Statistical Ranking of Stochastic Geomodels Using Streamline Simulation: A Field Application

Statistical Ranking of Stochastic Geomodels Using Streamline Simulation: A Field Application James R. Gilman; James R. Gilman iReservoir.com Inc. Search for other works by this author on: This Site Google Scholar Hai-Zui Meng; Hai-Zui Meng iReservoir.com Inc. Search for other works by this author on: This Site Google Scholar Michael J. Uland; Michael J. Uland iReservoir.com Inc. Search for other works by this author on: This Site Google Scholar Peter J. Dzurman; Peter J. Dzurman EnCana (UK) Ltd. Search for other works by this author on: This Site Google Scholar Stevan Cosic Stevan Cosic EnCana Corp. Search for other works by this author on: This Site Google Scholar Paper presented at the SPE Annual Technical Conference and Exhibition, San Antonio, Texas, September 2002. Paper Number: SPE-77374-MS https://doi.org/10.2118/77374-MS Published: September 29 2002 Connected Content Related to: Statistical Ranking of Stochastic Geomodels Using Streamline Simulation Cite View This Citation Add to Citation Manager Share Icon Share Twitter LinkedIn Get Permissions Search Site Citation Gilman, James R., Meng, Hai-Zui, Uland, Michael J., Dzurman, Peter J., and Stevan Cosic. "Statistical Ranking of Stochastic Geomodels Using Streamline Simulation: A Field Application." Paper presented at the SPE Annual Technical Conference and Exhibition, San Antonio, Texas, September 2002. doi: https://doi.org/10.2118/77374-MS Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAll ProceedingsSociety of Petroleum Engineers (SPE)SPE Annual Technical Conference and Exhibition Search Advanced Search AbstractStreamline-based flow simulation for the purpose of ranking large-scale geologic realizations continues to receive significant attention. However, the procedures and the analyses for ranking are not straightforward and therefore actual case examples are very limited.This paper describes a field example showing a very practical process for dynamically ranking various geologic realizations using uniform well patterns. This mature field has a 60-year primary recovery history but still has potential for additional development. The ranking process is further complicated by the presence of a gas cap and a water zone. A major difficulty with dynamic ranking of geological models is that the recovery may be as much a function of the flow-physics as the geologic variability. Accounting for gravity, fluid contacts, changing streamlines, and fractional flow effects may be important to the ranking study. Even the choice of well locations, rates, boundary conditions, and patterns will affect the ranking.The uniform patterns used in this study are not representative of actual well patterns or injected fluids rates. The waterflood efficiency, however, can still be used as a basis of comparison. A novel map based presentation of the ranking simulations provides valuable understanding of the effect of the geologic model on recovery uncertainty. The use of regular well patterns is different from the common approach of using existing wells with pseudo boundary conditions. The uniform spacing ensures complete coverage of the area-of-interest and not just the areas where the model is already conditioned to existing data. This method tests the variability of the models away from existing wells as these areas will have longer-term effect on performance and affect the decision regarding future infill wells and recovery methods.Another important aspect of this paper is a demonstration of how modern tools and analysis techniques are greatly improving the ability to understand complex reservoirs and thus make improved decisions regarding optimum development. Efficient analysis and visualization of the data and interpretations is important for a detailed understanding of the reservoir.Motivation for StudyThe methodologies described here resulted from several major considerations:evaluate the impact of geologic uncertainties on production performance within a one month window during which a conventional history match is performed;use existing commercial software to prevent long delay time in project completion,present the results in a manner which visually relay the results to a wide audience, anddevelop a methodology which provides more information than a simple cumulative distribution of field recovery.Anyone involved in reservoir simulation realizes there are several potential sources of errors or uncertainties when doing a reservoir study:numerical error (from the approximate solution of non-linear partial differential equations),error from the approximations in the underlying equations (e.g. 3-phase approximation of Darcy's law)errors or uncertainties in data interpretation (e.g. converting log signals to reservoir properties),ignored data (e.g. not using the seismic data in reservoir property distribution),unknown or uncertain data (e.g. only a small portion of the reservoir is sampled) andincorrect averaging of data (e.g. averaging log measurements over a flow unit). All of these errors or uncertainties lead to uncertainties in forecasts of future production. Recognition of these uncertainties has lead to a desire to incorporate the resulting uncertain rate and recovery forecasts into a corporate risk analysis methodology1–9. Keywords: modeling & simulation, well pattern, realization, stochastic geomodel, recovery, ranking, upstream oil & gas, streamline simulation, reservoir simulation, waterflood efficiency Subjects: Reservoir Simulation This content is only available via PDF. 2002. Society of Petroleum Engineers You can access this article if you purchase or spend a download.

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