A pore network-based multiscale coupled model for rapid permeability prediction of tight sandstone gas

Tight sandstone has multiscale pore structures. Gas transport in tight sandstones involves several length scales and the complicated physics. Gas transport characteristics in tight sandstone can be represented by apparent gas permeability. Microstructure-based accurate and efficient calculation of gas permeability is challenging. By combining CT and SEM images along with statistical analysis, we present a novel pore network-based multiscale coupled model (MCPNM) to rapidly predict the apparent gas permeability. CT images are adopted to extract the large-scale pore network (LPNM) and the clay component, and SEM image is used to get the properties of smallscale pores. Upscaled model (UM) of small-scale pores is built via statistical analysis and then assigned to the clay domains. The LPNM and UM are coupled as MCPNM by the cross-scale connection structure with variable diameter. The pore spaces at several length scales and the flow characteristics in them are included in the MCPNM. We validate the MCPNM by comparing the calculated apparent gas permeability to the results of available multiscale pore network models and the experimental data. Compared with the available multiscale pore network models, MCPNM solves the accuracy/efficiency trade-off of tight gas permeability prediction. The effects of pressure, temperature, and gas type on gas permeability are studied. The MCPNM simplifies the permeability calculation process and can accurately and rapidly predict tight sandstone gas permeability.