Electrofacies analysis based on the combination of microscopic pore structure and machine learning –An example of nuclear magnetic resonance while drilling in the Bohai Sea’s Guantao Formation

Abstract Electrofacies analysis is a key component of reservoir characterization because it links well-log responses to lithological and petrophysical properties; however its effectiveness is often limited by an overreliance on macroscopic logging signals. In heterogeneous reservoirs, microscopic pore structures exert a decisive influence on fluid storage and flow, and overlooking this control can lead to ambiguous or geologically inconsistent facies classifications. To overcome this limitation, this study integrates nuclear magnetic resonance while drilling (NMR-WD) T2 spectrum features with conventional logging data, allowing pore architecture to be quantified and incorporated directly into electrofacies analysis. Multidimensional pore-structure parameters are first extracted from the T2 spectrum and subsequently reduced through principal component analysis (PCA) to retain the dominant microstructural information while minimizing redundancy. Clustering is then carried out using an improved K-means++ algorithm, which enhances stability and reduces sensitivity to initial conditions, a common issue in unsupervised classification. The resulting electrofacies are validated through correlation with core-derived lithological data, ensuring geological consistency, and Fisher discriminant analysis is further applied to establish a predictive model for uncored intervals. The results indicate that the inclusion of NMR-WD-derived microstructural parameters significantly improves electrofacies discrimination, particularly in reservoirs where lithological differences are subtle but pore-scale heterogeneity is pronounced. Overall, this pore-aware framework advances traditional electrofacies analysis by bridging macroscale logging responses and microscale reservoir characteristics, providing a reliable and transferable tool for evaluating complex reservoirs and supporting informed exploration and development decisions.