A parallel BOA-PSO hybrid algorithm for history matching

Abstract

In order to make effective decisions regarding the exploitation of oil reservoirs, it is necessary to create and update reservoir models using observations collected over time in a process known as history matching. This is an inverse problem: it requires the optimization of reservoir model parameters so that reservoir simulation produces response data similar to that observed. Since reservoir simulations are computation ally expensive, it makes sense to use relatively sophisticated algorithms. This led to the use of the Bayesian Optimization Algorithm (BOA). However, the high performance of a much simpler algorithm - Particle Swarm Optimization (PSO) - led to the development of a BOA-PSO hybrid that outperformed both BOA and PSO on their own.