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Lithofacies Classification From Well Log Data Using Neural Networks, Interval Neutrosophic Sets and Quantification of Uncertainty

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Executive Summary

This paper proposes a novel approach to the question of lithofacies classification based on an assessment of the uncertainty in the classification results. The proposed approach has multiple Neural Networks (NN), and Interval Neutrosophic Sets (INS) are used to classify the input well log data into outputs of multiple classes of lithofacies. A pair of n-class neural networks are used to predict n-degree of truth memberships and n-degree of false memberships. Indeterminacy memberships or uncertainties in the predictions are estimated using a multidimensional interpolation method. These three memberships form the INS used to support the confidence in results of multiclass classification. Based on the experimental data, the approach improves the classification performance as compared to an existing technique applied only to the truth membership.

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