Proceedings of SPE Annual Technical Conference and Exhibition 2003
DOI: 10.2523/84445-ms
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Obtain an Optimum Artificial Neural Network Model for Reservoir Studies

Abstract: TX 75083-3836, U.S.A., fax 01-972-952-9435. AbstractArtificial Neural Networks (ANNs) excel in dealing with uncertainty, fuzziness, incompleteness, and poorly defined nonlinear systems. These factors widely exist in reservoir studies. Training neural networks is a notoriously difficult problem. In training neural networks, one of the major pitfalls is overtraining, analogous to curve fitting for rule-based systems. Emperical evidence suggests that the number of records must exceed the number of neural network … Show more

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