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We present a hybrid digital rock analysis workflow integrating multiscale pore network modeling (PNM), AI-supported 2D–3D model generation, and anchoring to laboratory data to predict petrophysical properties, capillary pressure, and relative permeability. High-resolution micro-CT and SEM analyses are used to extract pore networks across multiple length scales, while unresolved porosity below micro-CT resolution is captured through 2D–3D reconstruction based on SEM images and incorporated into a multi-scale pore network model (MSPNM).

The model integrates fine-scale heterogeneities and macropore connectivity to enable physically representative two-phase flow simulations. Anchoring to laboratory measurements of porosity, absolute permeability, and mercury injection capillary pressure (MICP) is used to calibrate and validate the digital model prior to SCAL simulations. A case study demonstrates the full workflow from imaging to multi-scale two-phase displacement simulations. Applied to complex samples, the methodology provides accurate, non-destructive, and reproducible SCAL property estimation.

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