神经网络在测井解释中的应用  

Application of Nerve Network in Logging Interpretation

作　　者：张宇晓[1] 

机构地区：[1]河南石油勘探局

出　　处：《河南石油》1996年第2期21-25,5,共5页Henan Petroleum

摘　　要：介绍了神经网络的基本成分-神经元对信号的接收、激活与处理输出过程和网络的结构。从目前已有的几种模型中选取了误差反向传播网络模型。阐明了使用该模型进行训练与预测的方法和步骤,并用于测井中的粒度中值和渗透率解释。其预测结果、平均相对误差分别是7.84％与22.98％,而用以前其它方法,这一误差分别是20％与40％左右。The basic features of nerve network and its implementation process are presented in this paper. The selected BP network model is shown in detail and applied to interpretation of the median grain diameter and permeability in logging. The mean relative errors of the predicted results are 7.84% and 22.98% respectively, but we use previous method, these errors are about 20% and 40% respectively.

关 键 词：测井解释 神经网络 粒度 渗透率 数据处理 

分 类 号：P631.84[天文地球—地质矿产勘探]