Отправлено: 26.04.13 12:27. Заголовок: Improving Performance of a Neural Network Model by Artificial Ant Colony Optimization for Predicting

Middle-East Journal of Scientific Research 13 (9): 1217-1223, 2013
ISSN 1990-9233
© IDOSI Publications, 2013
DOI: 10.5829/idosi.mejsr.2013.13.9.927
Corresponding Author: Amir Hatampour, Faculty of Chemical Engineering, Dashtestan branch of Islamic Azad University,
Dashtestan, Iran, Tel: 0098 939 160 5121.
1217
Improving Performance of a Neural Network Model by Artificial Ant Colony Optimization for Predicting Permeability of Petroleum Reservoir Rocks
Amir Hatampour, Rasul Razmi and Mohammad Hossein Sedaghat
Faculty of Chemical Engineering, Dashtestan Branch of Islamic Azad University, Dashtestan, Iran
Abstract: Over recent years, oil and gas exploration has been beloved due to extended needs for petroleum and
energy sources. In this way, the capabilities of intelligent techniques are studied by researchers in different
fields of petroleum industry and geosciences, whereas it seems that these techniques can improve the
prediction accuracy of exploration and production of the hydrocarbon reservoir. The performance indices of
the artificial models have proven to be better than the conventional linear and non-linear statistical models.
Artificial intelligence models are extremely useful for reservoir characterization, which requires a high-accuracy
prediction for a good exploitation of the oil and gas resources. The present paper introduces an optimized
neural network (NN) for predicting permeability based on a relatively new swarm intelligent approach, named
ant colony optimization. Ant colony optimization (ACO) is a relatively new computational intelligent approach,
which was initially inspired by the observation of ants. The number of neurons in hidden layer, weights and
bias are optimized in the proposed NN using the ACO. The data of a gas reservoir in the South Pars Gas Field
of Iran was used for analyzing the accuracy of optimized NN in a real case study. Finally, to clarify the
advantages of the optimized NN, its outcomes were compared with the results of a simple NN model, in which
the aforementioned parameters were determined through a try-and-error process. The MSEs of the optimized
and simple NN were equal to 7.95 md and 12.84 md, respectively, which correspond to the correlation coefficient
(R) of 0.94 and 0.866, respectively.