A reservoir skeleton-based multiple point geostatistics method

Traditional stochastic reservoir modeling, including object-based and pixel-based methods, cannot solve the problem of reproducing continuous and curvilinear reservoir objects. The paper first dives into the various stochastic modeling methods and extracts their merits, then proposes the skeleton-based multiple point geostatistics (SMPS) for the fluvial reservoir. The core idea is using the skeletons of reservoir objects to restrict the selection of data patterns. The skeleton-based multiple point geostatistics consists of two steps. First, predicting the channel skeleton (namely, channel centerline) by using the method in object-based modeling. The paper proposes a new method of search window to predict the skeleton. Then forecasting the distributions of reservoir objects using multiple point geostatistics with the restriction of channel skeleton. By the restriction of channel centerline, the selection of data events will be more reasonable and the realization will be achieved more really. The checks by the conceptual model and the real reservoir show that SMPS is much better than Sisim (sequential indicator simulation), Snesim (Single Normal Equation Simulation) and Simpat (simulation with patterns) in building the fluvial reservoir model. This new method will contribute to both the theoretical research of stochastic modeling and the oilfield developments of constructing highly precise reservoir geological models.