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Proceedings Paper

Applications of soft computing in petroleum engineering
Author(s): Andrew H. Sung
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Paper Abstract

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.

Paper Details

Date Published: 1 November 1999
PDF: 13 pages
Proc. SPIE 3812, Applications and Science of Neural Networks, Fuzzy Systems, and Evolutionary Computation II, (1 November 1999); doi: 10.1117/12.367696
Show Author Affiliations
Andrew H. Sung, New Mexico Tech (United States)


Published in SPIE Proceedings Vol. 3812:
Applications and Science of Neural Networks, Fuzzy Systems, and Evolutionary Computation II
Bruno Bosacchi; David B. Fogel; James C. Bezdek, Editor(s)

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