Copyright 2001, Society of Petroleum Engineers Inc. This paper was prepared for presentation at the 2001 SPE Annual Technical Conference and Exhibition held in New Orleans, Louisiana, 30 September3 October 2001. This paper was selected for presentation by an SPE Program Committee following review of information contained in an abstract submitted by the author(s). Contents of the paper, as presented, have not been reviewed by the Society of Petroleum Engineers and are subject to correction by the author(s). The material, as presented, does not necessarily reflect any position of the Society of Petroleum Engineers, its officers, or members. Papers presented at SPE meetings are subject to publication review by Editorial Committees of the Society of Petroleum Engineers. Electronic reproduction, distribution, or storage of any part of this paper for commercial purposes without the written consent of the Society of Petroleum Engineers is prohibited. Permission to reproduce in print is restricted to an abstrac t of not more than 300 words; illustrations may not be copied. The abstract must contain conspicuous acknowledgment of where and by whom the paper was presented. Write Librarian, SPE, P.O. Box 833836, Richardson, TX 75083-3836, U.S.A., fax 01-972-952-9435.

Abstract This paper describes FieldRisk, a knowledge based drilling system combined with a probabilistic cost and risk assessment software tool for integrated evaluation of field development options. The project uses field data and case studies and includes a database of drilling data from Australia, the Middle East, Europe, and the USA. Knowledge is captured from diverse sources including industry experts, company reports, and published papers using automated information extraction software. Lessons learned in previous field developments can be recycled and applied to similar situations to give a more accurate estimate of costs and risks. Risk, uncertainty and company learning curves are estimated using statistics from past data with allowance for dependencies between events. FieldRisk supports integration of drilling, reservoir and development risks which is a key need for realistic evaluation of offshore field development scenarios. For example, a horizontal well may offer higher production rates, but with a higher risk of hole instability and increased cost of reservoir monitoring and recompletion. Modelling the system in its coupled state can produce a global, life of field optimisation. The tool can use analogous well data from the database for a quick look at costs of proposed new acreage where company offset data may not be available. Multi-target 3D well trajectories can be designed automatically to estimate drilling costs by clicking on a plan view of the field and selecting target depths. Options for drilling and well sequencing,

completions, facilities, reservoir risks, reliability of supply and economic considerations can be evaluated in parallel. The tool allows different levels of granularity. Planners can get quick cost estimates, while the asset team can access more detailed information from the knowledge base or view graphically summarised supporting data from similar wells.

Introduction FieldRisk is a scenario modelling system for planning developments in an environment of uncertainty and risk. It facilitates very quick evaluation of a number of field development scenarios, with cost and risk estimates for each case, rather than attempting automatic optimisation. FieldRisk is an extension of Genesis, a software system including a global database of drilling data and tools for analysis, design and well costing. A multi-target trajectory design algorithm has been added to allow rapid design of the numerous wells in a field, for quick costing and risk estimation. A new casing setting depth algorithm allows quick automatic selection of casing shoe locations with adequate strength and kick tolerance. FieldRisk can import production forecast data from the user's chosen reservoir simulator. An economic model integrates risked drilling cost estimates, production profiles, facilities, operating costs, and a stochastic oil price prediction to produce cash flow and Net Present Value (NPV) estimates at selected discount rates for evaluation of each field scenario. Once the best field development option is selected the Genesis detailed well design system can be used for final design and AFE production.

Integration of Drilling, Reservoir and Facilities Aspects of Development Planning In the traditional approach to development planning, the geoscience, reservoir engineering, drilling, and facilities aspects were often handled sequentially and changes in the model required extensive reworking. While significant progress has been made in recent years towards integrating the geological, geophysical and reservoir engineering work flows,

SPE 71419

Assessment of Risk and Uncertainty for Field Developments: Integrating Reservoir and Drilling Expertise

R Irrgang1, SPE, H Irrgang2, SPE, S Kravis1, SPE, S Irrgang3, G Thonhauser4, SPE, A Wrightstone5, E Nakagawa5, SPE, M Agawani5, SPE, P Lollback5, T Gabler5, SPE, E Maidla5, SPE 1 CSIRO Mathematical and Information Sciences, 2 Irrgang Reservoir Management, 3Sydney University 4 Thonhauser Data Engineering, 5 CSIRO Petroleum

R IRRGANG,H IRRGANG, S KRAVIS, S IRRGANG, G THONHAUSER, A WRIGHTSTONE 2 E NAKAGAWA, M AGAWANI, P LOLLBACK, T GABLER, E MAIDLA SPE 71419

integration with drilling and facilities aspects is still very incomplete, and only limited tools are currently available.

The geology defines the formations encountered while

drilling, each with their own special drilling issues, e.g. shallow gas, swelling clays, bit balling, hard cemented zones, lost circulation, overpressure or underpressure, suitable casing seats. The importance of the tectonic regime has been recognised in recent years, e.g. faults encountered, fractured zones, stress related borehole instability, sensitivity to drilling azimuth. Development of multi-pool, marginal fields with their inherent need for development optimisation has created a trend towards more complex well paths, with multi-target, extended reach, horizontal and multi-lateral wells increasingly replacing the traditional vertical or build-and-hold wells. Rather than making do with a list of prognosed formation tops, this has created a driver for the capability to view the planned well path and casing seats within a 3D integrated geological model to ensure consistent and optimum well design.

Drilling parameters strongly affect reservoir productivity and ease of completion and evaluation. The mud chemistry, fluid loss characteristics, overbalance and duration of exposure have a major impact on the extent of formation damage. The ability to effectively evaluate the hole is a function mainly of hole angle, hole rugosity, filter cake thickness and invasion profile. For example, mud fluid loss characteristics can significantly impact the ability to obtain reliable wireline formation test pressures and samples.

High angle wells can offer significant benefits in increased well productivity, but will also increase completion costs and may prevent future wireline access for surveillance and recompletions. Difficulties in control of deviation angles or in tracking the reservoir can result in unnecessarily high maximum angles, limiting future operations. Conversely, unnecessarily tight areal or vertical targets requiring frequent path corrections can add significantly to drilling costs. There is significant interaction between hole/casing sizes and future limitations on tubing size, completion type (e.g. dual, selective single, monobore), sand control and stimulation options.

A "life of field" approach is needed to optimise the drilling, completion, future workovers, and operating costs. A typical issue is whether to use extended reach wells or remote sub-sea wells, each option carrying many implications regarding the surface facilities, future surveillance, production and workover constraints, and operating costs. These issues can only be properly addressed with an integrated model of the development, production and abandonment phases. The model needs to be able to incorporate new information or technologies becoming available during long-term development. In addition to addressing the technical issues, this will provide the focal point for the integrated development and operations team to ensure effective and constructive inter-team commu nication.

While optimisation in a deterministic world is tough, a major real life complication is the need to address the uncertainties in the geology, reservoir performance,

production forecasts, development modes, costs and schedules. Development schemes of apparently equal attractiveness may differ considerably in their robustness against uncertainty. Optimisation in this context requires the ability to handle discrete scenarios as well as continuous probability distributions. For example, sizing of facilities needs to take into account the appropriate range of oil, gas and water rates and pressures, with the ability to expand the well numbers and producing locations to handle reservoir upsides. Conversely, minimum exposure to the downsides is also important.

Development of tools for handling discrete and continuous geological uncertainty is well advanced, and good progress is being made with tools for translating this into reservoir development and performance uncertainties. A tool is also needed for quickly estimating the drilling costs for multiple development scenarios and the associated cost and schedule uncertainties, in a consistent manner, with the ability to interface into a probabilistic economic model. This is the focus of this paper. A statistical drilling cost estimation tool, interfaced with a probabilistic economic model is described and illustrated by Potoroo, a hypothetical field development on the North West shelf of Australia.

Quick Multi Target 3D Trajectory Design A requirement for evaluating alternative programs is the ability to automatically design a 3D trajectory passing through multiple targets. For screening studies, the targets can be quickly input by clicking on a plan view of the field and specifying the TVD of each target. For more detailed studies, the target cooordinates can be obtained from a 3D geological model. The target positions and build rates are shown in a grid which expands to contain the specified number of target positions. An initial maximum build rate can be input. The algorithm will notify the user if this rate is too small and will also suggest the lowest possible build rate to produce the required trajectory. Fig 1 shows an example of a well design on a plan view of the field, Fig 2 shows the 3D representation of the same trajectory. Relevant features such as depth contours and fluid contacts can be imported and displayed on the plan view. Clicking on the button "Knowledge about Field" loads a summary of available information relevant for the new development. The planned surface location for the well can also be displayed. In the example shown in Fig 1, a low build up rate was chosen to allow FieldRisk to select the minimum feasible build rate for each section of the well path. The build rates calculated are tabulated in the grid.

ASSESSMENTOF RISK AND UNCERTAINTY FOR FIELD DEVELOPMENTS: SPE 71419 INTEGRATING RESERVOIR AND DRLLING EXPERTISE 3

Automated Casing Setting Depth Calculations Once a trajectory has been designed and conductor and surface casing points specified, an automated casing setting depth module can calculate casing setting points based on kick tolerance. The algorithm is designed to work with any well path including sinuous wells. Figs 3 and 4 illustrate the results for an example horizontal well. Graphs of equivalent density versus MD and TVD, kick bubble length and bubble pressure versus MD are displayed for verification of the results. The user can also move casing points, add and delete casing shoes

Time Based Well Costs Dominate Well costs are estimated to make up 40-60% of the total cost of finding and producing oil and gas. Time based costs include rig day rates, drilling services, engineering and support services. Depth based costs such as casing and hardware, mud and cementing are to some extent correlated with drilling time. Well construction times are commonly estimated from end-of-well reports using a small number of offset or similar wells. However, companies moving into new areas, or trying new well types or technology new to the area, may not have access to relevant offset well data. Another problem is that diffe rences in inclination and hole diameter between the planned and offset wells affect the estimated duration of not only hole making activities but also many non-hole-making activities. These considerations provide support for a time based statistical approach as outlined below. Statistically Based Drilling Time Estimates Estimates of drilling time are based on a large global database collected using Genesis. The database allows the use of statistically-based estimates of construction time from daily drilling reports for large numbers of offset wells. A set of standard classifications for all operations associated with well construction has been defined to allow valid comparisons to be made between the durations of similar operations even when performed in different areas or by different companies. A synthetic time versus depth curve is produced by selecting phases from similar wells from the global database. Similarity can be defined in any way desired proximity, target MD, trajectory and drillability are most commonly used. If sufficient phases from offset wells are available, the statistical distribution of the normalized times or rates associated with an operation can be estimated, assuming a log normal distribution for all parameters. Mean values, best and worst cases are always calculated. If there are not enough samples available to estimate the distribution, a deterministic estimate of the mean of the samples is used. If no data if available, then system or company default ranges can be used, or these can be used in place of estimated values if these fall outside the plausible range. Tripping operations are particularly problematic in this regard, as other operations other than running pipe are frequently described as tripping in morning reports. Operation rates or times are defined to be dependent on depth (e.g. tripping), phase depth range (e.g. ream and wash), or water depth (e.g. running riser). They may also be independent of

any of these parameters (e.g. BOP Testing). A Monte Carlo simulation is used to model the full TxD curve. Statistical operations are assumed to be independent of each other, consistent with data examined so far.

As part of the setup process, pre-defined groups of operations corresponding to different types of phase have been defined. For example the operation sequence for the Surface Hole phase is defined as:

Each operation corresponds to a particular operation code, or group of codes, allowing the statistical estimate to be made by processing the input wells . Operations can be added or removed to cater for new situations. Fig 5 shows an example for which best, worst, mean, P10 and P90 time depth curves have been displayed. Validating Drilling Time Predictions To test the validity of the synthetic time versus depth curve, the well in Fig 5 was planned with the same trajectory and casing string as Stag 9H, an Apache operated horizontal well from the North West shelf off Western Australia. This was the first in a sequence of horizontal development wells. Offset wells were a combination of 5 vertical wells from within similar water depths along the NW Shelf, and a group of 11 horizontal wells with similar target depth and horizontal extents, all drilled within the previous year. The horizontal wells came from another lease 25 km away and were drilled by a different company, though using the same rig. The mean time estimated from the offset wells was 27.92 days with P10 and P90 estimates of 20.83 and 35.42 days respectively. This was an overestimate of 2.08 days on the actual time, which is

R IRRGANG,H IRRGANG, S KRAVIS, S IRRGANG, G THONHAUSER, A WRIGHTSTONE 4 E NAKAGAWA, M AGAWANI, P LOLLBACK, T GABLER, E MAIDLA SPE 71419

better than the original company estimate (which underestimated by 8.75 days). Results for Middle East exploration well ME-65 are shown in Figure 6. In this case, only three offset wells were available from a neighbouring field within the same region. These were development and injection wells from within 10 km of ME 65, two drilled 6 months before and one 3 years before. The TxD values for the three input wells are shown as light grey lines in Figure 6. None is as long as ME 65. All are deviated wells, but with much shorter inclined sections than ME 65. With only three wells, no statistical estimate can be made and the actual TxD is worse than the worst-case estimate derived from the three offset wells, with an underestimate of 48 days, (a similar underestimate was made by the company). Offset wells with comparable length of inclined section might have given a better estimate. To estimate the time for ME-70, which was the 5th development well in the same field as ME-65, 8 offset wells were used. These comprised all the preceding wells in the field (ME 65 to 69) and the three development/injection wells from a neighbouring field use for estimating ME-65. Trouble times were first plotted for all of the offset wells as shown in Figure 6A, in order to determine the primary causes of trouble for the wells . As most of the trouble was hole and equipment related, it could be included in further analysis. (Periods of trouble due to waiting on weather need to to be treated differently, as reduction during a sequence of wells cannot be necessarily be attributed to company learning.) Trouble time appears to diminish significantly between wells 5 and 8 showing possible company learning, and this improvement can be quantified

using the Learn Curve analysis 1. Figure 6B shows the results of Learn Curve analysis on the last 4 development wells before ME 70. Plotted times have been normalized to the mean well MD of 3661m. Trouble time decreased considerably, with a learning rate of between 0.9 and 1.4, with a best estimate of 1.15. Taking the slowest learning rate, and extrapolating the Learning Curve model to the 5th well gives an estimate of 5.3 days of trouble time for the next well. Allowing for the fact that the next well has an MD of 4331m, this gives an estimate of 6 days for the trouble time. Modelling of trouble times from other wells shows that the statistical distribution of trouble time can be modeled using a log normal distribution with the standard deviation equal to the mean. Figure 6C shows the result of performing the simulation ignoring all trouble time in the offset wells and adding a trouble estimate with a mean and standard deviation of 6 days at the end. The predicted time is 83 days compared to the actual time of 72.5 days and a company prediction of 40 days. Summary of Predictions Table 1 shows a summary of results from a comparison of Genesis and company estimates for further Apache operated horizontal wells from the North West shelf of Western Australia and three from the Middle East (ME68,69,70). For the Apache operated horizontal wells (Stag and Harriet), all wells preceding the actual well were used in the estimate. The calculations for ME-70 has already been described. For ME-68 and 69 (the 3rd and 4th development wells) trouble time estimates were decreased to allow for company learning. Means have been calculated for the Genesis and company

Time in Days

WellName Actual Plan Genesis

P10 Genesis Mean Genesis

P90

Genesis Mean - Actual

Company Plan - Actual

Genesis %

Error Company % Error

Stag-8 15.83 9.38 13.25 13.88 14.29 -1.96 -6.46 -12.37 -40.79

Stag-9H 25.83 17.08 20.83 27.92 35.42 2.08 -8.75 8.06 -33.87

Stag-18H 15.08 16.76 13.67 14.50 16.33 -0.58 1.68 -3.87 11.12

Stag-20H 9.67 11.33 10.33 10.92 12.50 1.25 1.67 12.93 17.24

Stag-21H 10.63 10.75 7.29 8.96 10.42 -1.67 0.13 -15.69 1.18

Harriet B-5H 43.67 20.83 24.58 42.50 62.08 -1.17 -22.83 -2.67 -52.29

ME-68 150.00 62.92 87.92 98.33 119.17 -51.67 -87.08 -34.44 -58.06

ME-69 57.50 43.75 46.67 68.33 97.92 10.83 -13.75 18.84 -23.91

ME-70 72.50 40.00 62.00 83.00 107.00 10.50 -32.50 14.48 -44.83

Mean difference -3.60 -19 Mean absolute differences 9.07 19.43 Std Dev 18.69 28.15

Ratio of Genesis to Company Std

Dev 0.66

Table 1

ASSESSMENTOF RISK AND UNCERTAINTY FOR FIELD DEVELOPMENTS: SPE 71419 INTEGRATING RESERVOIR AND DRLLING EXPERTISE 5

predicted - actual time estimates. Both Middle East and NW Shelf wells show a systematic underestimation. Genesis has underestimated times by a mean of 3.6 days, the companies by 19 days on the data sampled. The mean of the absolute errors in estimation for Genesis was 9.07 days, for the companies 19.43 days.

To compare the percentage error, standard deviations were calculated for both Genesis and company estimates, the ratio (Genesis SD/Company SD) is calculated. The result 0.66 indicates a 34% improvement in uncertainty could be gained using the Genesis predictions as well as removing most of the systematic underestimation bias. Costing Once probabilis tic Time Versus Depth estimates have been made, the results are passed into an AFE spreadsheet, which may be company-defined or the Genesis standard. An example of a section from an AFE is shown in Fig 7. The summary table of mean cost and P10 and P90 estimates is shown in Fig 8. The cost data collected for the Genesis project supports the conclusion that time based costs are in excess of 80% of total well costs. The project has therefore concentrated on providing risk estimates for drilling and completion of wells based mainly on time.

Knowledge Extraction Tools The drilling database contains in addition to numeric data, text based information in driller's morning reports, end of well and basin summary and incident reports. Valuable information relevant to planning a similar field can be extracted automatically from these text reports. Tools have been developed to automate the extraction and to summarise concepts of interest to new well planners. For example incidents such as lost circulation can be found and displayed on graphics showing the depth ranges for each incident. The automated extraction system is a shallow knowledge search, based on pattern matching of key phrases and headings. For example a heading in a report labeled "Recommendations" is expected to contain useful summary information. Information from experts can also be acquired and input, this is the most valuable knowledge source where it is available. Formation related problems are also extracted and summarized to appear as warning flags on graphics containing the associated formation, (an example is shown in Fig 9). The extraction is automatic but the resulting summaries can be edited by expert users, who can add additional information. Summaries can also be extracted from published papers with links giving the original reference.

The automatic text processing software being used to identify relevant technology applications and drilling problems for analogous wells is now being extended to help identify analogous fields and field developments. The text of interest is extracted and used to quickly identify the key features and relevance of the analogue to the field being developed. These summaries are very useful for full field development as they often contain valuable, in depth

recommendations, useful for similar field developments. For example an engineer planning the hypothetical Potoroo field on the North West shelf of Western Australia (which is located near Wandoo and Stag fields) can click on the button labeled "Knowledge about the Field" to retrieve the summary in Tables 2 and 3.

Main Features of Interest Thin Oil Column (22m) High Oil Viscosity Recovery is sensitive to height of

wellbore above OWC To a lesser extent recovery is

sensitive to depth below GOC Well paths need to be steered

within 0.5 to 1.5m tolerance Unconsolidated sands, sand

production is a major problem Very high permeability High glauconite content increases

uncertainty in log interpretation Goodacre2 1996

Table 2

Recommendations 1. Horizontal wells necessary for

commercial development, deviated wells were found to be uneconomic for the Wandoo development

2. 1100m horizontal wells can be drilled successfully

3. Optimum standoff above OWC 14m (+-2m)

4. High production rate needed above critical water coning rate

5. Dual wire packed screens used for sand control

Table 3

Integrated Field Development Example for Potoroo Field Potoroo is a hypothetical offshore field on the North West Shelf of Western Australia near the Stag and Wandoo fields, about 70 Km north northwest of Dampier in Western Australia. The main reservoir is assumed to be a thin oil column located around 800 metres below sea level in a poorly consolidated glauconitic sandstone. A deeper, marginally economic reservoir is located around 2000m TVD. Petrophysical evaluation is uncertain due to poor and non-representative core recovery from this unconsolidated sand,

R IRRGANG,H IRRGANG, S KRAVIS, S IRRGANG, G THONHAUSER, A WRIGHTSTONE 6 E NAKAGAWA, M AGAWANI, P LOLLBACK, T GABLER, E MAIDLA SPE 71419

washed out hole, and the effect of the glauconite on the logs. Accurate seismic mapping of the Potoroo reservoirs is a challenge as there is a lack of acoustic contrast between the M. Australis reservoir section and the overlying silty Upper Muderong claystone seal. Some of the appraisal wells have encountered overlying gas caps, but the gas cap area is uncertain. There is a down flank, (or partly underlying aquifer) whose strength is uncertain. The base case scenario studied used 10 horizontal oil development wells drilled from a single platform using a jackup rig, plus two water injection wells, with production going to an FPSO. A second scenario involved development of a marginal deeper objective. As the reserves per well for the deeper objective were insufficient to justify separate wells it was assumed that six of the M. Australis horizontal producers would be replaced with conventional deviated wells completed in both the M. Australis and the deeper reservoir.

Other development scenarios that needed to be evaluated were use of only deviated wells in the M. Australis, a two platform case to reduce hole angles and improved flank coverage, and a gas re-injection case. In addition, geological uncertainties on field area, gas cap size and aquifer size needed to be addressed.

The Field Development tool allows quick well trajectory design by clicking on a plan view of the field to select target locations, then specifying a TVD for each target. The system automatically designs a trajectory passing through all of the selected targets. Fig 10 shows the set of wells for Potoroo field on the plan view which also contains digitized lines for a significant fault, gas oil contacts, oil water contacts and significant horizons. Facilities descriptions and their costs can be selected from a list or input and saved to the database for later reuse. Currently cost details of facilities must be input by users. The wells can also be viewed in 3D as shown in Fig 11. The graphics system allows rotation in any direction using two slider bars for further checking of trajectory designs. More detailed trajectory design is also possible if required. Once trajectories have been saved, formation tops, pore pressure and fracture gradients can be estimated quickly from offset wells or specified manually, if required.

Proposed mu d weights can be input graphically on a plot showing geology, pore pressure and fracture gradient. The system can then automatically calculate casing setting depths as described previously. Users can edit the casing setting depths or specify them all manually by dragging in casing shoes on a vertical section graphic screen. Automated casing design is also available. Once each complete well is designed the Time versus Depth module can estimate the time to drill including mean, P10 and P90 risk estimates which are then exported into an economic model. Linking Genesis Drilling Estimates with the Economic Model The portion of the input screen in Fig 12, shows well data inputs to the economic model for a set of wells, Potoroo1 to Potoroo12. A risked duration, shown in Fig 13 for Potoroo1,

in days is estimated. A log normal distribution is fitted to the mean, P10 and P90 drilling time values for use in the @Risk formula RiskLognorm2(MeanLn,StdLn), where MeanLn and StdLn are the mean and standard deviation of the logarithm of the drilling time. (Variation in well construction time is dominated by trouble time , whose distribution is best modeled using a log normal distribution on the basis of data so far examined).

The relation 4

2

eL

s+= where Lm is the mean of the

lognormal distribution, is used to calculate the mean, m of the corresponding norma l distribution for use with @Risk.

For Potoroo1: MeanLn = 3.606 StdLn = (Ln(P10)-Ln(P90))/(2*1.28155) =0.146

Downside and Upside Uncertainties Additional uncertainties can be included in the model as shown in Fig 12, under the headings "Downside Uncertainties" and "Upside Uncertainties". A free text explanation can be included and the uncertainty can be low, medium, high or very high. The uncertainty rating for "Unconsolidated sandstone causing hole instability on horizontal sections" is set as high which in this case translates into a weighting on the P90 value of 1.3. The upside uncertainty, in which faster drilling is also possible due to the unconsolidated sandstone, is implemented as a weighting of the P10 value.

Monte Carlo Field Development Economics Fig 15 shows a section of the main Monte Carlo field development screen. Note that the numbers shown represent only the means of the statistical distributions used. Costs related to exploration and appraisal, development drilling and completions, surface facilities and abandonment (including parameters describing their statistical distribution), for the appropriate scenario, are imported from the Genesis drilling module or input by the user. They can also be retrieved from or saved to a database for later reuse. A consistent case numbering scheme allows matching, for each scenario, of the various cost and revenue data from the various sources. Oil price is a risked variable which will be subject to a random walk, with the user inputting initial and trend data only. Expected oil production profiles for the well configuration designed for scenario 1 should be imported from a reservoir simulator or otherwise input by the user, together with the statistical uncertainty. This particular model uses the total oil production from the wells, as all wells come into production together in year 2005, having been predrilled prior to commissioning of the surface facilities. Other scenarios may bring on early or later production from some wells. Fixed operating costs and variable operating costs per barrel or per well are also input. Revenue, royalties and Net Cash Flow before tax are calculated by the economic model. The graphs in Figs 16 and 17 illustrate some of the available system outputs for comparison between scenarios.

ASSESSMENTOF RISK AND UNCERTAINTY FOR FIELD DEVELOPMENTS: SPE 71419 INTEGRATING RESERVOIR AND DRLLING EXPERTISE 7

Oil Price and Production In this example, oil price is a risked input with a mean of $25 US per barrel for each of the 12 years modelled. A probability distribution is calculated for oil price each year, the results for the year 2012 are shown a in fig 14. Fig 16 shows the stochastic range of production curves for the combined wells in Potoroo field. The yellow line is the expected value of production, the brown bands give plus and minus one standard deviation from the mean. The green band is the 95% and 5% confidence limits for oil production over the plateau period and decline. Net Present Value Calculations Net present value of the project , shown in Fig 17, is calculated at discount rates of 8 to 14 percent. The cash flow distributions for each geological and/or reservoir uncertainty can be risk weighted to derive the risked value for that development scenario. This process can then be repeated for each field development scenario proposed, to identify the most attractive and robust scheme.

Second Scenario: - Development of Deeper Objective A second scenario was evaluated assuming combined development of both reservoirs as described earlier. The right side of figs 16 and 17 show the results for Scenario2. Although additional reserves are recovered and early production is higher, the performance of the deviated completions in M. Australis is poorer resulting in unattractive economics for this case. Plans for the Future A simple economic model with risk and uncertainty has been implemented and future versions could be expanded to include more complex models for the interaction between reservoir and drilling aspects of the field development. As the database expands, additional reasoning based on stored previous development experiences could be used to refine the risk and uncertainty calculations. Further research on capture and reuse of knowledge from expert engineers is also planned.

An expanded database of facilities and completion data with enhanced graphics to allow users to position facilities graphically is another future development. Further work will include benchmarking and well quality measures. It is also planned to add technical limit information to the database so that new wells can be planned with the more ambitious goal of moving towards technical limit.

Conclusions A software system has been produced for quickly estimating drilling costs and their uncertainties in a consistent manner, for multiple scenarios, using available offset or analogous well data. New algorithms for multi-target trajectory design and casing setting depth facilitate well planning. Cost estimates incorporate the geological and reservoir considerations. The original drilling cost estimator has been interfaced to a Monte Carlo economic model for integration of drilling, reservoir and facilities aspects, and for modeling of risk and uncertainty, to achieve global optimization over the life of the field. Data management for multiple scenarios allows linking to the common economic modelling.

Uncertainty is handled both as discrete alternative scenarios and as continuous stochastic variability, e.g. a proposed development plan can be evaluated against alternative geological and reservoir models, each weighted by an appropriate probability, to ensure the development has sufficient flexibility, without over-investment. References 1 Brett, J.F. and Millheim, K.K.: "The Drilling Performance

Curve: A Yardstick for Judging Drilling Performance", Proc 61st Annual Technical Conference, SPE, New Orleans, 1985

2 Fabian, M. R.,: "Wandoo: The Development of Marginal Field", APPEA Journal 1999, pp523-531.

3 Goodacre, J., Ion, A. M., Alexander, I. R.,: "Stag Development-Challenges and Successes in the First Two Years", SPE 64439, SPE Asia Pacific Oil and Gas Conference, Brisbane, Australia 16-18 Oct. 2000

4 Goodacre, J.,Wilson, G., Ure, J.S., Clyne, I.S.: "Wandoo Field Horizontal Completions - Phased Development of a Shallow, Highly Permeable Reservoir, with a Thin Oil Column", paper SPE 36965, 1996 SPE Asia Pacific Oil and Gas Conference Adelaide, Australia, Oct. 28-31.

5 Ion, A.,: "Stag Field: fro m Discovery to Break-Even", SPE News, Australasia, Feb. 2001, issue 54.

6 Irrgang, R.,Damski, C., Kravis S., Maidla, E., Milheim K.: "A Case-Based System to Cut Drilling Costs", SPE 56504, 1999 SPE Annual Technical Conference Houston, Texas, Oct 3-6.

7 @Risk, "Risk Analysis and Simulation", Palisade Corporation, 1997, NY USA.

Acknowledgement The authors would like to thank Apache Energy Ltd. for their assistance and permission to publish the drilling data

R IRRGANG,H IRRGANG, S KRAVIS, S IRRGANG, G THONHAUSER, A WRIGHTSTONE 8 E NAKAGAWA, M AGAWANI, P LOLLBACK, T GABLER, E MAIDLA SPE 71419

Figures

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Fig. 2 3-D view of the trajectory shown in Figure 1

ASSESSMENTOF RISK AND UNCERTAINTY FOR FIELD DEVELOPMENTS: SPE 71419 INTEGRATING RESERVOIR AND DRLLING EXPERTISE 9

Casing Setting Point1

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Fig. 3 Results from automatic casing design program using horizontal trajectory

Fig. 4 Verification of results of casing setting depth program from automatic casing design program

Fig. 5 Comparison of predicted and actual TxD curves for Potoroo 9H well and company predictions, using vertical wells from elsewhere on the NW shelf and horizontal offset wells from another company. Predicted time was 27.92 days, compared with an actual time of 25.83 days and a company prediction of 18.13 days. Final MD values for the different predictions have been displaced slightly for clarity

R IRRGANG,H IRRGANG, S KRAVIS, S IRRGANG, G THONHAUSER, A WRIGHTSTONE 10 E NAKAGAWA, M AGAWANI, P LOLLBACK, T GABLER, E MAIDLA SPE 71419

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Fig. 6 Comparison of predicted, company-predicted and actual TxD curves for Middle East exploration well ME65. Three offset wells were used from a neighbouring field, which were considerably shorter than the planned well, with much shorter inclined sections, as shown on the inset. Predicted time was 79 days, compared with an actual time of 127 days days and a company prediction of 63 days. If the comparison is made excluding trouble time, the predicted time is 75 days and the actual time is 93 days.

Fig. 6A Trouble episodes for offset wells used in estimating ME70 TxD. Wells 1-3 are development wells from a neighbouring field, Well 4 is the initial exploration well ME65 and wells 5-8 are development wells from the same field. Vertical numbers are the MD ranges for each well and the green line shows the TxD curve for each well. Well 4 (ME-65) and Well 5 had long periods of hole-related trouble( stuck pipe, tight hole) in the deeper phases of the well, but performance improves significantly in wells 6-8.

ASSESSMENTOF RISK AND UNCERTAINTY FOR FIELD DEVELOPMENTS: SPE 71419 INTEGRATING RESERVOIR AND DRLLING EXPERTISE 11

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Fig. 6B Learning rates for well operations in the 4 development wells preceding ME 70 The parameters in the inset box represent the parameters of the exponential learning curve model used by Brett & Millheim1 . Learning is manifest in the reduction in trouble time, and the learn rate fitted (1.15) is significant using the classification proposed by Brett & Millheim.

Fig. 6C Comparison of predicted and actual TxD curves Middle East well development well ME70, on the basis of exploration well ME65, 4 other development wells from the same field, and the development/injection wells from a neighbouring field used to estimate ME65. To account for learning trouble has been ignored in the offset wells, and a trouble time distribution with a mean and statndard deviation of 6 days has been added at the end. The value of 6 days trouble was derived from Learn Curve analysis of the 4 preceding wells. The predicted time is 83 days, compared to the actual time of 77.5 days and the company prediction of 40 days. Trajectories for offset wells and ME 70 are shown below.

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R IRRGANG,H IRRGANG, S KRAVIS, S IRRGANG, G THONHAUSER, A WRIGHTSTONE 12 E NAKAGAWA, M AGAWANI, P LOLLBACK, T GABLER, E MAIDLA SPE 71419

Fig. 7 Part of the Genesis standard AFE spreadsheet containing values derived from the Time Versus Depth Simulation

Fig. 8 Cost summary from AFE for Potoroo-9H showing P10, P90 and Mean cost estimatesfor each phase of construction (conductor, surface hole, intermediate, production & completion)

Fig. 9 Results from automated text extraction or expert knowledge associated with a particular formation top (M. Australis sand) displayed during geology definition. The presence of data for a particular formation is indicated by the presence of the purple dot, which can be clicked on to view the data.

ASSESSMENTOF RISK AND UNCERTAINTY FOR FIELD DEVELOPMENTS: SPE 71419 INTEGRATING RESERVOIR AND DRLLING EXPERTISE 13

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Fig. 10 Plan view of Potoroo field development showing depth contour lines at 775, 800 and 825 m below RKB, the oil-water and gas-oil contacts, the bounding fault, platform location and planned well paths for the proposed development. Proposed facilities can be selected from the list shown.

Fig. 11 3-D view of Potoroo development.

R IRRGANG,H IRRGANG, S KRAVIS, S IRRGANG, G THONHAUSER, A WRIGHTSTONE 14 E NAKAGAWA, M AGAWANI, P LOLLBACK, T GABLER, E MAIDLA SPE 71419

Drilling Cost Summary

Scenario 1 (Single Platform Development with 2 Subsea Injectors)

Rig Fixed Completion

Well Name Duration Cost Year P10 Mean P90 mean ln std ln Cost/Day Costs Costs

days $MM Drilled M$/d $MM $MM

Potoroo1 37.2 6.5 2001 33.00 37.00 48.00 3.606 0.146 120.0 1.20 0.80

Potoroo2 30.1 5.6 2001 28.00 30.00 37.00 3.398 0.109 120.0 1.20 0.80

Potoroo3 28.3 5.4 2001 21.00 28.00 36.00 3.321 0.210 120.0 1.20 0.80Potoroo4 18.1 4.2 2001 15.50 18.00 21.50 2.886 0.128 120.0 1.20 0.80

Potoroo5 25.2 5.0 2001 19.50 25.00 31.25 3.210 0.184 120.0 1.20 0.80

Potoroo6 16.1 4.1 2001 14.50 16.00 19.50 2.769 0.116 125.0 1.25 0.85

Potoroo7 25.2 5.2 2001 20.00 25.00 31.25 3.211 0.174 125.0 1.25 0.85

Potoroo8 10.1 3.4 2002 8.00 10.00 12.50 2.295 0.174 125.0 1.25 0.85

Potoroo9 15.1 4.0 2002 12.00 15.00 18.75 2.700 0.174 125.0 1.25 0.85

Potoroo10 11.1 3.5 2002 8.80 11.00 13.75 2.390 0.174 125.0 1.25 0.85Potoroo11 11.5 3.7 2002 10.50 11.50 12.50 2.441 0.068 140.0 1.25 0.85

Potoroo12 9.0 3.4 2004 8.00 9.00 11.50 2.192 0.142 140.0 1.25 0.85

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Fig. 12 Input data for economic model. Estimated durations are based TxD modeling from available offset wells. Modelling assumes a log normal distribution of durations, obtained by fitting a log normal curve to the mean, P10 and P90 values obtained from TxD modeling. An example of the distribution is shown in Fig. 13.

Fig. 13

Risked duration for Potoroo 1 well.

Fig. 14 Probability distribution for oil price for the 12 years modeled. The distribution has a mean of US$25 per barrel

ASSESSMENTOF RISK AND UNCERTAINTY FOR FIELD DEVELOPMENTS: SPE 71419 INTEGRATING RESERVOIR AND DRLLING EXPERTISE 15

Fig.15 Scenario 1: Potoroo Field Monte Carlo Simulation Inputs. Cells in blue contain mean time -related costs of drilling whose actual values will vary with each iteration of the simulation (risked inputs). Cells in red correspond to other risked inputs, and green cells are fixed inputs. The oil production inputs can be input from a reservoir simulator, together with uncertainties. For the Net Present Value calculations, mean rather than risked oil production values are used.

Monte Carlo Field Development Potoroo Field

EconomicsScenario1: Development with 2 Subsea InjectorsYear Total 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012Exploration and Appraisal ($MM) 10.0 5.0 5.0Development Drilling and Completion ($MM) 53.9 0.0 36.0 14.5 0.0 3.4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0Surface Facilities + Abandonment ($MM) 160.0 40.0 80.0Total Capital Costs ($MM) 223.9 5.0 41.0 14.5 40.0 83.4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

(MMBBL)Oil Production (MSTB/D) Risked 44.4 30.0 30.0 21.0 14.7 10.3 7.2 5.0 3.5

Annual Fixed Operating Costs ($MM) 48.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0Variable Operating Costs ($/bbl) 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0Variable Operating Costs ($/well)Total Annual Operating Cost 92.4 17.0 17.0 13.7 11.4 9.8 8.6 7.8 7.3Oil Price ($/BBL) 25.0 25.0 25.0 25.0 25.0 25.0 25.0 25.0 25.0 25.0 25.0 25.0Revenue ($MM) 1111.1 273.8 273.8 191.6 134.1 93.9 65.7 46.0 32.2Royalty 114.9 31.2 31.2 21.0 13.8 8.7 5.2 2.8 1.0Net Cash Flow before Tax 679.8 -5.0 -41.0 -14.5 -40.0 -83.4 225.6 225.6 157.0 109.0 75.4 51.9 35.4 23.9

DiscountDCFR 49% Rate $MMNPV (2001) @ Discount Rate 0% 679.8

8% 404.110% 356.812% 315.614% 279.4

Oil Production (MSTB/D) Mean 44.4 30.0 30.0 21.0 14.7 10.3 7.2 5.0 3.5Annual Fixed Operating Costs ($MM) Mean 48.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0

Variable Operating Costs ($/bbl) Mean 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0Oil Price ($/BBL) 25.0 25.0 25.0 25.0 25.0 25.0 25.0 25.0 25.0 25.0 25.0 25.0

R IRRGANG,H IRRGANG, S KRAVIS, S IRRGANG, G THONHAUSER, A WRIGHTSTONE 16 E NAKAGAWA, M AGAWANI, P LOLLBACK, T GABLER, E MAIDLA SPE 71419

Fig. 17. Net Present Value for discount rates from 8 to 14% for Scenarios 1 and 2. The cash flow distributions for each geological and/or reservoir uncertainty can be risk weighted to derive the risked value for that development scenario. This process can then be repeated for each field development scenario proposed, to identify the most attractive and robust scheme.

Fig. 16. Results of simulation of production curves from combined Potoroo wells. The left hand panel shows the field production in thousands of stock tank barrels per day for Scenario 1 and the right hand panel, the production for Scenario 2. The green regions show the 5% and 95% confidence limits.

Scenario 1: NPV $MM at Discount Rates 8-14%

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