The immiscible displacement behavior in porous media is crucial for oil recovery and subsurface remediation, yet how wettability influences this process across different pore structures remains unclear. Using a color-gradient lattice Boltzmann model, this study investigates immiscible displacement dynamics in porous media. Heterogeneous porous structures with various degrees of permeability were reconstructed using the quarter structure generation set algorithm, and wettability effects were analyzed with the contact angle set in the range of ${30}^{\circ }$ to ${150}^{\circ }$. The numerical results showed that porous heterogeneity greatly affects the displacement efficiency via permeability-dependent flow pathway optimization. Enhanced efficiency was observed in high-permeability media through low-tortuosity channels, whereas low-permeability systems exhibited reduced efficiency due to capillary trapping in pores with lower flow capacity. Wettability alters displacement patterns via capillary forces – under hydrophilic condition, the displacing fluid preferentially enters smaller pores. Fractal dimension and Euler number were used to quantify flow heterogeneity, revealing that increased permeability reduces flow complexity and improves connectivity. Moreover, permeability heterogeneity and wettability interact to disrupt classical linear flow responses, leading to non-monotonic efficiency trends in low-permeability systems. These findings highlight the importance of pore-scale multiphase flow in heterogeneous media and offer new insights for predicting wettability effects in subsurface flows.

The extension, penetration and expansion of coal and rock fracture structure caused by deep mining are the key factors that cause coal and rock damage.Therefore, the accurate characterization of the fracture structure is one of the core elements for understanding the coal and rock failure mechanism and developing coal and gas green co-mining.In this study, the Waifu2x convolutional neural network model was used to process the fractured coal and rock CT images to obtain CT images with higher resolution.And put forward the "seven-step processing method of fissure coal and rock labeling", which improves the efficiency of labeling, and uses image enhancement technology to expand the training set, which meets the model's requirements for the quantity and quality of the training set.The trained U-Net convolutional neural network is used for image segmentation to extract the fracture information in the coal and rock.Through comparison, it is found that the fracture connectivity, opening and distribution obtained by this method are closer to the real CT images, and the four quantitative indicators of the extracted fractures are better than other extraction methods.This study can provide a research foundation for the physical and mechanical behavior and mechanism analysis of fractured coal.

In this paper, an experimental platform was developed for transparent analysis of the multiphase displacement behavior. The 3D printing technology was used to rapidly prepare transparent models with complex discontinuous structures inside. The experimental teaching methods of contact angle measurement, surface tension measurement and multiphase displacement behavior transparent analysis were established, which improved the experimental teaching platforms of engineering fluid mechanics. By conducting multiphase displacement experiments, students can deepen their understanding of textbook theoretical knowledge, broaden their academic horizons, and improve their subjective initiative and innovation ability.