Pore-network models are widely used to describe pore-scale flow in porous media, and their reliability depends critically on accurate extraction of pore and throat structures. A new extraction framework, termed the topological pore-network finder, is proposed in this work, which combines topological data analysis, medial access path search, and flashlight search medial axis. The topological data analysis is used to identify pore connectivity and cluster the void space, thereby providing robust initial pore centers. The medial access path search method then traces strings between connected pore centers along the medial axis, while the flashlight search medial axis method is used to refine the resulting paths and improve computational efficiency. The method is validated using toy porous media, two- and three-dimensional digital rock samples. Sensitivity analyses show that the pore-network finder is stable with respect to image resolution and string discretization. Compared with the classical maximal-ball method, the pore-network finder achieves at least an order-of-magnitude acceleration while preserving the main geometric statistics and flow-response characteristics of the extracted networks. In addition, because the method operates in continuous space and can reuse information from previous states, it is well suited to quasi-dynamic updates during deformation. The pore-network finder therefore provides an efficient and accurate tool for pore-network extraction and subsequent pore-scale characterization in geo-energy systems.

In order to examine the heterogeneous nucleation and growth dynamics of mineral precipitation in reactive transport systems, as well as the evolution of key upscaling parameters, such as porosity and permeability, this study employs a model that integrates pore-scale reactive transport with arbitrary Lagrangian-Eulerian method. This model incorporates a heterogeneous probabilistic nucleation process based on classical nucleation theory, which is used to parametrically simulate the nucleation and growth processes of individual mineral particles within the reactive transport. The findings indicate that fluid velocity, along with nucleation and mineral growth rates, plays critical roles in determining the pattern and spatial distribution of precipitates. Nucleation promotes irregularities in the precipitate pattern and reduces the influence of flow on the spatial distribution of precipitate formation across particle surfaces. Precipitation on the surface of a single mineral particle within a pore channel is more accurately governed by a power law model, which captures the evolutionary relationship between porosity and permeability in porous media with periodic structures.

Understanding the complex behavior of fractured rock systems is critical for applications in energy development, geological sequestration, and tunnel construction. Microscale fracture surface morphology influences flow and mechanical behaviors, while upscaling frameworks. Despite progress in hydro-mechanical and thermo-hydro-mechanical coupling models, two-way mechanical-chemical interactions remain underexplored. Discrete fracture networks offer a robust statistical framework for modeling subsurface fracture systems. Advances in machine learning have accelerated the simulation and optimization of fractured geothermal systems, addressing the computational limitations of high-fidelity models. These methods support multi-objective design, enhance life cycle assessments, and provide insights into optimal geothermal management strategies. Fractured rocks serve as preferential pathways for fluid flow and heat transport, significantly influencing permeability and mechanical stability. However, the inherent complexity of coupled thermo-hydro-mechanical-chemical processes in these systems presents major challenges. Nonlinear fracture mechanics, stress perturbations, and chemical interactions drive dynamic changes in fracture connectivity and permeability, further complicated by recursive feedback mechanisms. By integrating numerical tools, machine learning techniques, and advanced discrete fracture network models, the fractured rock system could be optimized and clearly analyzed.

Subsurface gas storage refers to the practice of storing natural gas or other gases in underground reservoirs. It plays a crucial role in ensuring a stable and reliable supply of energy, especially during periods of high demand or supply disruptions. This work collectively highlights the significance of the microscopic and mesoscopic reservoir simulation techniques developed for subsurface gas storage. Specific technology progresses are demonstrated for a better storage of hydrogen and carbon dioxide, which meets well with the current focus on carbon reduction. In particular, molecular dynamics simulations can provide insight for the microscopic mechanisms affecting the adsorption and leakage of stored gas. Pore-network model generated using the advanced algorithm can determine the geological scenario for further flow and transport simulations.

In the last decades, shale gas development has relieved the global energy crisis and slowed global warming problems. The water bridge plays an important role in the process of shale gas diffusion, but the stability of the water bridge in the shale nanochannel has not been revealed. In this work, the molecular dynamics method is applied to study the interaction between shale gas and water bridge, and the stability can be tested accordingly. CO₂ can diffuse into the liquid H₂O phase, but CH₄ only diffuses at the boundary of the H₂O phase. Due to the polarity of H₂O molecules, the water bridge presents the wetting condition according to model snapshots and one-dimensional analyses, but the main body of the water bridge in the two-dimensional contour shows the non-wetting condition, which is reasonable. Due to the effect of the molecular polarity, CO₂ prefers to diffuse into kerogen matrixes and the bulk phase of water bridge. In the bulk of the water bridge, where the interaction is weaker, CO₂ has a lower energy state, implies that it has a good solubility in the liquid H₂O phase. Higher temperature does not facilitate the diffusion of CO₂ molecules, and higher pressure brings more CO₂ molecules and enhances the solubility of CO₂ in the H₂O phase, in addition, a larger ratio of CO₂ increases its content, which does the same effects with higher pressures. The stability of the water bridge is disturbed by diffused CO₂, and its waist is the weakest position by the potential energy distribution.