Application of RBF Algorithm in Prediction of Threshold Pressure Gradient

Chang-jun Zhu, Xiujuan Zhao, Wei-hua Yang
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引用次数: 2

Abstract

It is well known that it plays an important role to determine threshold pressure gradient (TPG) in developing the low permeability oil field, and it directly influences the accuracy of reservoir pressure and developing amount. Threshold pressure gradient becomes nonlinear relation with such factors that may influence accuracy as permeability, viscosity and density of fluid and porosity and so on. Such a problem of nonlinear nature can be solved by RBF neural network systems. Based on above thought, authors of this paper predict the TPG using RBF neural network. This approach has further been tested and verified by actual determining results .The experimental results show that RBF neural network is an effective method for TPG prediction with good precision. The application of this approach can supply basic data for developing oil field so as to save cost and labor
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RBF算法在阈值压力梯度预测中的应用
阈值压力梯度的确定在低渗透油田开发中起着重要的作用,它直接影响到储层压力的准确性和开发量。阈值压力梯度与流体渗透率、粘度、密度、孔隙度等影响精度的因素呈非线性关系。这种非线性问题可以用RBF神经网络系统来解决。基于上述思想,本文采用RBF神经网络对TPG进行了预测。实验结果表明,RBF神经网络是一种有效的TPG预测方法,具有较好的预测精度。该方法的应用可为油田开发提供基础数据,从而节省成本和人力
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