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Societyof PetroleumEnglnaers

SPE 27563

STORM: Integrated 3D Stochastic. Reservoir Modeling Tool forGeologists and Reservoir.. EngineersR.B. 3ratvold, ODIN Reservoir Software & Services As Lars Holden, Norwegian ComputingCenteC and Tarald Svanes and KellyTyler, Statoil A/S

SPE Members

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ABSTRACT

The petroleum industry is presently fwusing onimproved reservoir characterisation. Decisiorisconcerning development rmd depletion ofhydrocarbon reservoirs must be taken consideringthe uncertainties of the formation “involved. Thisrequires that geologi@ snd engineering dita arecombined to develop a detailed reservoir model.

Ge&tatiSti6s arid stochastic modeling techniqueshave emerged as. promising, approaches forintegrating, all relevant infognation ~agd describingheterogeneous reservoirs.” “By” “using ‘stochastic”techniques to generste a range of ‘“equiprobablereservoir descriptions the rmcertsinty in theimportant reservoir par~eters can be quantified.This qusntitiesti6ii~ tbgether with the enhancedunderstanding of tbe rese”tioir cha~cteristics givenby stochastic reservoir modeling and visualization,provides an essential basis for making informed fielddevelopment decisions.. ““

Until now no widely accepted software system forstochastic rese-rvoir characterisation bas beenavailable. This paper presents a software system,

‘References and iflnstrations at end of paper

STORM, which integrates the different data sourceswith a stochastic approach for reservoir description.

INTRODUCTION

As a result of high costs only a minimum ofexploration and appraisal wells can be justifiedbefore tiportsnt field development dscisions aremade. The use of oversimplified geological modelsbased on data from a limited number of widelyspaced wells is probsbly one of the most importantreasons for the failures in predicting fieldperformance. Ovemimplification and the use ofunrealistic geological models psrtly results from thepaucity of well data but also results from theimppropnate use of avsilsble data. Experience hasshown, for example, that linesr int@olation of

:petrophysiwd characteristics between wells someIci!orneters apsrt ususlly will not give a reafistii ‘:imsge of the heterogeneity required to predict fluidflow., To “~ve a realistic description of thi spatialvsriation, “We’resort to stochastic models sndsimulation. .A large n~ber of papers dkcussing,geostritistiii and stochastic modeling techniqueshave been published. Haldorsen and Damslethlpresent an excellent overview of the vsfue of;tochastic modeling as well as the methods suitablefor reservoir descfiptiori.” ‘-’ ““–

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The phenomem or variables that we normally MODELING APPROACHdescribe with stochastic models are those thatinfluence the amount, position, r,ccessibllity, and The reservoir description using STORM consists offluid flow through reservoirs. Thus, stochastic five atages which are conditioned to the knownmodeling or sirrnrfation in this context usually refera irrforrnatiorxto the g&eration o~synth+c geological arcbitec@reandlor property fields in one, two, or three ●

dimensions. “The different realizations honor allobsemationa and possess a number of other desirablereservoir/geological features hat provides animproved basis for recovery predktions. In iddhion,the uncertainty and risk associated with differentdevelopment options can be quantified better.

Evaluation of petrolernn reservoirs are performed at

~Y stages; .+$t.~e.exploration stage, the objectiveis to identi~ possible hydrocarbon accumulation forfirrther appraisal. At the appraisrd stage, the .objective of the evaluation is to determine whetherthe reservoir is ecmrotically depletable and irr thatcase, -define the development plan and recoverystrategy. At tie production stage, the evaluation isairried at considering various eirhanced recoveryschemes and optimize production. .

The software system STORM is a tool forevaluation at all of these stages. The main challengeconsists of integrating different “types of irrforrnat]onconcerning the reservoir under evaluation. Examplesare general geologic experience, outcropobservations, seismic data, well observations andproduction test data. Representations of thereservoir characteristics ‘in numerical form on acomputer is the result of this integration process.These can be used for predicting the hydrocarbonvolume irr place. Automatic procedures fortransferring the reservoir description into thedynamic reservoir sirindators which predictsproduction characteristics are irichrded. Predictedhydrocarbon volumes in place and productionprofiles with associated uncertainty measuresconstitutes the decision support information at theappraisal stage. As- discussed j: the Case Studysection of this paper, S“TORM ciir also be used atthe production stage for production historymatching. . .

Geome@”: The large scale properties” aremodeled which iuchrdes the horizons and zonalthicknesses. Ttii and velocities are simulatedusing correlated 2D Bayesiam updated GaussianRandom fields. The surfaces are thenintercomelated to reduce the uncertainties.Surfaces interpreted from high resolutionsequence stratigraphy (i.e. sequence bo~dariesand maximum flooding surfaces) are afsosimulated..

Farrlti A marked point process is used to modelfaufta which can not be interpreted ffom seismicdata. The transrnissib@ reduction due to fardtsis considered when acaliig the results to thesimulated grid.

Sedimentary properties: Facies tiditecture

vary according to *e sedirnentologicalenvironment, and therefore several techniquesare..icluded for sirrmlatiori of the sedimentaryfeatiues. The techriiqueiinchrde

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Markov Random fields for mosaic “”depositional deposits and large baniem,

Marked Pod processes for the flrrviallabyrinth deposits and smaller shalebarriers e.g., in shallow marine.environments.

Truncated .Gaussiarr function” forenvironments where facies types areordered.

Sequential Indicator simulation, a voxelbaaed geostatistical method for modelingfacies architecture.

Petrophysical properties: The petrophysicalproperties for each facies are simulated usingtransformed correlated Gaussian Random fieldscondhioned to the facies simulations.

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. Upscalirrg It is necessary to scale the highresolution of the reservoir description model tothe limited resolution of” tbe productionsimulation. Techniques influded solving the flowequation rnrinerically over the coarse gridrenormalization and mean averages.

The software is an inte@tiori. of the prototypesoftware” MOHERES,3.4 HOR120N5, HAvANAc,and BARESI developed by the NorwegianComputing “C&tei “@R) as tiell as major elementsfi’om Geostatisticil Software Library (GSLIB)7developed by Stanford University. Also included areutilities for exploratory data analysis such as thestatistical functions. necessary for the procei.sing ofthe input data, output data, and model crossvalidation. The sotlware is filly integrated with a 3Dvisualization tool used for presentation and qualitychecks of all stages of the reservoir description.

WHITECTURE .

Design Considerations

General

Improved reservoir description has been the focus ofthe petroleum industry for a long time. Geostatisti-&and stochastic modeling technkpres for reservoirevaluation has emerged as a promising approach, Alarge number of papers have been published irr thisarea over the past decade and a growing number ofoil companies are iirvestigiting the possibility ofusing these techniques.

Despite the significant effort in this area,geostatistics is gerieiallyconsidered to be difficult tounderstand and use. The geos~tistical software hastypically been developed by research institutes andhas not been commercial in the sense of beingefficient, robust, standardized, and easy to use.

The major goal in designing Wd developing STORMwas to make the techniques of geostatistics andstochastic modeling available as an everyday tool forthe geologists and rr&grv@ engineers. The iruerrtwas to make STORM a “practical” tool for thereservoir modelers. In line with this we have focused

on developing a “sedmentology” rather than an“algorithm” driven approach. An essential basis forbuilding a reservoir model is the geological model.The stocha@ technique chosen must be guided byclassic geologic principles.

.=Important design Wrrsiderations in developingSTORM were portability, flexibility, andextendibility. FigWe 1 shows an overview of thesystem architecture and how they relate to eachother. T@ system .:onsists of three rnsincomponents: (i)the graphical user interface (GUI),

(ii) the data m~deL ~d (iii) the kernel programswhich consists of geostatistical modules, statisticalmodufes and visualization modules. This design,..wtich is following the POSC specifications, ensirresmodularity, interchangeability, extendibility, and anefficient path for code evolution.

STORM is an object oriented application andconforms to OSF, ANSI, and POSC standards.

Data Model

The data model is perhaps the most importantelement in any application of a certain size that isdesigrredto be used, supported, and extended over aperiod of time. If the data model is not welldesigned, the flexibility, efficiericy and robustness ofthe application will suffer.

The data model in STORM is based on the POSC .“Epicentre Data iilodelg. Significant efforts weredevoted to adapt and extend the POSCspecifications to the “STORMrequirements.

STORM treats all input data, both reservoir specificand phenomenological, as objects; i.e., data likewells and facies are objects. that can be selected fromlists or as graphical objects. All input data isaccessible for evaluation, correlation, statisticalanalysis and viewing at all stages in STORM.

Graphical User Interface

The STORM graphical user interface (GUI) runsunder the OSF/Motif environment. The GUI isdesigned and implemented based on the OSF/Motif

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1.2 Style Guide9 wd the POSC” E & “P UserInterface Style Guide lo.

Recognizing-that not all users of STQRM Wbefamiliar wiih geostatistics” the GUI is simple andintuitive; it is-designed to guide the users through themodeling process. - .“”

The first step” in”deveIoping the.STORM &apFiciduser interface wasto’bufida prototype basedon theinput from “several geologists, reserwoir engineers,geostatisticiau$, @d computer experts. ““Theprototype was tested, reviewed and updated basedon the feedback from users in the oil companies.

Vkraiiiation

STORM includes ..both 2 arid 3 dimensionalvisualization “options. An impo’rtant”pafl” of””theexploratory data analysis is to generate 2-D plotslike histogr”ams,-scaJterplots and variograms. Thevariog~ amlysis is iiite~ctiv6~Snrd the user canchoose between a range of different varkigiaiij likeexponential, spherical, fracal, etc. The statisticalfunctions” can also be used .on the generatedrealizations. “. :.

3-D visrialization ti” STORM is handled by the”commercial visualization application Data eXplorerTM(Dx)ll. DX is included in STORM as a“mn-timelicense arid the user access the visualization optionsthrough the STORM user interface. A large range ofvisualization options’ ””ire avdlable to &e “usersirrchrdmg zooming, rotatimr, and multiple cr&ssections in any rwbitraryplane. DX runs under the X-W1ndows environment and use hardware or softwarerendering depending on the workstationconfiguration.

User Guidance and Help System

One of+the obstacles f6r geoIo@s &d rese.g’oirengineers in egteririg iirto the :world of-stochasticreservoir modeling” ia the requirement to specifistatistical parameters for the models. Typically, theseparameters do not have any “geo-comections’’..arrdrequire thorough understanding of the models andalgorithms.

“Some of the statistical parameters required havebeen “converted” orre-phrased into. terms that aremeanirrgfirl to geoscientists. Furthermore, STORMincludes agmp~cally based help system. llrisaflowsthe user to get a graphical explanation of the effectof each and every parameter that must be specified.

Before any stochastic model is executed, acorisistency and completeness check is performed.This prevents the model from breaking down or notconverge due to incorrect andor incomplete inputdata.

STORM also provides real time feedback during theexecution of the stochastic models. The feedbackconsists of plots and text which provides insight intothe convergence of the algorithm. lf the model hasproblems in conditioning to- any of the inputparameters, the executicm maybe halted by the userand restarted with improved parameter input andmodel.specification.

The help system in STORM is hypertext baaed.

Implementation

STORM is an object oriented system and isimplemented in CW. with the exception of some ofthe existing prototype software. These softwaremodul~ are implemented in FORTRAN 77 anchorANSI C.

METHODOLOGY

This section describes the methodology used irrSTORM. Olrly a brief overview of the methodologyis included and references are provided for moredetailed discussions of the methods arrdalgorithms.The presentation in this section refers t.o.Figure 2.

Base Information

Thqbaaeinfomri+tioq&ovides the foundation for thereservoir evaluation. It is information related to theparticular reservoir under evaluation. It consists ofphenomenological information ,which irrcludes the

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SPE 27563 ..R.B. Bratvol& L. Holden, T. Svanes, “imdK. Tyler 5

geological Wd@arrding” of the processes involved,general geologic experience and observations ofoutcrops and reservoirs with origin similar to. thereservoir under study. It also includes the veryimportant” reservoir specific” observations fromseisniic, well and production data.

Seisriiic””data is very important for describingphenomena above seismic resolution e.g., geologicalhorizons and large faults.in gener~ the value of theseismic information “fiircases”W;th iricreasing +isrnii”resolution.

Well data such as logs, cores, depth to horizons, andIithofacies information is used as conditioningparameters when generating the reservoirdescriptions. Some of the” datz e.g., depth tohorizons, can be. directly used in the conditioningprocess. However, often well data must usually beupscaled to the appropriate sripport volume; i.e., thereservoir description grid blocks, for modeling, ~sproblem of upscaling h~ been the focus of seve@papersl j.

Well test data and production history are dit%cult touse in fomial models. Pin” of the problem of usihgproduction data for conditioning pu@OS.4Sis.jhe .t.O

the lack of mriqueneis problem and”it is difficult tomatch the pressurdrate response to a specificreservoir. Some progress has been made in the useof well test data for cmrditioning purposes13 andSTORM contains a module which”uses simtiatedannealing for ~is purpose.

Outcrops may ~ve valuable qualitative informationabout parameters like average channel width,dkance between channels, fanlt properties andsmoothness, of horizons.

General geological knowledge provides import+t,rdbeit imprecise; infi”imation which can be used forprior d&ributions for the different model parametersin a Bayesian setting. The prior dk.ribntions are then.combined with the reservoir spccitic observationslike well and seismic data to obtain posteriordistributions. If measured data is”sparae, the generalgeological kgowledge is dominant and tieuncertainty h the par~eter is large.”This approach

rrMYbe used for all parameters; netigross ratio,

average saturations, permeabilities,, ““size.of flowunib, heightiwidth of channels, directiori of channelbelts, smoothness of surfaces, etc.

Exploratory Data Analysis

In any resezyokstudy it is of major importairtie tobecome thoroughly “familiar” with the input dataand the reservoir in question. Several studies”haveemphasized that preparing and understanding theinput data giv& new insights and that perhaps themajor part of any study should be spent on thisphase.

The Exploratory Data Analysis module in STORMcontairis statistical. tools for analysis andinterpretation of the input data. Significant effort hasbeen used on this module to obtain a comprehensiveand user friendly interface. ““

Individual objects, or a range ,of objects such as ‘Mlobjects connected with a given channel,” rrmy beidentified and selected through the graphical riserinterface. By genemting and looking at appropriatestatistical measures such as hktograms, scatterplots,variograrns, etc., the users &a~ ‘emphasize, confirm,’or dismiss’ qudiiative ideas about the rese~oir.Furthermore, the stochastic models require statisticalinput parameters which can be derived by @ng thetools in this module.

The variogram analysis and s~ection is interactiveand includes a range of different %r-iogrsms such asspherical, exponential, Gaussian, and fractalvariograms.

Reservoir Description

This consists of a set of tools for iitegiatipg the baseinformation. The itite~ation processes require thatthe aim of the study be kept clear in mind and thatthe various pieces of information are combined withrespect to their representability and precision. Themodules are

Structural Module

This module contains two sub-modules:

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~..This module focuses on the large scale geometry ofthe reservok capmm~ various horizons, major faultzones, formation thickness etc. As a general rule thelarge scale features_of thereseryir has.rnajor iiypacton the hydrocarbon-in-place and fluid flow responseof the reservoir. The module is based on seismicreflectiorr ffie”” maps and observatiori””of thegeome@cal sispec@ .@ the wells. In .?cklitkxrsubjective guesses ‘arjd trends” h [o~ationthicknesses and on seisriric velocities can be takeninto consideration.

In the module, a model for depth conversion in astochastic setting is detine:d, baied on a BayesianapprOach to Gaussian random fields [4-17.The ~~~euse two dimensional Gaussian rtidom fields both formodeling the time and the veIoci&es15.The surfacesfor the different”horizons “ire intercorrelated. Thisreduces the uncertainties and incorporates the- welldata in a consistent manner. Moreover, it allowsconsistent treatment of deviated wells.

Figure 3 shows s~,qlated ..isochore maps; i.e, ahorizon, generated by the geometric_ module. &shown on the tigiire, anisotropy &d sequence”stratigraphic iriterpretitions can be”included.

~., .. . . . . .Faults have major influence on. the fluid flowcharacteristics ot%” reservoir.” TIZs module focui&on the post sedimentary faulting. aspects @ thereservoti, fault geometry, fault pattern, fault planepropertied, conrpimtion/expanti6n due to faults. “

The module is based on phenomenologicalknowledge and seis@c: d@a in particular. Theseismic data is considered as a smoothedrepresentation of the faulted reservoir, hence it willcontain considerable information about the faultpattern. The method used is based on a MarkedPoint process.”””‘“ .:”-.: ““” ~‘ ~.. : - “:

The output is a set of realizations of the faultpatterns in the reservoirls-zo.

Sedimentary Module.

This concerns the sedimentary characteristics of thereservoi~ facies architecture, petrophysicalcharacteristics irr each facies type, zone averagetrends, etc.

The module is”primarily based on phenomenologiealknowledge and observations. in wells. The. use of..seismic data will be considered. Considerableamounts of subjective expert guesses are required,hence the merging of these with me reservoirspecific observations constitutes the mainmethodological challenge. The module is based on atwo-step model; facies tichitechrre model andpetrophysical facies-dependent model. Methodologyfmm spatial statistics including ge0statistic5 kusedz,zI-27.

T“wo different tWes of models are used in thismodule, discrete and continuous. Discrete modelsare used for modeling geological femnres like sandbodies, shafes, facie:, ~tc. ~ontiniious models areused for modeling continuous phenomena likeper2neabilities,” porosity, saturations, capillarypressriies; etc. ConiiimOuS modek may be used tomodel discrete phenomem by using triirrcationZ4.This approach should only be used if there is an

..orderirrg between the dkcrete classes.

Discrete PhenomenaMarked Point Processes (I!4PP) are well suited tomodel several objects with parameters like position,length, attractionkepulsion between objecL etc. andwhere the backgromrd is homogeneous. Irr STORMMPP are used to model small barriers27and channelsin fluvial reservoirs. Figirres 4, 5, and 6 showexamples where STORM has been used to modelchannels irr a fluvial reservoir. In these exampleseach object is a channel belt. The channel beltsconsist of several separate channels. Each channelbelt is an object with a main direction, position;number of charmel$ and correlated 1D Gauss%rrRandom fields along the each channel in the channelbelt as the most important parameters. The channelsare objects with pammeters like width, thickness,horizontal and vertical position which are modeledby a one dimensional Gaussian Random Process. ..:. ___

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The channels can be cmrdition<d to well da~ as wellas to sequence stratigraphk interpretations as shownirr Figiire “6. Figure 7 shows an example were anincreasing repulsion with elevation has been imposedresultiqg ii wider channel belts with elevation,

The channels modeled may also include intillheterogeneities as shown on Figure 8. These intillswill affect the petrophysical properties as shownwith a significantly reduced porosity ii “theinfill partof the charnel.

Markov Random Fields (MR@ are well suited forshalIow marine systems where there is no dominantbackground facies but rather all of the discreteclasses are equal in the model formulation.

In some reservoirs there are large barriers inhorizons. .The geometry of these horizons have asignifictit” effect “dn the reservoir propertieszs!ze.MRF can be used to model the geometry andtlickrress of large barriers as well as shaledistributions.

In STORM MRF may. also.be used to model faciesand flow units in three dimensions. The facies can beconditione~ exactly on well data and with a userspecified degree of exactness on ,mWrrally drq~fence diagrams. The model used is fmnal]y aMarkov model, but it lms similar properties as a tinedimensional serni-Markov model where time in eachclass is modeled separately. This ia used to modelthe size distributions and frequency for each facies.The model can also include irrteraction between thedifferent facies.

The use of fence” diagrams increases: the.=usmscontrol in determining the. realizations. Fencediagrams also lend themselves well to. .seryitivitystudy calculations.

The potential ‘for Markov Random field methods issignificant, It is a very large class of models whichhave a number of desireable features and possibilitiesfor reservoir modeling purposeszs.

Figtire ~ shows an example of a facies distributionmodeled by semi-Markov fields. Irr this example theblack is shale and the grey is sand bodies, .,

AS discussed facies .a;ghitectyres vw due to .fi?sedimen@ogical environment, and therefore severaltechniques are included in STORM for simulation of “‘the sedimentolo@al features. The techniquesinclude

. Mrrrkov Random fields for mosaicdeposition.a!deposits and large b+era.

. Marked Point process-for the fluvial labyrinthdepositsarrd srn.allershale barriers.

. Tmncated Gaussian function forenvironments where facies types are ordered.

. Sequential Indicator simulation, a voxelbased geostatistical method for modelingfacies architecture.

Continuous Variables .Continuous variables in STORM are modeled withtransformed correlated Gaussian Random fiekla29,30or Indicat& iQiging3],. I.n Gaussian Random fieldseach parameter is a Gaussian variable and the valuesin several dkcrete points are multiganssiandistributed, The model ..is fglly. specified by thetransformation, expectatiti~ and the correlationstmcpme. The expectation may depend on the spatialposition. “The spatial and intervariable correlationmust be specified.

A different petrophysical model may be used foreach facies Trends; e.g., decreasing parameter valuewith depth, may be specified.

Figure 10 shows the porosity distribution in the afluvial system. The lower part of this figure showsthe volume flooded after break-through where a verysimplified and fast single phase flow model has b=n.. _.used.

The different petrophysical variablea may havedhlerent properties depending on facies type.

:. Fhrid Module.

This module generates the initiil fluid distributionsand characteristics”““in the reservoiq the fluidcontacts, initial water saturation, capillary pressurecurves, relative permeability curves, conversionfactors, etc.

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The modufe is using a large varie~.. of baseinforfnation.”The modeling Gf hydrocarbon contactsirr heavily faulted reservoirs consti”tnte a particularchallenge. The module use dKferent statisticaltechniques; both. spatial and non-spatia132.Inchrdedin the modeling is I@ expectation, spatial variability,correlation between different variables, trends,diff&r&t properties in each flow unit, etc.

The output is a set of” realizations of the fluidcharacteristics: The contact surfaces will often bespatially varying.

Synthesis Module.

Thk, concerns the integration of tie iealimtion.s fromthe three reservoir description module$ Structural,Sedimentary, and Fluid.

The module will provide the user with the possibilityof intertiiitely combining all the reservoirdescription-” modules into a reservoir designrepresentative of the reservoir under study.

The output is a set of realizations of the reservoirdescriptiiii wi”thall the requested variables defined.

Reservoir Reslizstions

This consists of a collection of data sets representingthe possible reservoir descriptions, each horioringthe specified model and the reservoir specificobservations. The set reflects the uncertainty of theapecitied stochastic model.

Reservoir Performance

This consists of a“ set of operator-modules, eachtailored to answer important questions in reseivoirevaluation. Note that in order to obtain reliableanswers the important aspects of the reservoir-withrespect to the question being asked must be includedin the reservoir description.

HC-@-place Module.

This concerns the prediction ofbydrocarbon in-placeand will combine the variables in the reservoir

description to obtain this32.~3.The module will allowthe divisiori” of the reservoir “into differentcompartments if so is wanted.

The output is “a probability distribution of thepredicted HC-in-place, from which a best predictionand confidence intervals can be determined. Theinforrhation obtained from the HC-in-place moduleis vital at the appraisal stage.

Recovery Module.

This concerns the prediction of recovery andreservoir performance over time. Hence “amodel forfluid flow or production reservoir simulation must be.iQclu.&Jed.and this requires that a scale change pre-processor is available.

Thii module consists of two”types of sub-mcdrrles:

Scale-chymzesub-module.The representation of the reservoir description is irrvery fme resolution, far too fine for fuH fieIdsumdation of fluid flow in the reservoir. Hence ascale-change or homoge@tion into a coarserresolution, the reservoir simulator grid block level,must be performedj’+,zs. The scale-change sub-module is tailored to the dynamic reservoir simulatorbeing used. Scale-chamge on addhive variables liieporosity and saturation are trivial. Other variableslike permeabilities are much more complex.

Figure 11 shows the high resolution reservoirdescription grid a: well as the upscaled flrid flowsiomlation, in this case Eclipsem, grid in both thehorizontal and vertical duections. In the case studyd~cussed in a later section the grid for stochasticsimulation was 50m*50m*0.25m while the Eclipse7Mgrid was 100m*150ni*5m. It is not unusual that thefluid flow simulation grid blocks are even larger.

The lower part of Fi@re 11 shows the resulting.porosity distribution in both the high and lowresolution grids. It is quite clear from this figige~atinformation is lost in the upscaling process and thisis an area that needs more work and introduction ..ofmore flexible grid system for fluid flow silmrlation.

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Production Simulation sub-module.This contiins the model for fluid flow through wereservoir, constrained by the operatiomlrequirements. Considerable research has beeninvested in thk problem and a nnrnber of comrnerei~products are available.

STORM does not include sub-modules for jlu~dj70w, but efficient mferfacek to” cornrnirdially

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available and widely used production reservoirsirrmlatora are provided.

Decision Support Information

This is iriformation directly relevant for makingdecisions concerning, further development of aresewoir.

The information will primarily consist of probabilitydistributions for HC-in-pIace and a set of realizationsof production profiles for a given recovery s~atggy.The two types of information are rriutualiy consistentin the sense that the vohrrnefric uncertairr@ isincluded in the production profiles arid the detailed..-sednentary models are inclnded in the HC-in~placedisfributioris.

The results. are verifiable by re-generating resewoirdescription realiitions which gave rise to extiemevalues in the decision support information.

.The sedmenfmy strata were divided introtime zonesw~ch were modeled separately and separated bytime surfaces. The” tirrre surfaces were dependentupon the interpretation t?orn the hi~ resolutionsequence sfratigraphy and RFT data identifyinghorizontal pressure barriers.

Fa@s d$tiibutions were modeled within ideafiidparsllelepiped time zones ustig the Markov Randomfield model. The time zones were later transformedusing compactionktretchirig and erosions to.correctly characterize the sequences simulated.Parameters specified for the ficiis simulations in ‘..each tinre zones include

1. length distribution in x, y, and z directions ofeach sedentary facies,

2: relative frequency of each facies me,3. freedom of shape for each faciw””type,4. probability of transition between facies types,5. degree of confidence for which conditioning

should be performed to honor the inpuffacies based fence diagmrna.

The grid used for f=ies modeling was 50rn x 50m x0.25m, yielding a grid system of 79x79x225 gridblocks.

The pefrophysical properties, permeability, porosity~d water saturation, were simulated sinmltaneouslyusing correlations between them. A trend fimctionwas used for permeability irr the facies fype where

CASE STUDY ,. _ :Pp::pri?te.

Several studies using the modules comprisingSTORM liave “been publishedj~.~~,z~,z~One of themost recent case studies of the Ness formation ofone of the largest .NOUhsea..flel.&41..Restdta @@rresolution sequence stritigraphy and .fh? MarkcyRand~ Field model were integ@ed to createreSfiZafiOnS Of the facies dkifributiona andpefrophysical properties h“ the low stand and highstand sequences of the shallow marine Nessformation.’ ‘-” “””

The modeling consisted of 4 parts: .1) Georneffy ‘ofthe reservoir. 2) Sedimentary facies distribution. 3)Petrophysical properties. and 4) Upscaling.

14 realizations of facies and petrophysical propertieswere upscaled for use irr the element simulationmodel of the Upper Brenf reservoir (Ness andTarberf formations). This element model contained arefined grid in the Ness portion as well as the fhrxboundary condition option used to simulate the flowbetween the element and the remaining full fieldmodel.

From the analysis of currmlative wafer production intwo production wells within the element area; 6 ofthe 14 realizations show good comparisons. Theelement area contained two production wells whichproduce exclusively from the Ness and could be usedfor comparison wkb the sirnulatiorr of the

251

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10 ----- STORM SPE 27563. >:-... ““i.

realizations dkctly. The other two production wells:mchrdeda long production period from the Tsrbert,followed by commingled production with the Ness,making results difllcrrlt to interpret. For”one of thetwo production wells, the stochastic model matcheswater breakthrough and cumulative waterproduction very well as shown in Figure 12 RFT andPLT logs are alao matched well with the stochasticreafkations. The second production well has slowerwater production build-up in the stochastic modelcompared with the measured production rate. Thehktmy matchkg of the deten@ri$tic model was verydhlicult. for thi~well due to the extremely low waterproduction rate for the three years followed by avery rapid build-up.

Figure 13 shoys the. oil production from thedeterminiiticLmi5de1.and the 6 realizations generatedby STORM. .All the stochastic .reali.zations showsmore oil production in the years 1996-1998 than theconventional model. For water production, there is amore complicated ranking. However, the stochasticrealizations residt in higher production by the year2010. The simulations have been carried out for themodel using the sa!ne well actiyity plan as in theconventiomit model study.

The Markov Random model was proven to be avaluable tool in obtaining realizations which givegood historical production data for the shallowmarine Ness: formation.The stochastic approach tothe characterization of the fmmation has alsoallowed for the simulation of future productionpredictions based on all of the realization whichwere consistent with measured production data,allowing for ur@~aigtiea tObe assess@in .!hg“~~eprdctions, “riot obtainable through the historymatching/deterministic” approachforecasting.

~..

to prediction

The stochastic approach to reservoir modeling hasmoved beyond the reabn of the academic andresearch environmen~ and is a tecfilque that shorddbe made available to the Exploration & Productioncommunity along with and at. the same level as othercomplementary reservoir evaluation tools.

This paper has presented the stochastic reservoirmodeling system STORM. The aim of the softwaresystem STORM is to provide a “tool-box” ofgeostatistical and stochastic techniques which can beused to generate reahstic 3 dimensional reservoirdescriptions which are honoring all of the observeddata as well as general geological knowledge.

In STORM it is possible to integrate different kindsof data. It is also convenient and fast to update tbereservoir model as more information is gathered.Using the approach for reservoir characterizationdescribed in this paper, the users may asseas theuncefi@ of the hy_@ocarbon-in-placeas well as in

the production profiles. Combting the expensivelycollected data more optimally is often less expensivethan additional collection of data.

STORM is a modem software system in the sensethat it is user-friendly, robust, efticienL abide bystandards, and utilize state-of-the-art computertechnology.

ACKNOWLEDGEMENTS

The authors would like to express their appreciationsto several people for their corrkibutions irr makingSTORM a state-of-the-art geostatistical softwaresystem. The most central are Prof. Henning Omrepreviously with the Norwegian Computing Centerand now witi the Norwegian Institute ofTechnology, Prof. Adolfo Henriquez of Statoil, endKetil Waagb@ of ODIN Reservoir” Software &Services. Acknowledgement is also given to theentire staff iri”the SAND group of the NorwegianComputing Center, Massimo Bfibieri of”Agip, KjdfAme Jakobsen and Lars Magnus Nordeide ofStatoil, and HiWon Tjekelarrd of the NowegisnInstitute of Technology.

me first author wou!d. also like to express hisappreciation for the tirrancialand geoscience supportfrom Agip, Esso, and Statoil without which ST”ORMwould never have been built.

252

SPE 27563 — R.B. Bratvold, L. Ho!den,.T. Svanes, xd K. TYIer 11

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Hraiberg, J., Omre, H., and Tjelrnekmd, H.: “AS_~chastic_Model for Shale Dksibution iirPetroleum Reservoirs}. proceedings from fieSecond CODATA Conference onGeornathematics and Geostatistics, Leeds, Sept.10-14, 1990; to appear in Sciences de la Terre.

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SPE 27563 . .. . .... . R.B. Bratvold, L. Holden, T. Svanes, and K. Tyler

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Jones, A., Doyle, J,”Jicobsei, T., Kjmrs{i,”D.:“which Sub-seismic Heterogeneities @fluenceWaterflood Performance? A Case Sfidy of a ““- “”Low. Net-to-Gr&a Fluvial Reservoir,” paperpresented at the 1993.Erriopeari Sympogfi on10~ MOSCOW, Ott 27127. “

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Omre, H., Tjelrneknrd, “H., Q1, Y., tidHinderaker, L.: “Assessment of l.Jnce~inty iIIthe Production Characteristics of a Said-StoneReservoirfl proceedings NIPER “DOE‘“Third[ntemational Reservoir CharacterizationTectilcal Conference, Tulsa, Nov. 3:5, 1991.

Tyler, K.J., Svanes, T., and Omdal, S.: “FasterHk+to~ Matching and Uncertainty in Predicted

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