Mohammadmoradi, Peyman (University of Calgary) | Bashtani, Farzad (University of Calgary) | Goudarzi, Banafsheh (University of Calgary) | Taheri, Saeed (University of Calgary) | Kantzas, Apostolos (University of Calgary)
Due to the computational simplicity and time efficiency, pore network and morphological techniques are practical approaches for characterization of pore-scale structures. The methods are quasi-static and exploit pore space spatial statistics during invasion processes. Here, both procedures are evaluated applying the workflows to pore-level micro- and sub micro-scale images of Sandstone, Carbonate and Shale formations. A statistical approach is also utilized to improve the accuracy of Shale characteristics by spatial restoration of fragmentary parts of organic matter. Post-processing predictions include relative permeability and capillary pressure curves, absolute permeability, formation factor, and thermal connectivity. According to the results, the accuracy of pore network modeling in characterization of micro-CT images is compromised by the presence of limited number of network elements, ignoring the resistance of pore elements, multi-scale structures, and tight/weak connections represented by limited voxels. Pore network extraction affects the accuracy of petrophysical predictions and fluid occupancy profiles and also ignores the thermal and electrical properties of solid structure, including calcite, kerogen, quartz, etc. The pore morphological approach easily deals with a variety of rock configurations and resolutions and preserves connectivity and details of original images having more geometrical features than the pore network modeling. However, it predicts limited step-wised data points and realizations sourcing from its voxel-based nature. In addition, direct simulations confirm that stochastic conditional reconstruction of organic matter inside shale sub-volumes remarkably boosts the pore space connectivity and affects its predicted hydraulic properties.