关键词: 改进PSO-BP神经网络, 惯性权重因子, 储层参数, 预测

Abstract:  In order to improve the convergence speed and generalization ability of BP neural network and prevent it from falling into local optimal value, the traditional particle swarm optimization algorithm is improved in three aspects based on the previous research, including the limit of the maximum speed, the changes of the inertia weight factor and the improvement of the fitness function. Then it is used to optimize the weight and threshold of the BP neural network. And the dynamic prediction on reservoir parameter is realized by the improved PSO-BP neural network, the whole process is determining the input and output neurons, quantitating the time parameter, constructing the neural network model with the training samples and testing it. Finally, the simulation results of the average training error is analyzed, and it proves that the convergence and generalization ability of the improved PSO-BP algorithm are better than the BP algorithm and PSO-BP algorithm.

Key words: improved PSO-BP neural network, inertia weight factor, reservoir parameter, predication