This paper proposes a framework based on proxies and rejection sampling (filtering) to perform multiple history-matching runs with a manageable number of reservoir simulations. The proposed work flow 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.
In this work flow, the authors perform a set of training simulations (determined by experimental design) and use the result to build proxies for the objective function and each of the surveillance data points. Proxies are then used to generate a number of plausible realizations of the surveillance data. Then, in turn, one of the plausible data realizations is assumed to be the true data to be observed, and a history-matching run is performed to assimilate these ”true” data using proxy-based rejection sampling to establish the corresponding posterior distribution. The process is repeated for all plausible surveillance-data realizations, to obtain a set of plausible posterior distributions (one for each data realization). The amount of expected uncertainty reduction is obtained by comparing the amount of uncertainty in the prior distribution and the average amount of uncertainty in the posterior distribution. To the best of the authors’ knowledge, this is the first attempt for a priori surveillance analysis by multiple history matching under data uncertainty by use of rejection sampling and proxies....
Model-Based Evaluation of Surveillance-Program Effectiveness With Proxies
01 September 2015