Digital core analysis (DCA) uses high-resolution micro-CT imaging to examine pore structures and fluid flow in rocks, offering faster results, sample preservation, and multi-property measurements from a single core. However, DCA data are at microscopic scale and must be upscaled to core, log, and reservoir scales for field-level reservoir modelling. This study used DCA data from the Otway Formation, Australia, to enhance reservoir characterisation and carbon dixoide (CO2) plume prediction. A new randomised, iterative upscaling method was developed and implemented in the open-source MATLAB Reservoir Simulation Toolbox. This approach upscaled pore-scale properties, such as capillary pressure and relative permeability, while incorporating the proportions and spatial order of different rock types (facies). Results showed that both facies proportion and sequence strongly influenced the upscaled capillary pressure and relative permeability during drainage (CO2 injection) and imbibition (brine reinjection). By randomising sub-facies proportions and order, the method captured broader geological variability, which can yield more accurate, efficient, and reliable reservoir simulations. The proposed method provides a robust framework for linking micro-scale DCA data to field-scale models, supporting more precise and secure geological CO2 storage.
Aslannezhad et al. (Wed,) studied this question.