Simultaneous Integration of Pressure, Water Cut,1 and 4-D Seismic Data In Geostatistical Reservoir Modeling
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Simultaneous Integration of Pressure, Water Cut, 1 and 4-D Seismic Data In Geostatistical Reservoir Modeling1 Xian-Huan Wen,2 Seong Lee,2 and Tina Yu2 A geostatistically-based inverse technique, the sequential-self calibration (SSC) method, is used to update reservoir models so that they match observed pressure, water cut and time-lapse water saturation derived from 4-D seismic. Within the SSC, a steady-state genetic algorithm (GA) is applied to search the optimal master point locations, as well as the associated optimal permeability perturbations at the master locations. GA provides significant flexibility for SSC to parameterize master point locations, as well as to integrate different types of dynamic data because it does not require sensitivity coefficients. We show that the coupled SSC/GA method is very robust. Integrating dynamic data can significantly improve the characterization of reservoir heterogeneity with reduced uncertainty. Particularly, it can efficiently identify important large-scale spatial variation patterns (e.g., well connectivity, near well averages, high flow channels and low flow barriers) embedded in the reservoir heterogeneity. Using dynamic data, however, could be difficult to reproduce the permeability values on the cell-by-cell basis for the entire model. This reveals the important evidence that dynamic data carry information about large-scale spatial variation features, while they may be not sufficient to resolve the individual local values for the entire model. Through multiple realization analysis, the large-scale spatial features carried by the dynamic data can be extracted and represented by the ensemble mean model. Furthermore, the region informed by the dynamic data can be identified as the area with significant reduced variances in the ensemble variance model. Within this region, the cell-by-cell correlation between the true and updated permeability values can be significantly improved by integrating the dynamic data. KEY WORDS: history match; sequential-self calibration; genetic algorithm; production data integration; information of production data.
INTRODUCTION Dynamic data are the time dependent measurements of flow responses that are related to the reservoir properties through the flow equations, such as pressure, flow rate, fractional flow rate, saturation or 4-D seismic data. Various kind of dynamic data are usually available for most reservoirs under production. Integration 1Received
8 June 2004; accepted 8 July 2005; Published online: 27 May 2006.
2Chevron Energy Technology Company, 6001 Bollinger Canyon Rd., San Ramon, CA 94583, U.S.A.;
e-mail: [email protected] 301 C 2006 International Association for Mathematical Geology 0882-8121/06/0400-0301/1
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Wen, Lee, and Yu
of dynamic data in reservoir model requires that the solutions of flow equations based on the reservoir model are in reasonable agreement with actual measurement of those data. Traditionally, this integration is done through a history match procedure. In current industry practice, history matchin
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