Advanced fluid prediction using interpretable spatio-temporal network and sHapley additive exPlanations through well logging data

Abstract

In the realm of oil and gas exploration, accurately predicting subsurface fluid types is crucial. Traditional techniques such as core sampling, x-ray diffraction, and x-ray fluorescence, despite providing essential data, are hampered by high costs, time consumption, or limited applications. This paper introduces an interpretable spatiotemporal deep learning network, ISTNet, utilizing well log data to predict fluid types. The framework enhances prediction accuracy and model robustness through a dual-branch design integrating spatial and temporal branches. The spatial branch employs graph neural networks to capture spatial features of well log data, while the temporal branch analyzes time series features using bidirectional long short-term memory networks (BiLSTM). Additionally, ISTNet incorporates the SHapley Additive exPlanations (SHAP) model to augment the interpretability of predictions. Empirical studies in the Tarim Basin demonstrated that ISTNet outperforms seven other advanced models, achieving an average accuracy exceeding 97% on datasets from two distinct wells. ISTNet not only improves the accuracy and robustness of fluid predictions in oil and gas exploration but also enhances transparency and interpretability through the SHAP model, providing geologists and engineers with tools to deeply understand subsurface geological processes and refine exploration and development strategies.