Digital rock modeling of deformed multi-scale media in deep hydrocarbon reservoirs based on in-situ stress-loading CT imaging and U-Net deep learning

Deep and ultra-deep hydrocarbon reservoirs are regarded as an attractive target for onshore oil and gas exploration and development in China. The multi-scale fractures are widely distributed in this type of reservoir. The fluid-solid coupling effect is strong, which can greatly affect the fluid flow, resulting in a low hydrocarbon recovery with the average value less than 15%. To explore the underlying flow dynamics during efficient development of deep and ultra-deep hydrocarbon reservoirs, it is of great importance to characterize the multi-scale fracture-pore structure evolution of rock affected by strong geo-stress field variation. To tackle the above issues, an actual rock drilled from a typical ultra-deep reservoir in Tarim Basin are selected to conduct in-situ stress-loading computed tomography (CT) scanning experiments, and the CT gray images of rock microstructure under different dynamic loading conditions are obtained. A fully convolutional neural network (U-Net) deep learning segmentation algorithm is introduced to accurately distinguish the rock skeleton, pore space and fractures in deep rock. The three-dimensional (3-D) digital rock model of deformed multi-scale fracture-porous media under different dynamic loading conditions is ultimately established to investigate the evolution of fracture morphology and deep rock microstructure as the in-situ stress is gradually loaded. It indicates that, the U-Net deep learning semantic segmentation algorithm can accurately segment CT images of deep rocks, and the established 3D digital rock model of fractured porous media can accurately represent the pore-throat distribution, fracture morphology, and pore-scale topological connectivity. At the initial stage of stress loading, there are few micro-fractures inside the rock, and the fracture connectivity is poor. Due to effect of rock compaction, the average pore throat radius increases while pore throat ratio, coordination number and tortuosity greatly decrease. As the effective stress is gradually loaded, the micro-fractures begin to propagate, leading to stronger heterogeneity of micro-fractures’ distribution and better topological connectivity, and both the coordination number and tortuosity gradually increase. After the deep rock is fractured, micro-fractures run through a fracture network and all the above topological parameters increase greatly.