Adaptive Graph Convolutional Network with Deep Sequence and Feature Correlation Learning for Porosity Prediction from Well-Logging Data

Accurate porosity prediction is crucial for evaluating reservoir quality and potential productivity. Traditional laboratory experiments offer reliable porosity measurements but are time-consuming and computationally expensive. Recently, deep learning has been introduced for porosity prediction. However, most existing methods focus on either the relationships among features or deep sequences, neglecting the correlation between them. To better capture these correlations, we propose an adaptive graph convolutional network (GCN) with learning deep sequence and feature correlation graphs. The GCN is applied to well-logging data to capture non-Euclidean characteristics, extracting inherent spatiotemporal relationships. Furthermore, traditional GCNs rely on predefined graphs for feature aggregation, which can vary based on the thresholds for cosine similarity or covariance, potentially affecting accuracy and robustness. To tackle this issue, we introduced an adaptive mechanism for constructing deep sequence and feature correlation graphs, which eliminates the need for predefined thresholds and significantly enhances the accuracy and robustness of predictions. We demonstrated our method on well-logging datasets and comprehensively compared its performance with other deep learning models, showing its superiority in predicting porosity from well-logging data. This work offers geoscientists a more efficient and accurate analytical tool.