A novel digital extraction approach of pore network models from carbonates inspired by quantum genetic optimization techniques

Estimating hydraulic properties of carbonate is challenging due to its wider range of porosity and complex pore structures. To investigate the hydraulic properties of carbonate rocks, a novel approach inspired by quantum genetic optimization in this work is proposed to extract the optimal pore network model (PNM), which is applied to simulate the hydraulic properties. The pore network variables, such as porosity, pore and throat sizes, are considered as the optimal parameters, and the capillary pressure curve, breakthrough drainage pressure and permeability are calculated based on the constructed PNM. The computing time (CPU time) and memory usage (RAM usage) for the PNM extraction using the proposed approach and classical methods are compared. Results indicate that the proposed approach shows better computing efficiency than classical methods. Excellent agreements are found between the experimental and simulation results from the proposed and classical methods. The proposed approach provides promising tools to investigate the hydraulic properties of geomaterials.