Surveillance programs play an important role in reservoir management and are crucial for minimizing subsurface risks and improving decision quality. Optimal design and selection of the surveillance plan requires predicting the performance (e.g. in terms of the expected amount of uncertainty reduction in an objective function) of a given surveillance plan before it is implemented. Because the data from the surveillance program is uncertain at the time of the analysis, multiple history matching runs are required to evaluate the effectiveness of the surveillance program for different plausible realizations of the observed data. As such, the computational cost may be prohibitive as the number of reservoir simulations needed for the multiple history matching runs would be substantial. This paper proposes a framework based on proxies and rejection sampling (filtering) to perform the multiple history matching runs with a manageable number of reservoir simulations. The workflow proposed enables qualitative and quantitative analysis of a surveillance plan. Qualitatively, heavy hitter alignment analysis for the objective function and the observed data provides actionable measures for screening different surveillance designs. Quantitatively, the evaluation of expected uncertainty reduction from different surveillance plans allows for optimal design and selection of surveillance plans.

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