Lithofacies Characteristics Discovery from Well Log Data Using Association Rules

  • C. C. Fung
  • K. W. Law
  • K. W. Wong
  • P. Rajagopalan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1983)


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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Jian, F.X., Chork, C.Y., Taggart, I.J., McKay, D.M., and Barlett, R.M.: A Genetic Approach to Prediction of Petrophysical Properties. Journal of Petroleum Geology, Vol. 17, No. 1(1994) pp. 71–88.CrossRefGoogle Scholar
  2. 2.
    Hook, J. R., Nieto, J. A., Kalkomey, C. T. and Ellis, D. “Facies and Permeability Prediction from Wireline Logs and Core-A North Sea Case Study,” SPWLA 35 th Annual Logging Symposium, paper “AAA”, June (1994).Google Scholar
  3. 3.
    Ebanks, W.R. Jr.: “Flow Unit Concept-Integrated Approach to Reservoir Description for Engineering Projects.” Paper presented at the 1987 AAPG Annual Meeting, Los Angeles (1987).Google Scholar
  4. 4.
    Wong, P. M., Taggart, I. J. and Jian, F. X. “A Critical Comparison of Neural Networks and Discriminant Analysis in Lithofacies, Porosity, and Permeability Predictions,” Journal of Petroleum Geology, vol. 18(2), April (1995), pp. 191–206.CrossRefGoogle Scholar
  5. 5.
    Condert, L., Frappa, M. and Arias, R. “A Statistical Method for Lithofacies Identification”, Journal of Applied Geophysics, vol 32, (1994), pp. 257–267.CrossRefGoogle Scholar
  6. 6.
    Fung, C. C., Wong, K. W. Eren, H. and Charlebois, R. “Lithology Classification using Self-Organising Map,” Proceedings of IEEE International Conference on Neural Networks, Perth, Western Australia, December (1995), pp. 526–531.Google Scholar
  7. 7.
    Wong, P.M., Gedeon, T.D., and Taggart, I. J.: Fuzzy ARTMAP: A New Tool for Lithofacies Recognition. AI Applications, Vol. 10, No. 2(1996), pp. 29–39.Google Scholar
  8. 8.
    Rogers, S. J., Fang, J. H., Karr, C. L. and Stanley, D.A. “Determination of Lithology from Well Logs Using a Neural Network,” The AAPG Bulletin, vol. 76(5), (1992), pp. 731–739.Google Scholar
  9. 9.
    Fayyad, U. M., Piatetsky-Shapiro, G. and Smyth, P.: “From Data Mining to Knowledge Discovery: An Overview,” Advances in Knowledge Discovery and Data Mining, ed. U. Fayyad, G. Piatetsky-Shapiro, P. Smyth, R. Uthurusamy, The AAAI/MIT Press, Menlo Park, California/Cambridge, Massachusetts, (1996), pp. 1–34.Google Scholar
  10. 10.
    Chen, M. S., Han, J. and Yu, P. S.: “Data Mining: An Overview from a Database Perspective,” IEEE Transactions on Knowledge and Data Engineering, vol. 8(6), December (1996), pp. 866–883.CrossRefGoogle Scholar
  11. 11.
    Agrawal R., and Srikant, R.: Mining Quantitative Association Rules in Large Relational Tables. Proceedings of the 1996 ACM SIGMOD International Conference on Management of Data, Montreal Canada (1996), pp. 1–12.Google Scholar
  12. 12.
    Agrawal, R., Mannila, H., Srikant, R., Toivonen, H., and Verkamo, I.: Fast Discovery of Association Rules. In: Fayyad, U., Piatetsky-Shapiro, G., Smyth, P., and Uthurusamy, R. (eds.): Advances in Knowledge Discovery and Data Mining. AAAI Press/The MIT Press, Menlo Park California/Cambridge Massachusetts (1996), pp. 307–328.Google Scholar

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

Personalised recommendations