Integrating NMR and machine learning for pore-type driven rock classification in the heterogeneous Asmari carbonate...

The Asmari Formation's complex heterogeneity presents fundamental challenges for reservoir characterization, where conventional lithology-based methods inadequately capture dynamic fluid behavior and pore-scale productivity controls. This study develops an integrated NMR-electrofacies framework combining Combinable Magnetic Resonance (CMR) logging with explainable machine learning to overcome such limitations. High-resolution CMR data were analyzed to quantify pore structure characteristics, fluid mobility parameters, and permeability indicators, with electrofacies classification performed using optimized machine learning algorithms incorporating SHAP interpretability. Three key innovations are presented: (1) a novel pore-connectivity index derived from T₂ distribution analysis, (2) dynamic permeability corrections accounting for clay-bound fluid effects, and (3) a lithology-independent rock typing system based on pore functionality. Results demonstrate that intervals with similar porosity (11% average) exhibit 3-5× variations in hydrocarbon transmissibility, confirming pore architecture's dominant control. Notably, sandstone subunits representing just 15-20% of thickness contributed 60% of productive capacity through superior macroporosity (T₂ >100 ms). The machine learning framework achieved 92% prediction accuracy for flow units, with SHAP analysis identifying T₂ geometric mean (47% contribution) as the primary controlling parameter. These findings necessitate recharacterization of the Asmari Formation as a pore-connectivity governed system rather than lithology-dependent. Validation through comprehensive core-log integration (R²=0.89, RMSE = 0.32) and production data verification (84% PLT agreement) confirms method reliability. Practical applications include optimized well completion strategies and enhanced recovery in mature fields, while future adaptations encompass real-time implementation and CO₂ storage potential evaluation in analogous carbonate reservoirs. This work establishes a transformative approach for heterogeneous carbonate reservoir evaluation.