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Fuzzy preprocessing rules for the improvement of an artificial neural network well log interpretation model

Wong, K.W., Fung, C.C. and Law, K.W. (2000) Fuzzy preprocessing rules for the improvement of an artificial neural network well log interpretation model. In: TENCON 2000, 24-27 September 2000, Kuala Lumpur, Malaysia pp. 400-405.

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Link to Published Version: http://dx.doi.org/10.1109/TENCON.2000.893697
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Abstract

The success of an artificial neural network (ANN) based data interpretation model depends heavily on the availability and the characteristics of the training data. In the process of developing a reliable well log interpretation model, a log analyst has to spend many hours performing pre-processing on the training data set. This demands substantial experience and expertise from the analyst. This paper proposes a fuzzy logic approach to integrate the knowledge of the log analysts in the pre-processing stage. This paper also presents results from an experimental study which demonstrated the implementation of the fuzzy preprocessing technique which has increased the prediction accuracy of the ANN well log interpretation model. This new method has the potential to be a useful and important tool for professional well log analysts

Publication Type: Conference Paper
Murdoch Affiliation: School of Information Technology
Publisher: IEEE
Copyright: © 2000 IEEE
Notes: Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
URI: http://researchrepository.murdoch.edu.au/id/eprint/1008
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