Machine learning-driven permeability prediction in carbonates and sandstones using NMR relaxation dataOA

Nuclear Magnetic Resonance(NMR)has proven to be a powerful tool for in-situ permeability quantification however,it typically requires laboratory calibration,and its accuracy is strongly influenced by rock type and pore system heterogeneity.Existing NMR-based permeability studies are often limited by small datasets,commonly restricted to a single lithology(sandstone or carbonate),and rarely investigate whether permeability prediction is more reliable using the full transverse relaxation time(T_(2))distribution(spectrum)or NMR-derived parameters(e.g.,minimum,maximum,peak T_(2)…etc).As a result,the generalization of existing formulations across diverse geological settings remains limited.In this study,we address these gaps by developing machine learning models trained on a large and heterogeneous dataset of 308 core samples,including both sandstones and carbonates from the US,France,Middle East,and China.The dataset spans wide porosity(0.10-32.91%)and permeability(0.0003-15,400 mD)ranges,ensuring applicability across heterogeneous rock systems.Two al-gorithms,namely:Multilayer Perceptron(MLP)and eXtreme Gradient Boosting(XGB),are evaluated for pre-dicting matrix permeability from NMR data.Model performance is compared using three input configurations:(i)NMR parameters extracted from the T_(2) distribution,(ii)the full T_(2) spectrum,and(iii)a combination of both extracted parameters and the full spectrum.This comparison assists in evaluating the added value of the full relaxation distribution,which captures pore-scale information that may be overlooked in simplified parameters.The results show that XGB consistently outperformed MLP,with the best performance achieved when combining the full T_(2) spectrum and extracted parameters,yielding an R^(2) of 0.86 and a root mean square error(RMSE)of 0.51 in log permeability prediction(corresponding to approximately 3 mD).Incorporating lithology(rock type:carbonate versus sandstone)as an input has only a minor effect on XGB performance,suggesting prior litho-logical classification is not strictly required for accurate permeability prediction.These results indicate that the proposed approach can be generalized across sedimentary rocks and applied to both sandstone and carbonate reservoirs.