Traditional reconciliation of geomodels with production data is one of the most laborious tasks in reservoir engineering. The uncertainty associated with the great majority of model variables only adds to the overall complexity. This paper introduces an engineering workflow for probabilistic assisted history matching that captures inherent model uncertainty and allows for better quantification of production forecasts.

The workflow is applied to history matching of the pilot area in a major, structurally complex Middle East (ME) carbonate reservoir. The simulation model combines 49 wells in five waterflood patterns to match 50 years of oil production and 12 years of water injection and to predict eight years of production. Initially, the reservoir model was calibrated to match oil production by modifying permeability and/or porosity at well locations and by fine-tuning rock-type properties and water saturation. The second level history match implemented two-stage Markov chain Monte Carlo (McMC) stochastic optimization to minimize the misfit in water cut on a well-by-well basis. While relative to evolutionary algorithms or the ensemble Kalman filter (EnKF), the McMC methods provide a statistically rigorous alternative for sampling posterior distribution; when deployed in direct simulation, they impose a high computational cost. The approach presented here accelerates the process by parameterizing the permeability using discrete cosine transform (DCT), constraining the proxy model using streamline-based sensitivities and utilizing parallel and cluster computing.

While probabilistic assisted history matching (AHM) successfully reduced the misfit for most producing wells, the computational convergence was sensitive to the level of preserved geological detail. The optimal number of representative history-matched models was identified to capture the uncertainty in reservoir spatial connectivity using rigorous optimization and dynamic model ranking based on forecasted oil recovery factors (ORFs). The reduced set of models minimized the computational load for forecast-based analysis, while retaining the knowledge of the uncertainty in the recovery factor.

The comprehensive probabilistic AHM workflow was implemented at the operator's North Kuwait Integrated Digital Oilfield (KwIDF) collaboration center. It delivers an optimized reservoir model for waterflood management and automatically updates the model quarterly with geological, production, and completion information. This allows engineers to improve the reservoir characterization and identify the areas that require more data capture.

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