Pore Structure Characterization and Fractal Analysis of Lacustrine Shales: Integrating N2 Adsorption, Mercury Intrusion, and Deep Learning-Assisted FIB–SEM 3D Pore Surface Point Cloud Reconstruction

Lacustrine shales are key targets for shale oil exploration, yet the quantitative characterization of their complex and heterogeneous pore systems remains a significant challenge, constraining effective reservoir evaluation and development. This study investigates lacustrine shales from the Second Member of the Kongdian Formation by integrating N2 adsorption, mercury intrusion porosimetry, and focused ion beam scanning electron microscopy with fractal analysis. A Mamba-based deep learning model was applied to improve two-dimensional (2D) pore segmentation, and three-dimensional (3D) pore surface point clouds were reconstructed for 3D surface fractal characterization to reduce artifacts associated with conventional 3D reconstruction. The results indicate that the pore system is dominated by inorganic pores, mainly irregular interparticle pores and dissolution pores, while organic pores are scarce. Pore sizes are predominantly concentrated in the range of 5 to 200 nm. Adsorption-derived fractal dimensions exhibit systematic lithofacies differences, with D1 and D2 averaging around 2.47 and 2.56, respectively. These trends are consistent with the 3D pore surface fractal dimension derived from pore surface point clouds (mean 2.48), which supplements the bulk statistical results with direct geometric quantification of surface roughness. The heterogeneity of the pore system is influenced by the coupled effects of mineral composition, organic matter content, and diagenesis. Specifically, the enrichment of clay minerals and dolomite increases the irregularity of pore morphology and results in higher fractal dimensions. In contrast, samples enriched in feldspars and calcite are supported by a rigid granular framework that corresponds to lower 3D surface complexity. Ultimately, these fractal dimensions effectively quantify pore network complexity and reservoir heterogeneity in the Kong 2 shales, offering quantitative support for reservoir characterization and lacustrine shale oil exploration.