[1]尚福华[],姜萌[],马楠[],等. 基于改进马氏聚类方法的油田分类研究[J].计算机技术与发展,2015,25(08):175-178.
　SHANG Fu-hua[],JIANG Meng[],MA Nan[],et al. Research on Oil Classification Based on Improved Mahalanobis Clustering Method[J].,2015,25(08):175-178.
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 基于改进马氏聚类方法的油田分类研究()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
25
期数:
2015年08期
页码:
175-178
栏目:
应用开发研究
出版日期:
2015-08-10

文章信息/Info

Title:
 Research on Oil Classification Based on Improved Mahalanobis Clustering Method
文章编号:
1673-629X（2015)08-0175-04
作者:
 尚福华[1] ; 姜萌[1] ; 马楠[2] ; 解红涛[1]
 1.东北石油大学 计算机与信息技术学院；2.中国石油天然气股份有限公司规划总院
Author(s):
 SHANG Fu-hua[1];  JIANG Meng[1] ; MA Nan[2];  XIE Hong-tao[1]
关键词:
 ; 聚类分析; 马氏距离; 油田分类; 权重
Keywords:
 ; cluster analysis; Mahalanobis distance; oil classification; weight
分类号:
TP39
文献标志码:
A
摘要:
 针对大庆低渗透油田开发效果变差、开发储量品位降低、优选区块技术难度增大，导致原有的评价方法、优选技术、开采方式难以满足低渗透油田发展需要的问题，提出了应用聚类分析的方法对低渗透油田储层进行分类。首先简述聚类分析以及马氏距离的基本原理，并根据油田不同储层参数对储层采出贡献程度不同的特点，以及原有马氏距离在估计协方差矩阵方面难度大导致计算效率低的问题，提出了马氏距离聚类过程中估计协方差矩阵的迭代法。该方法充分考虑到了变量权重和样本类别的影响，对协方差矩阵的估计进行改进，能够在一定程度上减少权值确定上的主观因素并提高计算效率。以油田开发实际数据为例进行实验分类，结果表明该方法是有效可行的。
Abstract:
 Aiming at the problem that original evaluation method,optimal technology and exploring way can’ t meet the development need in Daqing oilfield with low permeability,which caused by worse development effect,lower development grade,and the technology of pre-fer block is more hard,propose that applying the clustering analysis to classify the oil reservoir with low permeability. First,describe the basic principle of cluster analysis and Mahalanobis distance briefly,according to the characteristics that the oil of different reservoir param-eters have different degree of contribution on reservoir recovery and the problem of the original Mahalanobis distance has low calculation efficiency in evaluating covariance matrix,present an iteration method to estimate the covariance matrix of Mahalanobis distance during the cluster analysis. The weights of variables and categories of samples are taken into account,estimation of the covariance matrix is im-proved,the method can reduce the subjective factors on determining weights and improve the computational efficiency in a certain extent. With actual oilfield development data as an example for classification,the experimental results demonstrate the effectiveness of the meth-od.

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更新日期/Last Update: 2015-09-14