Enhancing Lithofacies Interpretation in Well Logs With Graph-Based Feature Extraction

Subsurface lithology identification from well log signals is a crucial step in geological exploration, providing essential information about rock formation properties and fluid flow. Accurate identification of lithofacies aids in reservoir characterization and hydrocarbon exploration. This letter presents a novel approach for lithofacies identification from well logs using graph-based feature extraction and classification. The existing instance-based methods ignore the sequential information in well log signals, which can provide valuable insights about the local lithology. The proposed approach treats each instance in a temporal sequence as a node in a graph that captures the local geological information by aggregating temporally neighborhood nodes to create an embedded feature space. Two separate aggregating schemes are proposed, one using a spatial kernel approach and the other using an attention-based network layer, to find the nonlinear relationship between the feature vectors and give more weights to the nearest vectors in the feature space. The graph structure allows the network to incorporate spatial and relational information between different well log features into the classification process, leading to improved accuracy of predictions. The experiment is run on real-world data from the oil and gas exploration field at Krishna-Godavari (KG) Basin, India. The proposed method outperforms traditional feature-based classification and provides a unique way to enhance the representation of the well log signals for lithofacies classification tasks.