关键词: 神经网络, 孔隙度, 渗透率, 微粒群算法, 储层参数预测

Abstract:

A Predictive reservoir model with the selfadoption and complicated nonlinear property is set up. Because Multilayer Feed Forward Neural Networks BP Algorithm exists weakness of getting bogged down in the local optima, stronger robustness and global convergence of PSO Algorithm, this research makes use of the particle swarm optimization (PSO) to improve the neural network, then, on the basis of the Luodai gas field in Sichuan Province, by the computation methods of PSO of the neural network, the reservoir characters(such as porosity, permeability) is forecasted, also the precision is tested and is compared with traditional computation methods of BP and LMBP, by which a obvious geography efficiency superior to traditional explanation methods is obtained, the shortcoming base from BP and LMBP algorithm are effectively overcome.

Key words: artificial neural networks, porosity, permeability, particle swarm optimization, the forecast of the layer parameters