Rule Induction Algorithm for Application to Geological and Petrophysical Data

  • C. V. Deutsch
  • Y. L. Xie
  • A. S. Cullick
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 80)


Very large geological, geophysical, and petrophysical databases often contain multiple data types that must be interpreted for application to subsurface modeling. Significant advances in discovering complex and even nonintuitive data relationships could lead to better predictions. There is a litany of data analysis techniques used today, including cluster analysis, principal component analysis, discriminant analysis, parametric and nonparametric regression, and N-dimensional histograms. Regression techniques and neural networks have in common their multivariate combination of predictor variables. These techniques may be good at interpolating within the data boundaries of the training data, but may be poor for extrapolation because of the lack of understanding of the underlying relationships in the variables. Alternatively, machine learning and data mining technologies including Rough Sets hold the promise of finding data category relationships and expressing those in a rule-based language. This paper presents a novel rule induction algorithm derived from these machine-learning techniques, developed for reservoir characterization with geological and geophysical data. A set of facies models with systematical changing in the geometric features is synthesized. The geometric features are coded and the effective permeability is calculated. Rules between effective permeability and geometric features are deducted by using the proposed technique. The consistence of the deducted rules with those implemented in the data synthesization exhibit the effectivity of the proposed technique. Further a second example of facies assignment from wireline logs is used to test the proposed technique. The deducted rules are confirmed by the geologists who spend significant time trying to summarize rules from the well logs. The probability feature of the rules and the distingushibility analysis feature of the proposed technique supplied additional information for the geologist to reconsider their original distinction among facies.


Decision Outcome Effective Permeability Decision Attribute Rule Induction Negative Rule 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Balch RS, Stubbs BS, Weiss WW, and Wo S (1999) Using artificial intelligence to correct multiple seismic attributes to reservoir properties. In: SPE Annual Tech Conference and Exhibition. Houston, TX, October 5–8 1999, SPE 56733.Google Scholar
  2. 2.
    Berry MJA, Linoff G (1997) Data Mining Techniques, For Marketing, Sales, and Customer Support. Wiley Computer Publishing, John Wiley & Sons Inc., New York.Google Scholar
  3. 3.
    Bishop CM (1995) Neural Networks for Pattern Recognition. Oxford University Press, New York.Google Scholar
  4. 4.
    Bradley S, Fayyad U, Mangasarian OL (1999) Mathematical programming for data mining formulations and challenges. INORMS J. on Computing 11:217–238.CrossRefGoogle Scholar
  5. 5.
    Breiman L, Friedman J, Olshen R, Stone C (1984) Classification and Regression Tree. Chapman and Hall, New York.Google Scholar
  6. 6.
    Buntine W (1992) Learning classification trees. Statistics and Computing, 63–73.Google Scholar
  7. 7.
    Cabcna P, Hadjinian P, Stadler R, Verhees J, Zanasi A (1997) Discovering Data Mining from Concept to Implementation. Prentice Hall, New York.Google Scholar
  8. 8.
    Two Crows Corporation (1999) Introduction to Data Mining and Knowledge discovery.Google Scholar
  9. 9.
    Curram SP, Mingers J (1994) Neural networks, decision tree induction and discriminant analysis: an empirical comparison. Journal of the Operational Research Society 45:440–450.Google Scholar
  10. 10.
    Deutsch CV, Journel AG (1998) GSLIB: Geostatistical Software Library: an User’s Guide. Oxford University Press, New York, 2nd Ed.Google Scholar
  11. 11.
    Fayyad U, Piatetsky-Shapiro G, Smyth P, Uthurusamy R (1996) Advances in Knowledge Discovery and Data Mining. MIT Press, Cambridge, MA.Google Scholar
  12. 12.
    Hook JR, Nieto JA, Kalkomey CT, Ellis D (1994) Facies and permeability prediction from wireline logs and core: a north sea case study. In: 35th Annual SPWLA Logging Symposium,Tulsa.Google Scholar
  13. 13.
    Kohonen T (1989) Self-Organization and Association Memory (3rd edition). Springer-Verlag.CrossRefGoogle Scholar
  14. 14.
    Kohonen T (1995) Self-Organization Maps. Springer-Verlag, Heidelberg.CrossRefGoogle Scholar
  15. 15.
    Lee SH, Datta-Gupta A (1999) Electrofacies characterization and permeability predictions in carbonate reservoirs: roles of multivariate analysis and nonparametric regression. In: SPE Annual Tech Conference and Exhibition, Houston, TX, October 5–8, SPE 56658.Google Scholar
  16. 16.
    Lira TS, Loh Y, Shih YS (1999) A comparison of prediction accuracy, complexity and training time of thirty-three old and new classification algorithms. The Machine Learning Journal, 1–27.Google Scholar
  17. 17.
    Lin TY, Cercone N (1998) Rough Sets and Data Mining: Analysis for Imprecise Data. Kluwer Academic Publisher.Google Scholar
  18. 18.
    Menon S, Sharda R (1999) Data mining update: new modes to persue old objectives. ORMS Today, 26–29.Google Scholar
  19. 19.
    Murthy SK, Kasif S, Salzberg S (1994) A system for induction of oblique decision trees. Journal of Artificial Intelligence Research, 2:1–33.Google Scholar
  20. 20.
    Orlowska E (1998) Incomplete information: Rough set analysis. In: Vol 13, Studies in Fuzziness and soft computing, p 620. Physica Verlag.Google Scholar
  21. 21.
    Pal SK, Skowron A (1999) Rough Fuzzy Hybridization: A New Trend in Decision-Making. Springer Verlag, New York.Google Scholar
  22. 22.
    Pawlak Z (1991) Rough Sets: Theoretical Aspects of Reasoning About Data. Kluwer Academic Publisher, Dordrecht.Google Scholar
  23. 23.
    Polkowski L, Skowron A (1998) Rough sets and current trends in computing. In: First international Conference, RSCTC’98, p 601, Warsaw, Poland, Springer.Google Scholar
  24. 24.
    Polkowski L, Skowron A (1998) Rough sets in knowledge discovery, : Methodology and Application. In: Vol 18, Studies in Fuzziness and Soft Computing, p 570. Physica Verlag.Google Scholar
  25. 25.
    Polkowski L, Skowron A (1998) Rough sets in knowledge discovery, ii: Applications, Case Studies, and Software Ssystems. In: Vol 19, Studies in Fuzziness and Soft Computing, page 601. Physica Verlag.Google Scholar
  26. 26.
    Quinlan JR (1986) Induction of decision tree. Machine Learning, 1:81–106.Google Scholar
  27. 27.
    Ripley BD (1996) Pattern Recognition and Neural Networks. Cambridge University Press, Cambridge.Google Scholar
  28. 28.
    Scheevel JR, Payrazyan K (1999) Principal component analysis applied to 3D seismic data for reservoir property estimation. In: SPE Annual Tech Conference and Exhibition, Houston, TX, October 5–8. SPE 56734.Google Scholar
  29. 29.
    Shih YS (1999) Families of splitting criteria for classification trees. Statistics and Computing.Google Scholar
  30. 30.
    Slowinski R, Slowinski R (1992) Intelligent Decision Support: Handbook of Applications and Advances of the Rough Sets Theory. Kluwer Academic Publisher, Dordrecht.CrossRefGoogle Scholar
  31. 31.
    Ziarko WP (1993) Rough sets, Fuzzy Sets, and Knowledge Discovery. In: Proceedings of the International Workshop on Rough Sets and Knowledge Discovery (RSKD 95), Banff, Alberta, Canada, 12–15 October. SpringerVerlag.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • C. V. Deutsch
    • 1
  • Y. L. Xie
    • 1
  • A. S. Cullick
    • 2
  1. 1.School of Mining and Petroleum EngineeringUniversity of AlbertaEdmonton, AlbertaCanada
  2. 2.Landmark Graphics CorporationAustinUSA

Personalised recommendations