Intelligent technology for well logging analysis

Authors:
Zhongzhi Shi;Ping Luo;Yalei Hao;Guohe Li;Markus Stumptner;Qing He;Gerald Quirchmayr
Affiliations:
Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China;Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China;Advanced Computing Research Centre, University of South Australia, Australia;University of Petroleum, Beijing, China;Advanced Computing Research Centre, University of South Australia, Australia;Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China;Advanced Computing Research Centre, University of South Australia, Australia and Institut für Informatik und Wirtschaftsinformatik, Universität Wien, Wien, Austria
Venue:
Intelligent information processing II
Year:
2004

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Abstract

Well logging analysis plays an essential role in petroleum exploration and exploitation. It is used to identify the pay zones of gas or oil in the reservoir formations. This paper applies intelligent technology for well logging analysis, particular combining data mining and expert system together, and proposes an intelligent system for well log analysis called IntWeL Analyzer in terms of data mining platform MSMiner and expert system tool OKPS. The architecture of IntWeL Analyzer and data mining algorithms, including Ripper algorithm and MOUCLAS algorithm are also presented. MOUCLAS is based on the concept of the fuzzy set membership function that gives the new approach a solid mathematical foundation and compact mathematical description of classifiers. The aim of the study is the use of intelligent technology to interpret the pay zones from well logging data for the purpose of reservoir characterization. This approach is better than conventional techniques for well logging interpretation that cannot discover the correct relation between the well logging data and the underlying property of interest.