The Prediction of Permeability Using an Artificial Neural Network System |
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| Authors: | G R Pazuki M Nikookar M Dehnavi B Al-Anazi |
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| Affiliation: | 1. Department of Chemical Engineering , Amirkabir University of Technology , Tehran , Iran;2. R&3. D, IOR Research Institute, National Iranian Oil Company , Tehran , Iran;4. Institute for Nano-Science and Nano-Technology, Sharif University of Technology , Tehran , Iran;5. King Abdulaziz City for Science and Technology, Oil and Gas Center , Riyadh , Saudi Arabia |
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| Abstract: | Abstract The authors studied the efficiency and accuracy of neural network model for prediction of permeability as a key parameter in reservoir characterization. So, some multilayer perceptron (MLP) neural network models with different learning algorithms of Levenberg-Margnardt, back propagation, improved back propagation (IBP), and quick propagation with three layers and different node numbers (3, 4, 5, 6, 7) in the middle layer have been presented. These models have been obtained by 630 permeability data from one of offshore reservoirs located in Saudi Arabia. The accuracy of models was studied by comparing the obtained results of each model with experimental data. So, the neural network with IBP learning method and five nodes in the middle layer has the most accuracy. |
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| Keywords: | artificial neural network modeling permeabililty reservoir |
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