A comparison study of using optimization algorithms and artificial neural networks for predicting permeability

作者：Kaydani Hossein; Mohebbi Ali^*

来源：Journal of Petroleum Science and Engineering, 2013, 112: 17-23.

DOI：10.1016/j.petrol.2013.11.009

• permeability
• neural network
• cuckoo optimization algorithm
• particle swarm optimization
• imperialist competitive algorithm
• well logs data

This paper presents a novel approach of permeability prediction by combining cuckoo, particle swarm and imperialist competitive algorithms with Levenberg-Marquardt (LM) neural network algorithm in one of heterogeneous oil reservoirs in Iran. First, topology and parameters of the Artificial Neural Network (ANN) as decision variables were designed without the optimization method. Then, in order to improve the effectiveness of forecasting when ANN was applied to a permeability predicting problem, the design was performed using Cuckoo Optimization Algorithm (COA) algorithm. The validation test result from a new well data demonstrated that the trained COA-LM neural model can efficiently accomplish permeability prediction. Also, the comparison of COA with particle swarm optimization and imperialist competitive algorithms showed the superiority of COA on fast convergence and best optimum solution achievement.

• 出版日期2013-12