Velocity deviation-based pore type classification in an Iranian carbonate gas reservoir using multiscale integrated data

Abstract

This study presents an integrated approach to pore type classification in a carbonate gas reservoir from the Dalan/Kangan formations in Iran, utilizing velocity deviation log (VDL) analysis derived from thin sections, conventional well logs, Nuclear Magnetic Resonance (NMR) data, and seismic inversion. Petrographic analysis of thin sections provided the foundational classification of pore types across the Dalan/Kangan reservoir, identifying moldic, intergranular, intercrystalline, matrix-supported, and minor contributions from intragranular, vuggy, and fracture types. Moldic porosity, particularly within oolitic and bioclastic facies, is the most pervasive but exhibits variable connectivity due to diagenetic overprints. These observations served as a benchmark for evaluating the results of subsequent geophysical methods. VDL was first computed using standard petrophysical logs, followed by an enhanced estimation incorporating total porosity from NMR measurements. The spatial distribution of VDL was then used as a key input for post-stack seismic inversion, enabling the prediction of pore types across the reservoir volume. All methods consistently identify the dominant pore types across the studied intervals, showing strong agreement with thin section observations. Both conventional and NMR-based VDLs confirm the prevalence of matrix-supported and moldic porosity, reinforcing the reliability of integrated interpretation. However, in some intervals, the NMR-derived VDL profile suggests the presence of compliant pore system/fracture indicator not observed in petrographic analysis. This discrepancy is likely due to the NMR tool’s sensitivity to bound and free fluid distributions, which may mimic fracture signatures in zones with enhanced microporosity or subtle structural heterogeneities. This workflow enhances the reliability of pore type classification in complex carbonate systems and provides a robust framework for reservoir characterization and modeling. Overall, the study underscores the value of combining advanced petrophysical measurements with seismic interpretation to develop more accurate subsurface models and support informed reservoir development strategies in Iranian carbonate gas fields.