An NMR EMG-Based Method for Pore Structure Characterization and Permeability Prediction in Carbonate Reservoirs

Carbonate reservoirs host substantial hydrocarbon resources; however, their characterization remains challenging due to strong heterogeneity and complex pore systems, which often produce asymmetric and highly multimodal NMR T₂ distributions. These characteristics undermine the applicability and robustness of conventional pore-structure interpretation and permeability models. To overcome these limitations, we propose a novel nuclear magnetic resonance (NMR)-based method that employs an Exponentially Modified Gaussian (EMG) model to quantitatively characterize pore structure and improve permeability estimation. First, the EMG function is used to decompose the measured T₂ distribution into multiple components with clear physical implications, enabling separation and quantification of pore contributions across scales. Second, the EMG-derived characteristic parameters are subsequently incorporated to refine the conventional Schlumberger-Doll Research (SDR) permeability model, thereby accounting for the impact of complex pore geometry on flow capacity. Validation using diverse carbonate samples demonstrates that the EMG model provides accurate and stable fitting of T₂ distributions across varying pore complexity, ranging from unimodal to highly multimodal distributions. Moreover, EMG-informed permeability estimation yields significantly improved accuracy and robustness compared with conventional methods. Overall, the proposed NMR EMG-based method offers a reliable solution for pore-structure characterization and permeability evaluation in complex carbonate reservoirs.