Lithofacies Characteristics Discovery from Well Log Data Using Association Rules

  • C. C. Fung
  • K. W. Law
  • K. W. Wong
  • P. Rajagopalan
Conference paper

DOI: 10.1007/3-540-44491-2_15

Volume 1983 of the book series Lecture Notes in Computer Science (LNCS)
Cite this paper as:
Fung C.C., Law K.W., Wong K.W., Rajagopalan P. (2000) Lithofacies Characteristics Discovery from Well Log Data Using Association Rules. In: Leung K.S., Chan LW., Meng H. (eds) Intelligent Data Engineering and Automated Learning — IDEAL 2000. Data Mining, Financial Engineering, and Intelligent Agents. IDEAL 2000. Lecture Notes in Computer Science, vol 1983. Springer, Berlin, Heidelberg

Abstract

This paper reports the use of association rules for the discovery of lithofacies characteristics from well log data. Well log data are used extensively in the exploration and evaluation of petroleum reservoirs. Traditionally, discriminant analysis, statistical and graphical methods have been used for the establishment of well log data interpretation models. Recently, computational intelligence techniques such as artificial neural networks and fuzzy logic have also been employed. In these techniques, prior knowledge of the log analysts is required. This paper investigated the application of association rules to the problem of knowledge discovery. A case study has been used to illustrate the proposed approach. Based on 96 data points for four lithofacies, twenty association rules were established and they were further reduced to six explicit statements. It was found that the execution time is fast and the method can be integrated with other techniques for building intelligent interpretation models.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • C. C. Fung
    • 1
  • K. W. Law
    • 1
  • K. W. Wong
    • 2
  • P. Rajagopalan
    • 3
  1. 1.School of Electrical and Computer EngineeringBentleyWestern Australia
  2. 2.School of Information TechnologyMurdoch UniversityMurdochWestern Australia
  3. 3.School of ComputingCurtin University of TechnologyBentleyWestern Australia