Evaluation Model of Profile Control Layers' Selection Based on Fuzzy Cluster Analysis

被引:1
|
作者
Zhang, Angang [1 ]
Fan, Zifei [1 ]
Song, Heng [1 ]
机构
[1] Petrochina Res Inst Petr Explorat & Dev, Beijing 100083, Peoples R China
来源
关键词
Profile Control Layers; Evaluation Index; Screening; Fuzzy Clustering; Result Examination;
D O I
10.4028/www.scientific.net/AMR.734-737.1374
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
The selection of profile control layers is based on the decision made by single factor at present. Fuzzy cluster analysis based on transitive closure is introduced to establish an evaluation model of profile control layers' selection. Firstly, combining the static geological data and dynamic development data, the evaluation indexes which affect the selection of profile control layers are screened to form the evaluation index system; secondly, according to fuzzy cluster analysis theory, the evaluation index matrix related to profile control layers' selection is dynamically clustered, and the profile control layers are selected on the basis of each category's characteristics of geological and development; finally, the results of profile control layers' selection are examined by the comparison of water injection profile. In addition, the evaluation model is put into practice in a testing well group, and the application shows that the profile control layers selected by the model is reasonable and reliable.
引用
收藏
页码:1374 / 1380
页数:7
相关论文
共 50 条
  • [31] Threshold selection based on cluster analysis
    Kwon, SH
    [J]. PATTERN RECOGNITION LETTERS, 2004, 25 (09) : 1045 - 1050
  • [32] Stock selection based on cluster analysis
    Da Costa, Newton, Jr.
    Cunha, Jefferson
    Da Silva, Sergio
    [J]. ECONOMICS BULLETIN, 2005, 13
  • [33] Research on supplier evaluation and selection based on fuzzy hierarchy analysis and grey relational analysis
    张铁柱
    卓剑
    毕克新
    胡运权
    [J]. Journal of Harbin Institute of Technology(New series), 2007, (05) : 698 - 702
  • [34] Data and cluster weighting in target selection based on fuzzy clustering
    Kaymak, U
    [J]. FUZZY SETS AND SYSTEMS - IFSA 2003, PROCEEDINGS, 2003, 2715 : 568 - 575
  • [35] Fuzzy based enhanced cluster head selection (FBECS) for WSN
    Mehra, Pawan Singh
    Doja, Mohammad Najmud
    Alam, Bashir
    [J]. JOURNAL OF KING SAUD UNIVERSITY SCIENCE, 2020, 32 (01) : 390 - 401
  • [36] A Selection Model for Optimal Fuzzy Clustering Algorithm and Number of Clusters Based on Competitive Comprehensive Fuzzy Evaluation
    Wang, Yaonan
    Li, Chunsheng
    Zuo, Yi
    [J]. IEEE TRANSACTIONS ON FUZZY SYSTEMS, 2009, 17 (03) : 568 - 577
  • [37] Performance evaluation of two fuzzy-based cluster head selection systems for wireless sensor networks
    Anno, Junpei
    Barolli, Leonard
    Durresi, Arjan
    Xhafa, Fatos
    Koyama, Akio
    [J]. MOBILE INFORMATION SYSTEMS, 2008, 4 (04) : 297 - 312
  • [38] MODEL BASED FUZZY CONTROL
    BATUR, C
    KASPARIAN, V
    [J]. MATHEMATICAL AND COMPUTER MODELLING, 1991, 15 (12) : 3 - 14
  • [39] Evaluation of an Apartment Selection Model by Integrating Fuzzy AHP and Fuzzy TOPSIS
    Luc M.H.
    Nguyen Q.V.
    Do Q.H.
    Van Trang T.
    [J]. International Journal of Fuzzy System Applications, 2022, 11 (01)
  • [40] Logistics Enterprise Evaluation Model Based On Fuzzy Clustering Analysis
    Fu, Pei-hua
    Yin, Hong-bo
    [J]. INTERNATIONAL CONFERENCE ON APPLIED PHYSICS AND INDUSTRIAL ENGINEERING 2012, PT C, 2012, 24 : 1583 - 1587