Field applications of a multi-scale multi-physics history matching approach
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Copyright 2015 Society of Petroleum Engineers. Reliable early characterization of global reservoir connectivity is critical for improving field development plans. A main difficulty in identification of field-scale reservoir connectivity is the discrepancy between the low resolution of available field-scale pressure data and the high resolution of geologic models. In this paper, we propose a workflow for integration of pressure data for estimating large-scale reservoir connectivity. Since pressure variation represents a smooth function, we adopt an extremely low resolution (coarse scale) grid system for reservoir simulation. We generate the grid system through Delaunay triangulation by using the location of the static pressure measurements as control points and distribute the unstructured grid blocks according to the spatial resolution of the observations. Using flow-based upscaling, we create the initial coarse scale static simulation model from fine-scale geological model. Then, we use the ensemble Kalman filter to automatically adjust the global parameters such as aquifer strength, global continuity/discontinuity of reservoir properties, and fault transmissibilities to match the static pressure. The important advantages of the proposed workflow for characterization of field-scale reservoir connectivity from pressure data include very fast connectivity estimation with a low-order model and effective parameterization to reduce the number of unknowns to a level commensurate with the available static pressure measurements. We successfully apply our framework to data from real fields to illustrate its suitability and application to realistic reservoirs. To verify the performance of our method we demonstrate the compatibility of the estimation results with the existing geological evidence.