Intelligent Data Engineering and Automated Learning — IDEAL 2000. Data Mining, Financial Engineering, and Intelligent Agents

Volume 1983 of the series Lecture Notes in Computer Science pp 97-102


Lithofacies Characteristics Discovery from Well Log Data Using Association Rules

  • C. C. FungAffiliated withSchool of Electrical and Computer Engineering
  • , K. W. LawAffiliated withSchool of Electrical and Computer Engineering
  • , K. W. WongAffiliated withSchool of Information Technology, Murdoch University
  • , P. RajagopalanAffiliated withSchool of Computing, Curtin University of Technology

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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.