首页 | 本学科首页   官方微博 | 高级检索  
     

最小二乘支持向量机在储层流体识别中的应用
引用本文:魏聪,肖玉峰,董平川. 最小二乘支持向量机在储层流体识别中的应用[J]. 石油天然气学报, 2009, 31(2): 275-278
作者姓名:魏聪  肖玉峰  董平川
作者单位:[1]中国石油大学石油工程教育部重点实验室,北京102249 [2]中国石油大学油气资源与探测国家重点实验室,北京102249
摘    要:在测井储层流体识别中引入基于统计学习理论的最小二乘支持向量机(LS-SVM)算法,它是在传统的支持向量机(SVM)基础之上加以改进的一种新算法。LS-SVM采用结构风险最小化原则代替了传统的经验风险最小化原则,保证了其具有全局最优性和较好的泛化能力,并且它将凸二次规划问题转变成了线性方程组的求解问题.使计算效率大大提高。介绍了LS-SVM方法的基本原理和多分类方法,通过该法利用少量的测井资料作为学习样本,准确地对油气水层进行了识别。将它与交会图判别法和BP神经网络方法的预测结果进行比较,表明用LS-SVM方法来进行储层流体识别是可行的,且具有一定的优越性。

关 键 词:最小二乘支持向量机  油气水层识别  核函数  交会图  神经网络

Application of Least-square Support Vector Machine Method in Identifying Reservoir Fluids
WEI Cong XIAO Yu-feng DONG Ping-chuan. Application of Least-square Support Vector Machine Method in Identifying Reservoir Fluids[J]. Journal of Oil and Gas Technology, 2009, 31(2): 275-278
Authors:WEI Cong XIAO Yu-feng DONG Ping-chuan
Affiliation:MOE Key Laboratory of Petroleum Engineering in China University of Petroleum;Beijing 102249;China;State Key Laboratory of Petroleum Resource and Prospecting in China University of Petroleum;China;MOE Key Laboratory of Petroleum Engineering in China University of Petroleum;China
Abstract:The method of least -square support vector machine (LS-SVM),which was an improved algorithm for traditional support vector machine(SVM) based on statistical learning theory,was presented to study the recognition of reservoir fluids from geologic logging. By using the principle of structure risk minimization as opposed to empirical risk supported by conventional technique,the LS-SVM method was very good in global optimization and generalization. Furthermore,it acquires a high computing efficiency by solving ...
Keywords:least -squares support vector machine  recognition of the oil  gas and water zone  kernel function  cross plot  neural network  
本文献已被 CNKI 维普 等数据库收录!
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号