Explainable Artificial Intelligence for Seismic Reservoir Characterization: A Review of Machine Learning, SHAP-Based Interpretability, and Future Research Directions

Abstract

The rapid advancement of Artificial Intelligence (AI) has transformed seismic reservoir characterization by enabling automated interpretation of complex seismic datasets and improving the prediction of subsurface reservoir properties. Machine learning techniques, including ensemble learning, support vector machines, artificial neural networks, and gradient boosting algorithms, have demonstrated significant improvements in porosity prediction, lithofacies classification, fault detection, and structural mapping compared with traditional deterministic approaches. However, despite their predictive capability, most AI models operate as black-box systems, limiting transparency and reducing confidence in critical exploration and reservoir management decisions. This limitation has motivated increasing interest in Explainable Artificial Intelligence (XAI), which seeks to provide interpretable and trustworthy explanations for machine learning predictions. Among existing XAI techniques, SHapley Additive exPlanations (SHAP) has emerged as one of the most robust and theoretically sound methods for quantifying feature importance and explaining complex nonlinear models. This review examines recent developments in AI-driven seismic interpretation, digital twin technologies, machine learning-based reservoir characterization, and SHAP-based interpretability. It discusses the evolution of seismic attribute analysis from conventional statistical methods to modern explainable AI frameworks and highlights recent advances in uncertainty-aware reservoir characterization and intelligent digital reservoir systems. Furthermore, current research challenges, including model transparency, geological consistency, uncertainty quantification, data heterogeneity, and real-time explainability, are critically analyzed. Finally, future research directions involving explainable deep learning, physics-informed AI, federated learning, and digital twin-enabled intelligent reservoir management are presented. This review provides a comprehensive reference for researchers and petroleum engineers seeking to develop transparent, reliable, and trustworthy AI solutions for next-generation seismic reservoir characterization.