A Comparative Study of Genetic and Particle Swarm Optimization Algorithms and Their Hybrid Method in Water Flooding Optimization

Abstract

Optimization of oil production from petroleum reservoirs is an interesting and complex problem which can be done by optimal control of well parameters such as their flow rates and pressure. Different optimization techniques have been developed yet, and metaheuristic algorithms are commonly employed to enhance oil recovery projects. Among different metaheuristic techniques, the genetic algorithm (GA) and the particle swarm optimization (PSO) have received more attention in engineering problems. These methods require a population and many objective function calls to approach more the global optimal solution. However, for a water flooding project in a reservoir, each function call requires a long time reservoir simulation. Hence, it is necessary to reduce the number of required function evaluations to increase the rate of convergence of optimization techniques. In this study, performance of GA and PSO are compared with each other in an enhanced oil recovery (EOR) project, and Newton method is linked with PSO to improve its convergence speed. Furthermore, hybrid genetic algorithm-particle swarm optimization (GA-PSO) as the third optimization technique is introduced and all of these techniques are implemented to EOR in a water injection project with 13 decision variables. Results indicate that PSO with Newton method (NPSO) is remarkably faster than the standard PSO (SPSO). Also, the hybrid GA-PSO method is more capable of finding the optimal solution with respect to GA and PSO. In addition, GA-PSO, NPSO, and GA-NPSO methods are compared and, respectively, GA-NPSO and NPSO showed excellence over GA-PSO.