Artificial Intelligence and Machine Learning Applications in Unconventional Reservoirs: A Review of Advances, Field Deployment Challenges, and Future Roadmaps

Unconventional oil and gas resources have become central to the global energy supply, but their development is held back by low matrix permeability, complex fracture networks, and reservoir heterogeneity that empirical workflows handle poorly. Modern operations also generate vast multiscale data─drilling, completion, and production records─that remain unstructured and siloed, leaving a “data-value gap” between collection and decision-making. Artificial intelligence (AI) and machine learning (ML) bridge this gap by extracting nonlinear patterns across the exploration and production (E&P) lifecycle. This review covers four application areas: subsurface characterization (seismic interpretation, sweet-spot mapping, lithofacies classification, geomechanical estimation, and digital rock physics), well construction and stimulation (drilling optimization, real-time risk monitoring, well placement, and hydraulic fracturing), production forecasting and reservoir management, and CO 2 -EOR coupled with carbon capture, utilization, and storage (CCUS). Three deeper shifts are evident. Physics-informed neural networks (PINNs) and Fourier neural operators (FNOs) embed governing equations into the loss function, producing physically admissible solutions while easing data dependence. Generative models (GANs, diffusion architectures, and U-Net segmentation) confront chronic core-data scarcity by reconstructing pore networks and synthesizing facies realizations. Explainable AI methods (SHAP and LIME) push back against the black-box character of purely data-driven workflows. Field deployment is still blocked by three persistent gaps: fragmented data governance, a 20–40% accuracy drop when models cross basins, and the underdeveloped CCUS frontier of plume migration and caprock integrity monitoring. Closing these gaps will require hybrid, physics-constrained, interpretable architectures rather than ever-deeper black-box predictors.