A Practical Data Integration Approach to History Matching: Application to a Deepwater Reservoir
- B. Todd Hoffman (Montana Tech) | Jef K. Caers (Stanford University) | Xian-Huan Wen (Chevron Corp.) | Sebastien B. Strebelle (Chevron ETC)
- Document ID
- Society of Petroleum Engineers
- SPE Journal
- Publication Date
- December 2006
- Document Type
- Journal Paper
- 464 - 479
- 2006. Society of Petroleum Engineers
- 5.1.5 Geologic Modeling, 5.6.1 Open hole/cased hole log analysis, 2.4.3 Sand/Solids Control, 1.2.3 Rock properties, 6.1.5 Human Resources, Competence and Training, 1.6.9 Coring, Fishing, 5.5.8 History Matching, 7.6.2 Data Integration, 4.1.2 Separation and Treating, 5.8.6 Naturally Fractured Reservoir, 4.3.4 Scale, 4.1.5 Processing Equipment, 5.1 Reservoir Characterisation, 5.5 Reservoir Simulation, 5.1.2 Faults and Fracture Characterisation
- 2 in the last 30 days
- 615 since 2007
- Show more detail
- View rights & permissions
|SPE Member Price:||USD 10.00|
|SPE Non-Member Price:||USD 30.00|
This paper presents an innovative methodology to integrate prior geologic information, well-log data, seismic data, and production data into a consistent 3D reservoir model. Furthermore, the method is applied to a real channel reservoir from the African coast. The methodology relies on the probability-perturbation method (PPM). Perturbing probabilities rather than actual petrophysical properties guarantees that the conceptual geologic model is maintained and that any history-matching-related artifacts are avoided. Creating reservoir models that match all types of data are likely to have more prediction power than methods in which some data are not honored. The first part of the paper reviews the details of the PPM, and the next part of this paper describes the additional work that is required to history-match real reservoirs using this method. Then, a geological description of the reservoir case study is provided, and the procedure to build 3D reservoir models that are only conditioned to the static data is covered. Because of the character of the field, the channels are modeled with a multiple-point geostatistical method. The channel locations are perturbed in a manner such that the oil, water, and gas rates from the reservoir more accurately match the rates observed in the field. Two different geologic scenarios are used, and multiple history-matched models are generated for each scenario. The reservoir has been producing for approximately 5 years, but the models are matched only to the first 3 years of production. Afterward, to check predictive power, the matched models are run for the last 1½ years, and the results compare favorably with the field data.
Reservoir models are constructed to better understand reservoir behavior and to better predict reservoir response. Economic decisions are often based on the predictions from reservoir models; therefore, such predictions need to be as accurate as possible. To achieve this goal, the reservoir model should honor all sources of data, including well-log, seismic, geologic information, and dynamic (production rate and pressure) data.
Incorporating dynamic data into the reservoir model is generally known as history matching. History matching is difficult because it poses a nonlinear inverse problem in the sense that the relationship between the reservoir model parameters and the dynamic data is highly nonlinear and multiple solutions are avail- able. Therefore, history matching is often done with a trial-and-error method.
In real-world applications of history matching, reservoir engineers manually modify an initial model provided by geoscientists until the production data are matched. The initial model is built based on geological and seismic data. While attempts are usually made to honor these other data as much as possible, often the history-matched models are unrealistic from a geological (and geophysical) point of view. For example, permeability is often altered to increase or decrease flow in areas where a mismatch is observed; however, the permeability alterations usually come in the form of box-shaped or pipe-shaped geometries centered around wells or between wells and tend to be devoid of any geologica.considerations. The primary focus lies in obtaining a history match.
|File Size||4 MB||Number of Pages||16|
Caers, J. 2003. HistoryMatching Under Training-Image-Based Geological Model Constraints.SPEJ 8 (3): 218-226. SPE-74716-PA. DOI: 10.2118/74716-PA.
Damsleth, E., Hage, A., and Volden, R. 1992. Maximum Information at Minimum Cost:A North Sea Field Development Study With Experimental Design. JPT44 (12): 1350-1360. SPE-23139-PA. DOI: 10.2118/23139-PA.
Deutsch, C.V. and Journel, A.G. 1998. GSLIB: Geostatistical SoftwareLibrary and User's Guide. Second edition. New York: Oxford U. Press.
Geman, S. and Geman, D. 1984. Stochastic relaxation, Gibb distribution, andthe Bayesian restoration of images. IEEE Transactions on Pattern Analysisand Machine Learning 14: 721-741.
Hoffman, B.T. 2005. Geologically Consistent History Matching whilePerturbing Facies. http://geothermal.stanford.edu/pereports/PhD/Hoffman05.pdf. PhDdissertation. Stanford U., Stanford, California.
Hoffman, B.T. and Caers, J. 2005. Regional probability perturbations forhistory matching. Journal of Petroleum Science and Engineering46: 53-71.
Journel, A.G. 2002. Combining Knowledge from Diverse Sources: An Alternativeto Traditional Data Independence Hypothesis. Mathematical Geology34 (5): 573-596. DOI: http://dx.doi.org/10.1023/A:1016047012594.
Krishnan, S. 2004. The Tau Model to Integrate Prior Probabilities. PhDdissertation. Stanford U., Stanford, California.
Landa, J.L. 2001. Technique ToIntegrate Production and Static Data in a Self-Consistent Way. Paper SPE71597 presented at the Annual Technical Conference and Exhibition, New Orleans,30 September-3 October SPE-71597-MS. DOI: 10.2118/71597-MS.
Mohaghegh, S.D. 2000. Virtual-Intelligence Applications inPetroleum Engineering: Part 1—Artificial Neural Networks. JPT52 (9): 64-73. SPE-58046-PA. DOI: 10.2118/58046-PA.
Press, W.H., Teukolsky, S.A., Vetterling, W.T., and Flannery, B.P. 1989.Numerical Recipes in Fortran. Cambridge: Cambridge U. Press.
Roggero, F. and Hu, L.Y. 1998. Gradual Deformation of ContinuousGeostatistical Models for History Matching. Paper SPE 49004 prepared forpresentation at the SPE Annual Technical Conference and Exhibition, NewOrleans, 27-30 September. SPE-49004-MS. DOI: 10.2118/49004-MS.
Simulation Software Manuals, Eclipse Technical Description. 2003.Schlumberger.
Strebelle, S. 2002. Conditional Simulation of Complex Geological StructuresUsing Multiple-Point Statistics. Mathematical Geology 34 (1):1-21. DOI: http://dx.doi.org/10.1023/A:1014009426274.
Suzuki, S., Daly, C., Caers, J., and Mueller, D. 2005. History Matching of NaturallyFractured Reservoirs Using Elastic Stress Simulation and ProbabilityPerturbation Method. Paper SPE 95498 presented at the Annual TechnicalConference and Exhibition, Dallas, 9-12 October. SPE-95498-MS. DOI:10.2118/95498-MS.
Wen, X.-H., Deutsch, C.V. and Cullick, A.S. 1998. Integrating Pressure andFractional-Flow Data In Reservoir Modeling With Fast Streamline-Based InverseMethod. Paper SPE 48971 prepared for presentation at the SPE AnnualTechnical Conference and Exhibition, New Orleans, 27-30 September.SPE-48971-MS. DOI: 10.2118/48971-MS.