizingdata from temperature surveys in deepboreholes as well as results of field, labo-ratory and analytical investigations ingeothermics for use by petroleum reser-voir engineers, drilling and productionengineers, geologists and geophysicists.
Contents:
• Introduction
• Temperature Field of Reservoirs
• Wellbore and Formations TemperatureDuring Drilling
• Wellbore and Formations TemperatureDuring Shut-In
• Cementing of Casing
• Production and Injection Wells
• Interpretation and Utilization of Tem-perature Data
• Appendices, References, Index
...An excellent review of the varioususes of temperature in the design ofgeologic and petroleum engineeringprojects. The book is also a good sourceof data on thermal characteristics ofrocks and design of drilling and com-pletion operations.
Robertson JO Jr and Chilingar GV: Journal of
Petroleum Science & Engineering 28, nos. 1-2
(October 2000): 83-84.
Advanced Process Control andInformation Systems for the Process IndustriesLes A. Kane (ed)Gulf Publishing CompanyP.O. Box 2608Houston, Texas 77252 USA1999. 336 pages. $75.00ISBN 0-88415-239-1
Intended as a practical guide to improv-ing process control and information sys-tems in the process industries, the bookprovides a collection of case histories,techniques and guidelines that havebeen proven in industrial installations.
Contents:
• Project Justification and Implementation
• Model-Based Control and Optimization
• Information Systems
• Frontline Control
• Index
A thorough understanding of theprocess is key to maximizing the benfitsof modern control and informationsystems, and [the book] presents con-siderable information on applyingthese technologies to specific processes.
The chapters...adhere to the...policyof publishing only practical, non-commercial, problem-solving articlesfor technology users. The authors aretypically well-known authorities intheir fields.
Barfoot L: Journal of Canadian Petroleum Tech-
nology 39, no. 4 (April 2000): 22.
65Winter 2000/2001
ARTICLES
From Reservoir Specifics toStimulation SolutionsAl-Qarni AG, Ault B, Denoo S, Fairhurst D, Heckman R, Kaiser B,Logan D, McClure S, McNally AC,Norville MA, Ramsey L, Rowe W and Seim MR.Vol. 12, no. 4 (Winter 2000/2001): 42-60.
Growing Interest in Gas HydratesCollett TS, Lewis R and Uchida T.Vol. 12, no. 2 (Summer 2000): 42-57.
The Little Pumper That CouldBraun B, Foda S, Kohli H, Landon I,Martin J and Waddell D.Vol. 12, no. 2 (Summer 2000): 18-29.
Making Decisions in the Oiland Gas IndustryCoopersmith E, Dean G, McVean J and Storaune E.Vol. 12, no. 4 (Winter 2000/2001): 2-9.
New Directions in RotarySteerable DrillingDownton G, Hendricks A, Klausen TSand Pafitis D.Vol. 12, no. 1 (Spring 2000): 18-29.
The Next Step in Collaborative TrainingBowman C, Cotton WB, Gunter G,Johnson JD, Millheim K, North B,Smart B and Tuedor F.Vol. 12, no. 2 (Summer 2000): 30-41.
On the Cutting EdgeBesson A, Burr B, Dillard S, Drake E,Ivie B, Ivie C, Smith R and Watson G.Vol. 12, no. 3 (Autumn 2000): 36-57.
Perforating Practices ThatOptimize ProductivityBehrmann L, Brooks JE, Brown A, Farrant S, Fayard A, Michel C, Noordermeer A, Smith P, Underdown D and Venkitaraman A.Vol. 12, no. 1 (Spring 2000): 52-74.
Portfolio Management forStrategic GrowthAdams T, Albers JA, Back M, Howell JI, Lund J and McVean J.Vol. 12, no. 4 (Winter 2000/2001): 10-19.
Real-Time LWD: Logging for DrillingBargach S, Bornemann T, Codazzi D,Falconer I, Ford G, Grether B, Hartner J, Hodenfield K, Maeso C,Plumb R, Rasmus J and Rohler H.Vol. 12, no. 3 (Autumn 2000): 58-78.
Seismicity in the Oil FieldAdushkin VV, Rodionov VN, Turuntaev S and Yudin AE.Vol. 12, no. 2 (Summer 2000): 2-17.
A Snapshot of CarbonateReservoir EvaluationAkbar M, Alghamdi AH, Allen D,Carnegie A, Chourasiya RD, Dutta D,Herron M, Logan D, Netherwood R,Olesen J-R, Russell SD, Saxena K,Stief D and Vissapragada B. Vol. 12, no. 4 (Winter 2000/2001): 20-41.
Solving Deepwater Well-Construction ProblemsCuvillier G, Denyer G, Edwards S,Johnson G, Plumb D, Mendonça JE,Sayers C, Theuveny B and Vise C.Vol. 12, no. 1 (Spring 2000): 2-17.
Taking a Calculated RiskBailey W, Couët B, Lamb F, Rose P and Simpson G.Vol. 12, no. 3 (Autumn 2000): 20-35.
Trends in NMR LoggingAllen D, Bedford J, Castelijns K,Fairhurst D, Flaum C, Gubelin G,Heaton N, Minh CC, Norville MA,Pritchard T, Ramakrishnan TS,Ramamoorthy R and Seim MR.Vol. 12, no. 3 (Autumn 2000): 2-19.
Water ControlBailey B, Crabtree M, Elphick J,Kuchuk F, Romano C, Roodhardt L and Tyrie J.Vol. 12, no. 1 (Spring 2000): 30-51.
NEW BOOKS
Advanced Process Control andInformation Systems for theProcess IndustriesKane LA (ed).Vol. 12, no. 4 (Winter 2000/2001): 65.
Applied Geothermics forPetroleum EngineersKutasov IM.Vol. 12, no. 4 (Winter 2000/2001): 65.
Biostratigraphy in Productionand Development Geology,Geological Society SpecialPublication No. 152Jones RW and Simmons MD (eds).Vol. 12, no. 3 (Autumn 2000): 82.
Cenozoic Foreland Basins ofWestern Europe, GeologicalSociety Special Publication No. 134 Mascle A, Puigdefàbregas C, Luterbacher HP and Fernàndez M (eds).Vol. 12, no. 1 (Spring 2000): 78.
The Deep Hot Biosphere Gold T.Vol. 12, no. 2 (Summer 2000): 60.
Dynamics and Methods ofStudy of Sedimentary Basins Majithia M (ed).Vol. 12, no. 2 (Summer 2000): 60.
Elastic Waves in RandomMedia: Fundamentals of Seismic Stratigraphic FilteringShapiro SA and Hubral P. Vol. 12, no. 2 (Summer 2000): 60.
Groundwater in Geologic ProcessesIngebritsen SE and Sanford WE.Vol. 12, no. 4 (Winter 2000/2001): 64.
Handbook of Petrochemicalsand Processes, 2nd ed.Wells, GM.Vol. 12, no. 4 (Winter 2000/2001): 64.
Introduction to SeismologyShearer PM.Vol. 12, no. 4 (Winter 2000/2001): 65.
Strategies for OptimizingPetroleum ExplorationKnoring LD, Chilingar GV and Gorfunkel MV.Vol. 12, no. 3 (Autumn 2000): 82.
Time Machines: ScientificExplorations in Deep Time Ward PD.Vol. 12, no. 1 (Spring 2000): 78.
Unsteady-State Fluid FlowHoffman EJ.Vol. 12, no. 4 (Winter 2000/2001): 64.
66 Oilfield Review
Oilfield Review Annual Index—Volume 12
