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Stochastic 3D Geological Modeling of Deep Water Reservoirs: Methodology and Case Studies
Various turbidite facies architectures have been observed in deep water fields offshore Gulf of Mexico including lobe systems, amalgamated channel systems, sheet sand systems, and channel levee systems. The geometric anisotropy and petrophysical properties of the individual facies in the individual depositional systems are different. As such, they need to be captured in the geological model for accurate monitoring of reservoir production performance. Integrated geological, geophysical, and petrophysical studies provide information of reservoir architecture and heterogeneity at different scales. In this study various stochastic techniques have been successfully used to model the varied reservoir architecture in deep water systems.
Different case studies presented here demonstrate the ability of using available stochastic techniques in geological modeling of deep water systems. General Marked Point Process, an object-based modeling technique, with specially designed facies shapes and 3D intensity function are used in a prograding turbidite lobe system to model the feeder channel, lobe and barrier facies. In the channelized turbidite system, fluvial algorithm and a specially designed grid is used to model the channel and overbank facies with curved paleo-flow directions. For petrophysical modeling, pixel-based Sequential Gaussian Simulation algorithm with a specific data transformation and trends can help to capture the variations of petrophysical properties in different facies.
During the modeling process, great efforts were made to optimize the workflow, algorithms and input parameters for each depositional system. Careful quality control was performed to insure that the major facies architecture and petrophysical features are preserved and input data are honored.
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