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Lithofacies Characteristics Discovery from Well Log Data Using Association Rules
Lecture Notes in Computer Science
Springer Berlin / Heidelberg
ISSN 0302-9743 (Print) 1611-3349
Volume 1983/2009
Intelligent Data Engineering and Automated Learning — IDEAL 2000. Data Mining, Financial Engineering, and Intelligent Agents
DOI 10.1007/3-540-44491-2

ISBN 978-3-540-41450-6
DOI 10.1007/3-540-44491-2_15
pp 101-119
Lithofacies Characteristics Discovery from Well Log Data Using Association Rules
C. C. Fung6 , K. W. Law6, K. W. Wong7 and P. Rajagopalan8

(6) School of Electrical and Computer Engineering, 6102, Kent St, Bentley, Western Australia
(7) School of Information Technology, Murdoch University, South St, 6150 Murdoch, Western Australia
(8) School of Computing, Curtin University of Technology, Kent St, 6102 Bentley, Western Australia

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.

C. C. Fung
Email: TFUNGCC@cc.curtin.edu.au
K. W. Wong
Email: K.Wong@murdoch.edu.au


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