History matching of an Oil Reservoir using Non-dominated Sorting Genetic Algorithm-II coupled with Sequential Gaussian Simulation

History matching, Reservoir modeling, and production projection help with effective petroleum exploration management. These reservoirs are nonlinear and heterogeneous in nature. Obtaining credible calculates of the spatial distribution of the parameters of the reservoir and related production profiles is frequently challenging. The goal of this research is to use Non-dominated Sorting Genetic Algorithm-II (NSGA-II) and Sequential Gaussian Simulation (SGSIM) to history-match an oil reservoir. The normalized sum-of-square errors for history matching is taken as objective function. A case study is chosen and the defined objective function is used to optimize the parameters. This article analyzes the application of NSGA-II, with larger number of variables, and NSGA-II coupled with Sequential Gaussian Simulation (SGSIM), in which number of variables is drastically reduced, for the same case study.