Permeability Prediction in Petroleum Reservoir using a Hybrid System
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.
KeywordsGenetic Algorithm Membership Function Hybrid System Connection Weight Parent Chromosome
Unable to display preview. Download preview PDF.
- Bloch, S., 1991, Empirical prediction of porosity and permeability in sandstones. AAPG Bulletin, 75, 1145–1160.Google Scholar
- 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
- 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
- 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
- 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
- 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
- Holland, J., 1975, Adaptation in Natural and Artificial Systems. Ann Harbor: University of Michican Press.Google Scholar
- 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
- Goldberg, D.E., 1989, Genetic Algorithms in Search, Optimization, and Machines Learning. Addison-Wesley, Reading, MA.Google Scholar
- Michalewicz, Z., 1994, Genetic Algorithms + Data Structures = Evolution Programs. Springer-Verlag, Berlin.Google Scholar