--> --> 3D Geologic Modeling and Integration of Diagenesis Predictions for Carbonate Reservoirs, by Hsin-Yi Tseng, Sean A. Guidry, Gregory S. Benson, and Linda W. Corwin; #90052 (2006)

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3D Geologic Modeling and Integration of Diagenesis Predictions for Carbonate Reservoirs

Hsin-Yi Tseng1, Sean A. Guidry1, Gregory S. Benson1, and Linda W. Corwin2
1 ExxonMobil Upstream Research Company, Houston, TX
2 ExxonMobil Exploration Company, Houston, TX

Although every hydrocarbon field is unique and the 3D geologic modeling (3DGM) processes for carbonate reservoirs are currently evolving, we established a general workflow for modeling carbonate reservoirs that can be adapted and applied to other cases. We developed new methodologies for modeling both the data-rich mature fields in production stage and the data-poor fields in exploration and development phases. For a data-rich scenario, the first step in the modeling workflow was to build the structural and stratigraphic framework. A depositional facies model and a diagenetic overprints model followed this. These two were then combined to form a reservoir-rock-type (RRT) facies model. The matrix porosity and permeability models were created within each RRT using property data specific to each RRT facies. Various geostatistical routines such as Sequential Indicator Simulation, Sequential Gaussian Simulation and other mathematical model operations were applied to populate the model with well data, variograms, environments of deposition polygons, local varying azimuth grids, target histograms, probability grids interpreted by geologists for each grain fabric type and diagenetic facies, porosity and permeability attributes. For a data-poor scenario, the probabilistic predictions of diagenetic overprints based on a first-principle approach using Bayesian Belief Networks were integrated into the modeling workflow. A numerical process-based forward model of the distribution of the hydrological zones in a carbonate platform (CARB+) was used to define the spatial distribution of diagenetic effects. The methodologies developed in this 3DGM work would result in better capabilities for flow simulation and production forecasting for carbonate reservoirs.