This paper describes several applications of neural networks and fuzzy logic in petroleum engineering that have been, or are being, developed recently at New Mexico Tech. These real-world applications include a fuzzy controller for drilling operation; a neural network model to predict the cement bonding quality in oil well completion; using neural networks and fuzzy logic to rank the importance of input parameters; and using fuzzy reasoning to interpret log curves. We also briefly describe two ongoing, large-scale projects on the development of a fuzzy expert system for prospect risk assessment in oil exploration; and on combining neural networks and fuzzy logic to tackle the large-scale simulation problem of history matching, a long- standing difficult problem in reservoir modeling.
© (1999) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Andrew H. Sung, Andrew H. Sung, } "Applications of soft computing in petroleum engineering", Proc. SPIE 3812, Applications and Science of Neural Networks, Fuzzy Systems, and Evolutionary Computation II, (1 November 1999); doi: 10.1117/12.367696; https://doi.org/10.1117/12.367696

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