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The Prediction of Permeability Using an Artificial Neural Network System

机译:人工神经网络系统对渗透率的预测

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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.
机译:作者研究了将神经网络模型作为预测储层表征关键参数的渗透率预测的效率和准确性。因此,一些多层感知器(MLP)神经网络模型具有不同的Levenberg-Margnardt学习算法,反向传播,改进的反向传播(IBP)以及具有三层和不同节点编号的快速传播(3、4、5、6、7 )的中间层。这些模型是通过630个渗透率数据从位于沙特阿拉伯的一个海上油藏中获得的。通过将每个模型的获得结果与实验数据进行比较,研究了模型的准确性。因此,具有IBP学习方法和中间层五个节点的神经网络具有最高的准确性。

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