Hybrid monitoring framework for geological CO2 storage: Comparative insights from nuclear magnetic resonance (NMR) and conventional techniques

• Comparative review of NMR and conventional CO₂ storage monitoring techniques. • NMR reveals pore-scale CO₂ trapping, wettability, and fluid dynamics processes. • Conventional methods capture large-scale plume movement but lack pore resolution. • Hybrid NMR–geophysical monitoring improves accuracy and leakage detection. • Machine learning integration enhances predictive capability and uncertainty control. Effective tracking of the geological carbon dioxide (CO 2 ) storage is very important in ensuring the safety of the environment and adherence to storage rules. This review discusses the classic geophysical techniques such as 4D seismic, electromagnetic (EM), and gravimetry and their abilities are compared to nuclear magnetic resonance (NMR), which is an emerging technology that improves monitoring on a microscopic scale. Conventional methods are appropriate to map the movement of plumes and structural variations but are not good enough to see important processes such as residual trapping, changes in wettability, and fluid dynamics at the pore-scale. Conversely, NMR quantitatively describes fluid interactions and phase behavior at the pore level and is able to give quantitative information on CO 2 saturation and trapping processes. This review shed light on how NMR, under a combination with conventional geophysical methods, can form a hybrid monitoring system that can offer the pore-scale accuracy of the monitoring approach, and the field-scale extent of the monitoring framework. Through machine learning, built-in workflows now can combine seismic, pressure, and fluid chemistry data increasing predictive accuracy and uncertainty quantification. This hybrid monitoring method will greatly enhance the credibility of the CO 2 storage measurements through the real-time identification of the possible leakage and the reservoir maintenance. Future CO 2 storage projects can realize this by placing NMR in greater monitoring networks.