Research on Reservoir History Matching Method Based on Real⁃Coded Genetic Algorithm and Connectivity Model - Ainiwaer AILIYAER; Chunli ZHAO; Feng LIU (Liaoning Shiyou Huagong Daxue xuebao, 2025)
The optimization process in reservoir history matching belongs to the high⁃dimensional system's optimal control problem, and the selection of a suitable optimization algorithm is crucial for achieving a good fitting effect. As gradient⁃based methods face challenges in computing the gradient of the objective function, intelligent optimization algorithms with stochastic properties are widely applied in reservoir optimization processes. A method for reservoir history matching based on real⁃number coding genetic algorithm RGA and connectivity model was proposed. This method eliminates the need for encoding and decoding operations by directly using feasible solutions obtained from traditional solving methods as initial parameters for the improved genetic algorithm, thereby reducing the complexity of the search space. In RGA, real⁃number coding is employed to represent parameters, enabling the algorithm to handle continuous variables directly, thus enhancing search accuracy and convergence speed. A adaptive selection strategies, crossover, and mutation operations are introduced in this paper to further enhance the algorithm's performance. Application of RGA to the history matching problem in a mechanistic model demonstrates that RGA can effectively improve fitting results and find relatively optimal solutions in a short time. Therefore, this method has significant potential for widespread application in reservoir history matching problems.