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This comprehensive review explores the integration of artificial intelligence (AI) in shale gas and oil resource management, with a focus on the application of machine learning and deep learning techniques. Shale gas and oil extraction, once constrained by complex geological and petrophysical challenges, has benefited significantly from AI methodologies, which enhance reservoir characterization, fracture prediction, production forecasting, and optimization of hydraulic fracturing. The review examines the current state of AI applications, identifying key advances in AI-driven models that improve predictive accuracy, address data heterogeneity, and integrate diverse data sources. Special attention is given to the combination of AI with traditional physical simulation models, such as physics-informed neural networks, and the potential for hybrid modeling approaches. Despite these advancements, the review highlights several challenges, including data sparsity, model interpretability, and the need for continuous updates with real-time data. Moreover, the review discusses emerging trends, such as explainable AI and real-time monitoring systems, which offer the promise of improving model transparency and adaptability. Future research directions are proposed to enhance the scalability and robustness of AI models, particularly through the integration of advanced hybrid models, uncertainty quantification techniques, and collaborative efforts across academia and industry, ultimately aiming to foster sustainable and efficient shale resource management.
Yang et al. (Mon,) studied this question.