Recently, the team of Zhang Kai has made new progress in the field of reservoir history matching, and the related research has been published in the Mathematical Geosciences. The paper is titled "A Local Parameterization-Based Probabilistic Cooperative Coevolutionary Algorithm for History Matching".
Innovation: Traditional history matching methods require multiple adjustments of uncertain parameters in the model to conduct reservoir numerical simulation in order to fit the historical production data and obtain the inverted reservoir model. This process is time-consuming, mainly due to the large number of variables in the reservoir model, which can typically reach millions in actual reservoir models. In this study, we address the above challenges by introducing the "divide and conquer" approach, decomposing the reservoir model into regional dimensions, and combining cooperative coevolutionary algorithms for efficient optimization. This method demonstrates good convergence and suitability for high-dimensional problems.
Abstract: 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.
Mathematical Geosciences is an international academic journal published by Springer, founded in 1969. The main research areas of this journal include mathematical modeling and analysis in various aspects of earth sciences such as geology, geophysics, geochemistry, and geobiology. The journal's latest impact factor is 2.6, with a 3-year average impact factor of 2.56. This journal currently appears in the JCR Q2 or the Chinese Academy of Sciences ranking 3.
Paper link:
https://doi.org/10.1007/s11004-023-10069-7
Citation:
Zhang, J., Guo, X., Zhao, Z. et al. A Local Parameterization-Based Probabilistic Cooperative Coevolutionary Algorithm for History Matching. Math Geosci (2023). https://doi.org/10.1007/s11004-023-10069-7.
