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Intelligent Technology for Well Logging Analysis

  • Zhongzhi Shi
  • Ping Luo
  • Yalei Hao
  • Guohe Li
  • Markus Stumptner
  • Qing He
  • Gerald Quirchmayr
Conference paper
Part of the IFIP International Federation for Information Processing book series (IFIPAICT, volume 163)

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.

Key words

Intelligent Technology Well Log Analysis Data Mining MOUCLAS Algorithm 

References

  1. 1.
    B. Lent, A. Swami, and J. Widom. Clustering association rules. ICDE’97, (1997) 220–231Google Scholar
  2. 2.
    B. Liu, W. Hsu, and Y. Ma. Integrating classification and association rule mining. KDD’98. (1998) 80–86Google Scholar
  3. 3.
    Meretakis, D., & Wuthrich, B. Extending naive Bayes classifiers using long itemsets. Proc. of the Fifth ACM SIGKDD. ACM Press. (1999) 165–174Google Scholar
  4. 4.
    Dong, G., & Li, J. Efficient mining of emerging patterns: Discovering trends and differences. Proc. of the Fifth ACM SIGKDD. (1999)Google Scholar
  5. 5.
    Quinlan, J. R. C4.5: Programs for machine learning. San Mateo, CA: Morgan Kaufmann. (1993)Google Scholar
  6. 6.
    Cover, T. M., & Hart, P. E. Nearest neighbor pattern classification. IEEE Transactions on Information Theory, 13. (1967) 21–27CrossRefGoogle Scholar
  7. 7.
    F. Aminzadeh, Future Geoscience Technology Trends in, Stratigraphic Analysis, Utilizing Advanced Geophysical, Wireline, and Borehole Technology For Petroleum Exploration and Production, GCSEPFM pp 1–6, (1996)Google Scholar
  8. 8.
    Zhongzhi Shi. MSMiner: Data Mining Platform, Keynote speech, ICMLC2002, 2002Google Scholar
  9. 9.
    Zhongzhi Shi. OKPS: Expert System Developing Tool, Technical Report, ICT of CAS, 2004Google Scholar
  10. 10.
    William W. Cohen. Fast Effective Rule Induction. In Machine Learning: Proceedings of the Twelfth International Conference, Lake Taho, California, 1995.Google Scholar
  11. 1 1.
    Clifford A. Brunk and Michael J. Pazzani. An investigation of noise-tolerant relational concept learning algorithms. In Proceedings of the 8th International Workshop on Machine Learning, pages 389–393, Evanston, Illinois, 1991.Google Scholar

Copyright information

© International Federation for Information Processing 2005

Authors and Affiliations

  • Zhongzhi Shi
    • 1
  • Ping Luo
    • 1
  • Yalei Hao
    • 2
  • Guohe Li
    • 3
  • Markus Stumptner
    • 2
  • Qing He
    • 1
  • Gerald Quirchmayr
    • 2
    • 4
  1. 1.Institute of Computing TechnologyChinese Academy of SciencesBeijingChina
  2. 2.Advanced Computing Research CentreUniversify of South AustraliaAustralia
  3. 3.University of PetroleumBeijingChina
  4. 4.Institut für Informatik und WirtschaftsinformatikUniversität WienWienAustria

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