Permeability Prediction in Petroleum Reservoir using a Hybrid System

  • Y. Huang
  • P. M. Wong
  • T. D. Gedeon
Conference paper


This paper introduces and demonstrates a hybrid soft computing system for predicting reservoir permeability of sedimentary rocks in drilled wells in the petroleum exploration and development industry. The method employs Takagi-Sugeno’s fuzzy reasoning, and its fuzzy rules and membership functions are automatically derived by neural networks and floating-point encoding genetic algorithms. The method is trained with known data and tested with unseen data. The results show that the hybrid system has a good generalisation capability and is effective for industrial applications.


Genetic Algorithm Membership Function Hybrid System Connection Weight Parent Chromosome 
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  1. [1]
    Bloch, S., 1991, Empirical prediction of porosity and permeability in sandstones. AAPG Bulletin, 75, 1145–1160.Google Scholar
  2. [2]
    Jian et al., 1994, A genetic approach to the prediction of petrophysical properties. Journal of Petroleum Geology, 17, 71–88.CrossRefGoogle Scholar
  3. [3]
    Wong, P.M. Taggart, I.J. and Gedeon, T.D., 1995, Use of neural network methods to predict porosity and permeability of a petroleum reservoir. AI Applications, 9(2), 27–38.Google Scholar
  4. [4]
    Mohaghegh et al., 1996, Petroleum reservoir characterization with the aim of artificial neural networks. Journal of Petroleum Science and Engineering, 16, 263–274.CrossRefGoogle Scholar
  5. [5]
    Huang, Y., Wong, P.M. and Gedeon, T.D., 1997, Spatial interpolation in log analysis using neural-fuzzy technique. 59th EAGE Conference & Technical Exhibition, Geneva, Extended Abstracts, vol. 1, P174.Google Scholar
  6. [6]
    Gedeon et al., 1997, Two dimensional neural-fuzzy interpolation for spatial data. Proceedings of GIS AM/FM ASIA ′97 & Geoinformatics ′97, Taipei, Taiwan, vol. 1, 159–166.Google Scholar
  7. [7]
    Takagi, T. and Sugeno, M., 1985, Fuzzy identification of systems and its applications to modeling and control. IEEE Transactions on Systems, Man & Cybernetics, 15(1), 116–132.MATHCrossRefGoogle Scholar
  8. [8]
    Takagi, H. and Hayashi, I., 1991, NN-driven fuzzy reasoning. International Journal of Approximate Reasoning, 5, 91–212.CrossRefGoogle Scholar
  9. [9]
    Huang Y., Wong, P.M. and Gedeon T.D., 1998, Neural-fuzzy-genetic-algorithm interpolator in log analysis. 60th EAGE Conference and Technical Exhibition, Leipzig, Germany, Extended Abstracts, vol. 1, P106.Google Scholar
  10. [10]
    Huang, Y., Wong, P.M. and Gedeon, T.D., 1998, Prediction of reservoir permeability using genetic algorithms. Al Applications, 12(1-3), 67–75.Google Scholar
  11. [11]
    Holland, J., 1975, Adaptation in Natural and Artificial Systems. Ann Harbor: University of Michican Press.Google Scholar
  12. [12]
    Wang, X. and Elbuluk, M., 1996, Neural network control of induction machines using genetic algorithm training. Proceedings of the 31 st IEEE IAS Annual Meeting, 3, 1733–1740.Google Scholar
  13. [13]
    Goldberg, D.E., 1989, Genetic Algorithms in Search, Optimization, and Machines Learning. Addison-Wesley, Reading, MA.Google Scholar
  14. [14]
    Michalewicz, Z., 1994, Genetic Algorithms + Data Structures = Evolution Programs. Springer-Verlag, Berlin.Google Scholar

Copyright information

© Springer-Verlag London 2000

Authors and Affiliations

  • Y. Huang
    • 1
  • P. M. Wong
    • 2
  • T. D. Gedeon
    • 3
  1. 1.TechComm Simulation Pty LtdChippendaleAustralia
  2. 2.School of Petroleum EngineeringUniversity of New South WalesSydneyAustralia
  3. 3.School of Information TechnologyMurdoch UniversityMurdochAustralia

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