CO₂ injection into oil reservoirs is expected to achieve enhanced oil recovery along with the benefit of carbon storage, while the application potential of this strategy for shale reservoirs is unclear. In this work, a numerical model for multiphase flow in shale oil reservoirs is developed to investigate the impacts of nano-confinement and oil composition on shale oil recovery and CO₂ storage efficiency. Two shale oils with different maturity levels are selected, with the higher-maturity shale oil containing lighter components. The results indicate that the saturation pressure of the lower-maturity shale oil continues to increase with increasing CO₂ injection, while that of the higher-maturity shale oil continues to decrease. The recovery factor and CO₂ storage rate for higher-maturity shale oil after CO₂ huff-n-puff are 12.02% and 44.76%, respectively, while for lower-maturity shale oil, these are 4.41% and 69.33%, respectively. These data confirm the potential of enhanced oil recovery in conjunction with carbon storage in shale oil reservoirs. Under the nano-confinement impact, a decrease in the oil saturation in the matrix during production is reduced, which leads to a significant increase in oil production and a significant decrease in gas production. The oil production of the two kinds of shale oil is comparable, but the gas production of higher-maturity shale oil is significantly higher. Nano-confinement shows a greater impact on the bubble point pressure of higher-maturity shale oil and a more pronounced impact on the production of lower-maturity shale oil.

Digital rock analysis is a promising approach for visualizing geological microstructures and understanding transport mechanisms for underground geo-energy resources exploitation. Accurate image reconstruction methods are vital for capturing the diverse features and variability in digital rock samples. Stable diffusion, a cutting-edge artificial intelligence model, has revolutionized computer vision by creating realistic images. However, its application in digital rock analysis is still emerging. This study explores the applications of stable diffusion in digital rock analysis, including enhancing image resolution, improving quality with denoising and deblurring, segmenting images, filling missing sections, extending images with outpainting, and reconstructing three-dimensional rocks from two-dimensional images. The powerful image generation capability of diffusion models shed light on digital rock analysis, showing potential in filling missing parts of rock images, lithologic discrimination, and generating network parameters. In addition, limitations in existing stable diffusion models are also discussed, including the lack of real digital rock images, and not being able to fully understand the mechanisms behind physical processes. Therefore, it is suggested to develop new models tailored to digital rock images for further progress. In sum, the integration of stable diffusion into digital core analysis presents immense research opportunities and holds the potential to transform the field, ushering in groundbreaking advances.