Digital Pore-Scale Rock Typing Using CT and SEM Image Analysis: An Integrated Approach for Quantifying Heterogeneity in Carbonate Reservoirs; Cretaceous Sarvak Formation, Zagros Basin, Iran

Characterizing the complex pore systems of carbonate reservoirs remains a paramount challenge due to their inherent multi-scale heterogeneity. Traditional petrophysical methods often struggle to accurately predict fluid behavior as they infer pore geometry from indirect measurements. This study develops and validates an integrated digital pore-scale rock typing workflow to quantitatively define and predict this heterogeneity by integrating Computed Tomography (CT) and Scanning Electron Microscopy (SEM) with conventional core and Mercury Injection Capillary Pressure (MICP) data. The methodology was applied to the Sarvak Formation in the Abadan Plain. We first established a geological framework from thin sections, identifying eight microfacies altered by processes ranging from porosity-destructive compaction to enhancing dissolution. Core and MICP data were used to define ten Hydraulic Flow Units (HFUs) and seven Pore Size Classes (PSCs). The core of the workflow involved quantitative CT analysis, which led to the identification of six distinct CT Rock Types (CTRTs), each representing a unique 3D pore network. Finally, MICP data were synthesized to define five definitive Pore Facies, resolving critical ambiguities in pore-throat structure. Quantitative comparison of pore facies, HFUs, PSCs, and CT-based rock types shows that, although all schemes are strongly associated (Cramér’s V ≈ 0.50–0.71), their sample-level agreement is only moderate (ARI ≈ 0.15–0.30; NMI ≈ 0.27–0.46), with CT-based rock types providing the strongest integrative link between facies, pore geometry, and flow behavior. Each rock type is grounded in a specific geological origin, explaining the pore system's genesis—from tight, microporous mudstones to highly connected, vuggy and fractured grainstones. In conclusion, the integrated workflow successfully bridges the gap between micro-scale pore geometry and macro-scale fluid flow. By unifying CT, SEM, and MICP data, it provides a more robust and predictive model that directly links 3D pore structure to storage capacity and flow dynamics, offering a framework for populating geological models and guiding reservoir development strategies.