摘要
以认识油藏水淹层的水淹情况、指导石油勘探开发为目标,对比常用的多元回归分析方法,引入反向传播神经网络技术。针对油藏水淹层的测井资料,选取感应电导率、声波时差和电阻率作为特征变量,利用聚类分析法,根据水淹层测井数据的亲疏关系进行分类,分类结果作为神经网络结构输出,对测井数据进行训练学习,提高水淹层识别准确率。研究结果表明基于聚类分析的神经网络技术,可以很好地对油层水淹情况进行分析。
A technology of back propagation neural network is presented by comparing with the multiple regression analysis.This technology can not only identify the flooding of water-flooding layer but also guide the hydrocarbon exploration and development.In addition,the induction conductivity,acoustic time and resistivity are selected to be characteristic variables;and then well-logging data of water-flooded layer is classified by clustering analysis;the classified result can be as output of neural network structure and the well-logging data can be trained in order to increase identification accuracy.Result shows the neural network technology based on clustering can analyze water flooding of oil layers very well..
出处
《天然气勘探与开发》
2012年第2期22-24,86,共3页
Natural Gas Exploration and Development
基金
国家自然科学基金项目(50874114)"超深油气储层岩石物性垂向分布规律及渗流特征研究"资助
关键词
神经网络
聚类分析
水淹层
声波时差
电阻率
感应电导率
neural network,clustering analysis,water-flooded layer,acoustic time,resistivity,induction conductivity