Experimental Design and Analysis Methods for Assessing Volumetric Uncertainties
- Yaw Peng Cheong (Curtin U. of Technology) | Ritu Gupta (Curtin U. of Technology)
- Document ID
- Society of Petroleum Engineers
- SPE Journal
- Publication Date
- September 2005
- Document Type
- Journal Paper
- 324 - 335
- 2005. Society of Petroleum Engineers
- 5.6.1 Open hole/cased hole log analysis, 1.2.3 Rock properties, 5.6.3 Deterministic Methods, 5.5.8 History Matching, 4.1.5 Processing Equipment, 5.6.9 Production Forecasting, 5.1.5 Geologic Modeling, 4.1.2 Separation and Treating, 1.6.9 Coring, Fishing, 5.7 Reserves Evaluation, 3.3.6 Integrated Modeling, 5.5 Reservoir Simulation, 7.2.2 Risk Management Systems
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Experimental design and analysis (EDA) methods can be used practically tominimize the number of 3D geological models that must be built to capture andassess the significant effects in multiple deterministic (or scenario)modeling. This study investigates the feasibility of EDA methods by using threeexamples. It includes discussions and guidelines on how to select efficientdesign matrices by using expert knowledge (the possible effects of anexperiment) and a decision tree, and how the experimental response can befitted accurately with the response surface method to develop a good surrogateequation.
EDA methods have been shown in the literature to have significant potentialin recoverable reserves uncertainty studies. For example: screening andsensitivity studies in recoverable reserves1-4 and in history matching5;production forecasting and estimating ultimate recovery (UR) curves1-3,6-13;and field development optimization.14,15 In these studies, a design matrix isused to obtain the experimental response (i.e., UR). A surrogate equation,which is in the form of a simple mathematical function (often withnonlinearities), is then developed to replace the experiment (3D numericalreservoir simulator). The challenge is to generate an accurate surrogateequation using a design matrix with a small number of design runs. In thisstudy, it is found that expert knowledge can be used effectively to achievethis objective.
This study shows how the EDA methods should be used in multipledeterministic (or scenario) modeling16 to study the hydrocarbon in-place volume(VHCIP) of a reservoir. This is important especially during the exploration orearly appraisal stage, where the amount of data is not sufficient formeaningful 3D numerical reservoir simulations. Multiple deterministic modelingis being used more frequently as higher-risk marginal fields are developed.Theoretically, it is better than a probabilistic approach (e.g., Monte Carlosimulation17) in the investigation of VHCIP because it is based on a geologicalrepresentation of the reservoir, which can be used for field developmentplanning and the like. However, it is not practical because a large number ofmodels must be built to generate a VHCIP distribution curve (similar to thatderived from the probabilistic approach).
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1. White, C.D. et al.: "Identifying and EstimatingSignificant Geologic Parameters With Experimental Design," SPEJ (September2001) 311.
2. Zabalza-Mezghani, I., Manceau, E., and Roggero, F.: "A new approach forquantifying the impact of geostatistical uncertainty of production forecast:the joint modeling method," paper presented at the 2001 Annual Conference ofthe Intl. Assn. for Mathematical Geology, Cancún, Mexico, 6-12 September.
3. Corre, B. et al.: "Integrated Uncertainty Assessment forProject Evaluation and Risk Analysis ," paper SPE 65205 presented at the2000 SPE European Petroleum Conference, Paris, 24-25 October.
4. Kjønsvik, D., Doyle, J., and Jacobsen, T.: "The Effects of SedimentaryHeterogeneities on Production From a Shallow Marine Reservoir—What ReallyMatters?," paper SPE 28445 presented at the 1994 SPE Annual TechnologyConference and Exhibition, New Orleans, 25-28 September.
5. Zabalza-Mezghani, I., Mezghani, M., and Blanc, G.: "Constraining Reservoir Facies Modelsto Dynamic Data—Impact of Spatial Distribution Uncertainty on ProductionForecasts," paper SPE 71335 presented at the 2001 SPE Annual TechnicalConference and Exhibition, New Orleans, 30 September-3 October.
6. Damsleth, E., Hage, A., and Volden, R.: "Maximum Information at Minimum Cost:A North Sea Field Development Study With an Experimental Design," JPT(December 1992) 1350.
7. Egeland, T., Holden, L., and Larsen, E.A.: "Designing Better Decisions,"paper SPE 24275 presented at the 1992 SPE European Petroleum ComputerConference, Stavanger, 24-27 May.
8. Wang, F., and White, C.D.: "Designed Simulation for a Detailed 3DTurbidite Reservoir Model," paper SPE 75515 presented at the 2002 SPE GasTechnology Symposium, Calgary, 30 April-2 May.
9. van Elk, J.F. et al.: "Improved Uncertainty Management inField Development Studies Through the Application of the Experimental DesignMethod to the Multiple Realizations Approach," paper SPE 64462 presented atthe 2000 SPE Asia Pacific Oil and Gas Conference, Brisbane, 16-18 October.
10. Friedmann, F., Chawathé, A., and Larue, D.K.: "Assessing Uncertainty in ChannelizedReservoirs Using Experimental Designs ," SPEREE (August 2003) 264.
11. Friedmann, F., Chawathe, A., and Larue, D.K.: "Uncertainty assessment ofreservoir performance using experimental designs," Paper 2001-170 presented atthe Petroleum Soc.'s 2001 Canadian Intl. Petroleum Conference, Calgary, 12-14June.
12. Venkataraman, R.: "Application of the Method ofExperimental Design to Quantify Uncertainty in Production Profiles," paperSPE 59422 presented at the 2000 SPE Asia Pacific Conference on IntegratedModeling for Asset Management, Yokohama, 25-26 April.
13. Chewaroungroaj, J., Varela, O.J., and Lake, L.W.: "An Evaluation of Procedures toEstimate Uncertainty in Hydrocarbon Recovery Predictions," paper SPE 59449presented at the 2000 SPE Asia Pacific Conference on Integrated Modeling forAsset Management, Yokohama, 25-26 April.
14. Aanonsen, S.I. et al.: "Optimizing Reservoir PerformanceUnder Uncertainty With Application to Well Location," paper SPE 30710presented at the 1995 SPE Annual Technical Conference and Exhibition, Dallas,22-25 October.
15. Dejean, J.-P., and Blanc, G.: "Managing Uncertainties on ProductionPredictions Using Integrated Statistical Methods," paper SPE 56696presented at the 1999 SPE Annual Technical Conference and Exhibition, Houston,3-6 October.
16. Grötsch, J., and Mercadier, C.: "Integrated 3-D reservoir modeling basedon 3-D seismic: the Tertiary Malampaya and Camago buildups, offshore Palawan,Phillipines," AAPG Bulletin (1999) 83, 1703.
17. Murtha, J.A.: "Monte CarloSimulation: Its Status and Future," JPT (April 1997) 361
18. Charles, T. et al.: "Experience With the Quantification ofSubsurface Uncertainties," paper SPE 68703 presented at the 2001 SPE AsiaPacific Oil and Gas Conference and Exhibition, Jakarta, 17-19 April.
19. Morris, M.D.: "Three Technometrics experimental design classics,"Technometrics (2000) 42, 2.
20. Montgomery, D.C.: Design and Analysis of Experiments, John Wiley andSons, New York City (2001).
21. Montgomery, D.C. and Peck, E.A.: Introduction to Linear RegressionAnalysis, John Wiley and Sons, New York City (1992).
22. Plackett, R.L. and Burman, J.P.: "The design of optimal multifactorialexperiments," Biometrika (1946) 33, 305.
23. Simpson, T.W. et al.: "Kriging models for global approximation insimulation-based multidisciplinary design optimization," American Institute ofAeronautics and Astronautics Journal (2001) 39, 2233.
24. Smith, G.C. et al.: "The Chinguetti deepwater turbidite field,Mauritania: reserve estimation and field development using uncertaintymanagement and experimental designs for multiple scenario 3D models," APPEAJournal (2004) 521.
25. Ye, K.Q., Li, W., and Sudjianto, A.: "Algorithmic construction ofoptimal symmetric latin hypercube designs," Journal of Statistical Planning andInference (2000) 145.
26. Weber, K.J. and van Geuns, L.C.: "Framework for Constructing ClasticReservoir Simulation Models," JPT (October 1990) 1248; Trans., AIME,289.