Inference of Global Reservoir Connectivity from Static Pressure Data with Fast Coarse-Scale Simulation Models
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2018, International Association for Mathematical Geosciences. Characterization of field-scale reservoir connectivity is critical for production optimization and field development planning. The information content of the data corresponding to different physical processes, coupled with the discrepancy in production data and geologic model resolutions, complicate the estimation of global field connectivity. Due to its diffusive nature, pressure varies smoothly in the reservoir, a characteristic that can be adequately captured using coarse-scale description of the connectivity in the reservoir model. On the other hand, flowrate and watercut information are relatively more localized and require higher-resolution models to adequately describe their variations. Hence, pressure and flowrate data carry information pertinent to their respective scales. In this paper, a fast workflow is presented for estimating large-scale reservoir connectivity from static reservoir pressure measurements. For this purpose, a coarse-scale grid system with Delaunay triangulation is developed to represent the global reservoir connectivity. Flow-based upscaling is applied to create the initial coarse-scale static simulation models from geological or simulation models. The ensemble Kalman filter is then applied to calibrate the initial models against static pressure data and to identify field-scale connectivity parameters that control the global pressure distribution, such as aquifer strength, continuity/discontinuity in reservoir properties, and fault transmissibilities. This approach enables fast characterization of field-scale reservoir connectivity from pressure data with a coarser description of the reservoir connectivity, which offers a parameterization level that is commensurate with the resolution and information content of the static pressure measurements. The proposed calibration approach can serve as preconditioning to generate initial reservoir models with consistent global connectivity patterns for full-scale history matching. To illustrate the feasibility and performance of the method, examples are drawn from real field cases in which static pressure data are integrated to infer global reservoir connectivity maps. In this paper, the resulting connectivity maps are used to obtain facies probability maps to generate initial facies models for full-scale history matching.
