Well log data are routinely used for stratigraphic interpretation of the earth’s subsurface. This paper investigates using a co-evolutionary fuzzy system to generate a well log interpreter that can automatically process well log data and interpret reservoir permeability. The methodology consists of 3 steps: 1) transform well log data into fuzzy symbols which maintain the character of the original log curves; 2) apply a co-evolutionary fuzzy system to generate a fuzzy rule set that classifies permeability ranges; 3) use the fuzzy rule set to interpret well logs and infer the permeability ranges. We present the developed techniques and test them on well log data collected from oil fields in offshore West Africa. The generated fuzzy rules give sensible interpretation. This result is encouraging in two respects. It indicates that the developed well log transformation method preserves the information required for reservoir properties interpretation. It also suggests that the developed co-evolutionary fuzzy system can be applied to generate well log interpreters for other reservoir properties, such as lithology.
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References
Abonyi, J., Feil, B., Nemeth, S., and Arva, P. (2005). Modified gath-geva clustering for fuzzy segmentation of multivariate time-series. Fuzzy Sets and Systems, 149:39–56.
Apostolico, A., Bock, M. E., and Lonardi, S. (2002). Monotony of surprise and large-scale quest for unusual words. In Proceedings of the 6th International Conference on Research in Computational Molecular Biology, pages 22–31.
Bentley, Peter J. (2000). “Evolutionary, my dear watson” investigating committee-based evolution of fuzzy rules for the detection of suspicious insurance claims. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2000), pages 702–709. Morgan Kaufmann.
Keogh, Eamonn, Chakrabarti, Kaushik, Mehrotra, Sharad, and Pazzani, Michael (2001). Locally adaptive dimensionality reduction for indexing large time series databases. In Proceedings of ACM SIGMOD Conference on Management of Data, pages 151–162.
Larsen, R. J. and Marx, M. L. (1986). An Introduction to Mathematical Statistics and Its Applications,2nd Edition. Prentice Hall, Englewood.
Lin, J., Keogh, E., Lonardi, S., and Chiu, B. (2003). A symbolic representation of time series, with implications for streaming algorithms. In Proceedings of the 8th ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery.
Pena-Reyes, Carlos Andres and Sipper, Moshe (2001). Fuzzy coco: A cooperative coevolutionary approach to fuzzy modeling. IEEE Transactions on Fuzzy Systems, 9(5):727–737.
Potter, Mitchell A. and Jong, Kenneth A. De (1994). A cooperative coevolutionary approach to function optimization. In Parallel Problem Solving from Nature – PPSN III, pages 249–257, Berlin. Springer.
Potter, Mitchell A. and Jong, Kenneth A. De (2000). Cooperative coevolution: An architecture for evolving coadapted subcomponents. Evolutionary Computation, 8(1):1–29.
Yu, Tina (2001). Hierachical processing for evolving recursive and modular programs using higher order functions and lambda abstractions. Genetic Programming and Evolvable Machines, 2(4):345–380.
Yu, Tina and Bentley, Peter (1998). Methods to evolve legal phenotypes. In Parallel Problem Solving from Nature – PPSN V, pages 280–291, Berlin. Springer.
Yu, Tina and Wilkinson, Dave (2007). A fuzzy symbolic representation for intelligent reservoir well log interpretation. In “Hybrid Intelligent Systems using Soft Computing” of the Series on Computational Intelligence, Springer Verlag Edited by, O. Castillo, P. Melin, W. Pedrycz, and J. Kacprzyk.
Yu, Tina, Wilkinson, Dave, and Xie, Deyi (2003). A hybrid GP-fuzzy approach for reservoir characterization. In Riolo, Rick L. and Worzel, Bill, editors, Genetic Programming Theory and Practise, chapter 17, pages 271–290. Kluwer.
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Yu, T., Wilkinson, D. (2008). A Co-Evolutionary Fuzzy System for Reservoir Well Logs Interpretation. In: Yu, T., Davis, L., Baydar, C., Roy, R. (eds) Evolutionary Computation in Practice. Studies in Computational Intelligence, vol 88. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75771-9_9
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DOI: https://doi.org/10.1007/978-3-540-75771-9_9
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