成都理工大学学报（自然科学版）2016，Vol.43Issue(6)：663-670,8.DOI:10.3969/j.issn.1671-9727.2016.06.04
基于 BP 神经网络的叠前流体识别方法
Study of pre-stack fluid identification method based on BP neural network
摘要
Abstract
The delta deposits of Triassic reservoirs in Sangtamu area of Tarim oilfield vary laterally,so it is very difficult to identify the fluid type in the reservoir.Discrimination of reservoirs is vital to oil and gas exploration in this formation.Therefore,a method of fluid identification in reservoir by application of distinguishing oil-bearing layers from water-bearing or dry layers by BP neural network is proposed.Pre-stack sensitive elastic parameters inversion data and logging interpretation results are used to generate training samples,and to divide the training samples into subsets of modeling building and verification by adopting random sampling.Accordingly,700 training samples and 62 test samples from 26 wells are used to build BP neural network.It shows that the success rate is more than 90%and the model is used to predict the whole sand formation successfully.The practice indicates that the method is suitable for fluid identification in the study area.关键词
弹性参数/BP 神经网络/模式识别/流体识别Key words
elastic parameters/BP neural network/pattern recognition/fluid identification分类
信息技术与安全科学引用本文复制引用
汪佳蓓,黄捍东..基于 BP 神经网络的叠前流体识别方法[J].成都理工大学学报（自然科学版）,2016,43(6):663-670,8.基金项目
国家科技重大专项（2011ZX05006-006；2011ZX05009）；国家“973”计划项目（2011CB201104）。 （）
