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

ABSTRACT: 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) dynami

SUBMITTER: Veysi M 

PROVIDER: S-EPMC12824165 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature