Integration of Niching Technique and Surrogate-assisted Method with Particle Swarm Optimization for History Matching

History matching can provide reliable numerical models for reservoir management and development by assimilating the historical production data into prior geological realizations. It is usually a typical inverse problem with multiple solutions. However, efficiently obtaining multiple posterior solutions is still challenging for most existing history matching algorithms. In this paper, we present a novel algorithm to tackle this problem, which integrates the niching technique and surrogate-assisted method into the particle swarm optimization (PSO), in which, the niching technique can improve the exploration ability and maintain the diversity of the population, while the surrogate-assisted method is focused on accelerating the convergence. Additionally, the convolutional variational autoencoder (CVAE), a deep learning model, is adopted to map the high-dimensional spatially uncertain parameters such as permeability and porosity to low-dimensional latent variables. Experimental results show that the proposed algorithm has good convergence and sampling ability for history matching problems.