基于RBF神经网络的油藏相对渗透率曲线计算

Computation of reservoir relative permeability curve based on RBF neural network

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。

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.