Advancing the frontiers of nuclear magnetic resonance in porous media: A review of reservoir characterization and the energy transition

Abstract Nuclear magnetic resonance (NMR) has been widely applied for reservoir characterization for several decades; however, most existing reviews focus either on laboratory‐scale applications or logging technologies, without integrating recent advances in artificial intelligence (AI) and sustainable energy systems. This gap is increasingly significant as the energy sector transitions toward digitalization, carbon management, and hydrogen storage. This review provides a comparative synthesis of NMR developments, emphasizing its role not only in traditional hydrocarbon exploration but also in emerging applications such as carbon capture and storage (CCS), underground hydrogen storage, and geothermal reservoir evaluation. Uniquely, we highlight how AI‐ and machine learning‐driven workflows are reshaping NMR interpretation, improving accuracy in permeability prediction, fluid typing, and real‐time monitoring. The review also contextualizes NMR alongside alternative characterization tools, clarifying its distinctive contributions and limitations. Key technical challenges, including low signal‐to‐noise ratios, nanopore resolution limits, and hardware performance in high‐temperature, high‐pressure environments, are critically examined together with emerging solutions. By outlining both the proven capabilities and the unresolved gaps, this review offers a forward‐looking framework for integrating NMR into the broader landscape of smart, sustainable reservoir management and identifies concrete avenues for future research and industry implementation.