摘要
将模式识别理论中的支持向量机、Bayes判别分析以及多元回归分析应用于卡钻类型判别分析,建立了基于模式识别理论的卡钻判别模型。以近几年川东北卡钻数据为例进行了算例分析,结果表明:采用支持向量机、Bayes判别法及多元回归法对卡钻类型判别的结果与实际结果的误判率分别为1.92%,11.52%,61.54%。支持向量机(SVM)判别结果精度最高,但其判别方程式较为复杂,不能直观看出各分量对结果的影响程度;多元回归分析判别方程形式简单,可以直观表达各参量与卡钻之间的密切程度,但其判别精度较低;Bayes判别法计算精度介于二者之间,但其判别精度与判别式的个数密切相关。
The Support Vector Machine（ SVM）,Bayes discriminant analysis and multiple regression analysis were used for diagnosis and prediction of sticking. The discriminant model of stick type was built based on pattern recognition theory. The calculation analysis on the sticking data of northeastern Sichuan was made,it was indicated that the misjudgment rate for discriminant result of SVM,Bayes discriminant analysis and multiple regression analysis was 1. 92%,11. 52% and 61. 54%. The accuracy of SVM recognition result was the highest,but its discriminant equation is complex and the contribution of each component to the result could not be intuitively seen; while the equation of multiple regression analysis is simple,which could intuitively show the close degree between each component and sticking,but the accuracy of recognition result was lower. The accuracy of Bayes discriminant analysis was between the above two,but the discriminant accuracy is closely related to the number of discriminant.
出处
《探矿工程（岩土钻掘工程）》
2015年第10期31-34,共4页
Exploration Engineering:Rock & Soil Drilling and Tunneling
基金
中石化科技攻关项目"页岩气‘井工厂’技术研究"(编号:P13138)
关键词
模式识别
支持向量机
Bayes判别法
多元回归
卡钻
pattern recognition
Support Vector Machine
Bayes discriminate method
multiple regression
sticking
作者简介
吴军，男，汉族，1981年生，石油工程专业，主要从事涪陵页岩气钻井技术管理及成本分析方面的研究与管理工作，重庆市涪陵区焦石镇涪陵页岩气公司钻井工程项目部，zangyb．sripe@sinopec．com。
