CIESC Journal ›› 2013, Vol. 64 ›› Issue (12): 4571-4577.DOI: 10.3969/j.issn.0438-1157.2013.12.045

Previous Articles     Next Articles

Computation of reservoir relative permeability curve based on RBF neural network

GE Yulei, LI Shurong   

  1. College of Information and Control Engineering, China University of Petroleum, Qingdao 266580, Shandong, China
  • Received:2013-08-12 Revised:2013-08-22 Online:2013-12-05 Published:2013-12-05
  • Supported by:

    supported by the National Natural Science Foundation of China (60974039).

基于RBF神经网络的油藏相对渗透率曲线计算

葛玉磊, 李树荣   

  1. 中国石油大学(华东)信息与控制工程学院, 山东 青岛 266580
  • 通讯作者: 葛玉磊
  • 作者简介:葛玉磊(1989- ),男,硕士。
  • 基金资助:

    国家自然科学基金项目(60974039);山东省自然科学基金项目(ZR2011FM002)。

Abstract: In this paper,a novel calculating method on relative permeability curve is proposed based on improved RBF neural network.In this method,the hybrid RNA genetic algorithm (HRGA) with the position displacement idea of bare bones particle swarm optimization (PSO) changing the mutation operator is proposed.The HRGA is applied to optimize the value of radial basis function centers in the hidden layer of RBF neural network.This method is used in the calculation of relative permeability curve.By comparing and analyzing the accuracy of relative permeability curve calculated by HRGA-RBF and standard RBF,the experimental result indicated that HRGA-RBF can improve the calculating accuracy obviously.

Key words: RBF neural network, hybrid RNA genetic algorithm, bare bones particle swarm, value of radial basis function centers, relative permeability

摘要: 提出了一种基于改进的RBF神经网络的相对渗透率曲线计算方法。利用骨干粒子群的位置更新操作更新RNA遗传算法的变异算子得到混合RNA遗传算法(HRGA),针对RBF神经网络中隐含层径向基中心值的确定,利用HRGA算法对其进行优化,并用于相对渗透率曲线的计算。将HRGA优化的RBF神经网络和标准RBF神经网络计算的相对渗透率曲线与真实值误差对比分析,实验结果表明HRGA优化的RBF神经网络明显提高了计算精度。

关键词: RBF神经网络, 混合RNA遗传算法, 骨干粒子群, 径向基中心值, 相对渗透率

CLC Number: