In this work we describe a mathematical procedure and associated data-driven workflow for map-based estimation of reservoir pressure and saturation changes using 4D seismic and well production/injection data. The practicality of the approach is illustrated with a field application from an offshore clastic reservoir under waterflood production. The application of the procedure results in maps of estimated reservoir pressure and saturation changes that can be used directly in field reservoir management and history matching of flow simulation models. The outstanding feature of this data-driven approach is the reliance on field-observed data rather than on complex modeling and compute-intensive schemes typically found in classical mathematical inversion approaches. The focus is on joint use of acquired time-lapse (4D) seismic and measured well production/ injection data. Simple mathematical functions are used to model the correlation between 4D seismic and pressuresaturation information. The correlation models are calibrated with well data. Multiple seismic attributes are then used to infer map-based pressure-saturation change information. Use of flow simulation, forward seismic modeling, and rock-physics models is limited to the feasibility analysis stage of the inversion process, where such information is needed to generate synthetic test data. Uncertainty/probabilistic analysis in the map-based estimation of reservoir pressure and saturation changes is performed using the well-known Bayesian framework for inverse problems. The paper adds a new case study to the relatively limited existing body of literature on data-driven methods for pressure-saturation inversion from 4D seismic, where very few such field examples have been published to date. The innovative component in this work consists of a novel application of a data-driven inversion approach that improves the usefulness of the technology in real-data environments.
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