Multi-objective global and local Surrogate-Assisted optimization on polymer flooding

Abstract

Oil production and polymer injection are two performance indicators of polymer flooding and are usually conflicting objectives. In order to obtain optimal trade-off solutions, this paper proposes a multi-objective global and local surrogate-assisted particle swarm optimization (MO-GLSPSO) method, which consists of alternative steps: global population prescreen and local population search.The global steps use generalized regression neural network (GRNN) to prescreen a better population, and the local steps use radial basis function (RBF) as proxy to search for the next generation. The global steps aim to reduce the chance of generations being trapped in local minima, and the local steps obtain the optimal solutions with a fast convergence rate. The rates (liquid production rate and water injection rate) and polymer injection concentration of wells are tuned to obtain a Pareto-front that maximizes cumulative oil production and minimizes cumulative polymer injection.The MO-GLSPSO method is tested using both synthetic and Brugge benchmark cases. The iterations generally improve the oil production or reduce polymer injection and are stabilized at a Pareto-front of the two objectives. Improved sweep efficiency and polymer utility are also observed in the optimal results. The proposed method is also compared with other two methods, multi-objective genetic algorithm (MOGA) and multi-objective particle swarm optimization (MOPSO), to examine the pros and cons. The results indicate that MO-GLSPSO has a better pareto-front than others.