Deep Recurrent Neural Networks Approach to Sedimentary Facies Classification Using Well Logs

Abstract

Determination of lithofacies is one of the most important steps for reservoir characterization. Well log curves are not always sufficient to determine lithology as some times the signals are similar for different lithologies. We believe that the sequence of sedimentary patterns that follows the general geologic rules can be essential to help this disambiguation. This work aims to present a computational system based on deep recurrent neural networks (RNNs) as an effective method to automatically identify lithofacies patterns from well logs. We show that bidirectional long-short-term memory (BiLSTM) RNNs can learn long-term dependencies between time steps of sequence data improving the context available to facies classification. We validated our method by applying it to a real case study from Rio Bonito Formation (Paraná Basin, Brazil) and the proposed method is compared with XGBoost, Random Forest, Naïve Bayes, and support vector machine (SVM) learning approaches. The results indicate that the performance of the proposed method for lithology identification is higher when compared with these other methods.