Multiple-Point Geostatistical Simulation of Nonstationary Sedimentary Facies Models Based on Fuzzy Rough Sets and Spatial-Feature Method

3D simulation of sedimentary facies using seismic data is vital for reservoir evaluation and estimation of oil and gas reserves. As a result of the high nonstationarity of sedimentary facies and the highly nonlinear characteristics of seismic attributes, the mapping relationship with sedimentary facies has certain ambiguities and uncertainties that affect the modeling results of sedimentary facies. Multipoint geostatistics (MPS) has proven to be an effective technique for modeling subsurface geological bodies. However, the conventional MPS only deals with stationary applications, and its capability of revealing the distribution of sedimentary facies is thereby limited. In addition, the task of seismic attributes selection, which significantly affects the performance of the modeling method in simulating the distribution of sedimentary facies, is difficult because the relationship between sedimentary facies and seismic attributes is complex. This article presents a nonstationary modeling method for simulating the distribution of sedimentary facies, which is featured by the multiscale spatial feature of patterns. In particular, the spatial location of the patterns is introduced as auxiliary information in the classification and simulation processes. The method incorporates multiscale results during the modeling procedure. Patterns from the multicategory training images (TIs) are classified by the optimized workflow. The seismic attribute selection is achieved by using fuzzy-rough sets. The proposed simulation method is verified by two typical TIs, followed by applications to predict the actual distribution of sedimentary facies. Compared with the filter-based pattern simulation (FILTERSIM) approach, the proposed simulation method is applicable for revealing detailed subsurface models, especially under complex geological conditions and limited information.

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