Journal
JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING
Volume 112, Issue -, Pages 17-23Publisher
ELSEVIER
DOI: 10.1016/j.petrol.2013.11.009
Keywords
permeability; neural network; cuckoo optimization algorithm; particle swarm optimization; imperialist competitive algorithm; well logs data
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Funding
- National Iranian oil company (NIOC)
- National Iranian South Oilfields Company (NISOC)
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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. (C) 2013 Elsevier B.V. All rights reserved.
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