A hybrid particle swarm optimization–neural network strategy for permeability estimation of the reservoir
Reza Nasimi, Mehdi Shahbazian, Rasoul Irani · 2010
In this work we investigate how the integration of back propagation with particle swarm optimization (PSO) improves the reliability and prediction capability of artificial neural networks. This strategy is applied to predict permeability of Mansuri Bangestan reservoir located in Ahwaz, Iran utilizing available geophysical well log data. Our methodology utilizes a hybrid particle swarm optimization–back propagation strategy (PSO–BP). The particle swarm optimization algorithm has been proved to converge rapidly during the initial stages of a global search, but around global optimum, the search process will become very slow. On the contrary, the gradient descending method can achieve faster convergent speed around global optimum, with higher accuracy. PSO is used to decide the initial weights of the gradient decent methods intelligently. The experimental results show that the proposed hybrid PSO– BP algorithm reveals better performance than the conventional BP algorithm for permeability estimation.