A Local Parameterization-Based Probabilistic Cooperative Coevolutionary Algorithm for History Matching

History matching, as an essential part of reservoir development, aims to infer high-dimensional geological parameters of a reservoir with a small amount of observation. Despite the rapid development of optimization algorithms, finding optimal solutions for history matching is still challenging because of the large number of parameters that depend on the grid blocks of the numerical simulation model. Motivated by the divide-and-conquer strategy, in this work a novel probabilistic cooperative coevolutionary framework based on local parameterization (LP-PCC) is constructed to improve the convergence of the history matching of large-scale problems. First, the high-dimensional model parameters are decomposed based on smooth local parameterization, in which the divided low-dimensional parameters can reconstruct smooth boundaries of the geological structure during optimization. After that, a contribution-based cooperative coevolutionary algorithm is adopted to optimize the low-dimensional parameters in a round-robin fashion and allocate the computational resources reasonably. To further improve the performance of cooperative coevolution, a new probabilistic method integrated with contribution information is presented to select the subcomponents to be optimized. This framework incorporates domain knowledge for decomposition and a probabilistic mechanism to select subcomponents with large probability, which enhances both convergence and exploration in cooperative coevolution. Two synthetic reservoir cases are designed to validate the effectiveness and efficiency of the proposed method. The numerical results indicate that, compared with traditional strategies, the method can obtain better history-matching results and be adapted to large-scale reservoir problems.