Integration of facies models in reservoir simulation




Chang, Lin

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The primary controls on subsurface reservoir heterogeneities and fluid flow characteristics are sedimentary facies architecture and petrophysical rock fabric distribution in clastic reservoirs and in carbonate reservoirs, respectively. Facies models are critical and fundamental for summarizing facies and facies architecture in data-rich areas. Facies models also assist in predicting the spatial architectural trend of sedimentary facies in other areas where subsurface information is lacking. The method for transferring geological information from different facies models into digital data and then generating associated numerical models is called facies modeling or geological modeling. Facies modeling is also vital to reservoir simulation and reservoir characterization analysis. By extensively studying and reviewing the relevant research in the published literature, this report identifies and analyzes the best and most detailed geologic data that can be used in facies modeling, and the most current geostatistical and stochastic methods applicable to facies modeling. Through intensive study of recent literature, the author (1) summarizes the basic concepts and their applications to facies and facies models, and discusses a variety of numerical modeling methods, including geostatistics and stochastic facies modeling, such as variogram-based geostatistics modeling, object-based stochastic modeling, and multiple-point geostatistics modeling; and (2) recognizes that the most effective way to characterize reservoir is to integrate data from multiple sources, such as well data, outcrop data, modern analogs, and seismic interpretation. Detailed and more accurate parameters using in facies modeling, including grain size, grain type, grain sorting, sedimentary structures, and diagenesis, are gained through this multidisciplinary analysis. The report concludes that facies and facies models are scale dependent, and that attention should be paid to scale-related issues in order to choose appropriate methods and parameters to meet facies modeling requirements.



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