Flow characteristics and phase distribution of concentrated brine storage in mining area have been core factors linking to the leakage risk assessment and ecological evaluation. Notably, saturation plays a crucial role in impacting the flow characteristics and distribution of brine, while the existence of oil left by mining machines and original reservoir and gas produced from coal bed gas and air has complicated the issue. In this work, we conducted the microfluidic visualization experiments to reveal the saturation distribution during brine storage in mining area. We applied machine learning model to extract saturation data from experimental images with over 95% accuracy. Eventually, we found that the existence of gas significantly impacts on the saturation distribution in micropores accounting for more than 80% contribution. We clarified that the gas production rate of median 200 μL/min impacts the least on saturation variation. Results in this research are of significance for deeper comprehension on three-phase saturation characteristics of concentrated brine storage in mining area.