Pore Network and Morphological Characterization of Pore-Level Structures
Mohammadmoradi, P., Bashtani, F., Goudarzi, B., Taheri, S., Kantzas, A.
SPE 184964, presented at the SPE Canada Heavy Oil Technical Conference, Calgary, Alberta, Canada, February 15–16, 2017.
Due to the computational simplicity and time efficiency, pore network and morphological techniques are two practical approaches for characterization of pore-scale microstructures. The methods are quasi-static and exploit pore space spatial statistics to simulate pore invasions. Here, both procedures are evaluated applying the workflows to pore-level micro-scale subdomains of Sandstone, Carbonate and Shale formations. A statistical approach is also utilized to improve the accuracy of Shale characterization by spatial restoration of fragmentary parts of organic matter. Post-processing results include relative permeability and capillary pressure curves, absolute permeability, formation factor, and thermal connectivity. The results appear to suggest that the accuracy of pore network modeling in the characterization of subdomains 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 an inadequate number of voxels. Pore network extraction negatively affects the accuracy of petrophysical predictions and ignores solid matrix and its thermal and electrical properties. The pore morphological approach accurately reproduces the fluid occupancies, efficiently 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 improves the accuracy of predictions