Abstract
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.
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Huang, Y., Wong, P.M., Gedeon, T.D. (2000). Permeability Prediction in Petroleum Reservoir using a Hybrid System. In: Suzuki, Y., Ovaska, S., Furuhashi, T., Roy, R., Dote, Y. (eds) Soft Computing in Industrial Applications. Springer, London. https://doi.org/10.1007/978-1-4471-0509-1_38
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DOI: https://doi.org/10.1007/978-1-4471-0509-1_38
Publisher Name: Springer, London
Print ISBN: 978-1-4471-1155-9
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