Epistemic Uncertainty and Model Transparency in Rock Facies Classification Using Monte Carlo Dropout Deep Learning

Although Deep Learning (DL) architectures have been used as efficient prediction tools in a variety of domains, they frequently do not care about the uncertainty in the predictions. This may prevent them from being used in practical applications. In seismic reservoir characterisation, predicting facies from seismic data is typically viewed as an inverse uncertainty quantification issue. The goal of the current study is to analyse the dependability of rock facies classification model in order to quantify the uncertainty while maintaining the high accuracy by using and evaluating monte carlo dropout based deep learning (MCDL), a computationally efficient technique. The proposed method is unique since it can quantify the epistemic uncertainty of the classified facies in blind or unseen well conditioned on Seismic attributes in the bayesian approximation achieved by MCDL framework. The findings show that MC dropout is successful in terms of accuracy and reliability, with a blind test F1-scores of 98% and 82% in predicting facies from synthetic and seismic datasets respectively. Moreover, the applications in a 2D section indicate that the internal regions of the seismic sections are generally classified with less epistemic uncertainty than their boundaries, as calculated from the different realizations of the MCDL network. For comparison, a plain DL and support vector machine (SVM) are also implemented and the findings suggest that our method outperformns the other models in comparison which indicates the potential of the model to be implemented in a robust rock facies classification.