Applications of soft computing in petroleum engineering

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. Even though our experience of applying advanced computational techniques to petroleum engineering problems also includes results that are less than complete success, the soft computing methods have proved to be cost-effective in solving most of the problems we attempted.