测井技术2017，Vol.41Issue(2)：171-175,5.DOI:10.16489/j.issn.1004-1338.2017.02.008
低电阻率油层模式识别方法的变量选取及应用
Selection of Model Variables for Pattern Recognition Methods and Its Application in Low Resistivity Pay Identification
摘要
Abstract
The geological conditions of the reservoirs in AG Formation are very complicated.Many low resistivity pays are developed,the identification process is difficult and the accuracy is low.The model variables are always used by the pattern recognition methods,such as support vector machine and neural networks that are incapable to reflect fluid types and the model accuracy is low.Cores,thin sections,SEM and well logs are comprehensively utilized to choose better model variables for the pattern recognition methods in low resistivity pay identification.The results show that reservoir types,natural gamma,the ratio of spontaneous potential amplitude RSP,neutron-density index ICD,deep resistivity,acoustic time,compensate neutron and balk density are recommended as the model variables.The suggested variables and the variables are often used in support vector machine for fluid identification of the proved reservoirs in the studied area.The results show the accuracy with suggested variables reaches 94.4%,which is significantly higher than that with the variables often used.关键词
测井解释/低电阻率油层/模式识别/变量选择/支持向量机/神经网络Key words
log interpretation/low resistivity pay/pattern recognition method/selection of model variables/support vector machine/neural network分类
天文与地球科学引用本文复制引用
韩如冰,田昌炳,李顺明,干旭,何辉,杜宜静..低电阻率油层模式识别方法的变量选取及应用[J].测井技术,2017,41(2):171-175,5.基金项目
国家科技重大专项复杂油气藏精细表征与剩余油分布预测(2011ZX05009-003) （2011ZX05009-003）
