Geologic storage of CO ₂ and H ₂ are climate‐positive techniques for meeting the energy transition. While similar formations could be considered for both gases, the flow dynamics could differ due to differences in their thermophysical properties. We conduct a rigorous pore‐scale study of water/CO ₂ and water/H ₂ systems at relevant reservoir conditions in a Bentheimer rock sample using the lattice Boltzmann method to quantify the effects of capillary, viscous, inertial, and wetting forces during gas invasion. At similar conditions, H ₂ invasion is weaker compared to CO ₂ due to unfavorable viscosity ratios. Increasing flow rate, however, increases the breakthrough saturation for both gas systems in the range of capillary numbers studied. At isolated conditions of flow rate, viscosity ratio, and wettability, local inertial effects are found to be critical and show consistent increase in the invaded gas saturation. The effect of inertial forces persits for both gases across all field conditions tested.

Multiphase flow properties of fractures are important in engineering applications such as hydraulic fracturing, evaluating the sealing capacity of caprocks, and the productivity of hydrocarbon-bearing tight rocks. Due to the computational requirements of high fidelity simulations, investigations of flow and transport through fractures typically rely on simplified assumptions applied to large fracture networks. These simplifications ignore the effect of pore-scale capillary phenomena and 3D realistic fracture morphology (for instance, tortuosity, contact points, and crevasses) that lead to macro-scale effective transport properties. The effect of these properties can be studied through lattice Boltzmann simulations, but they require high performance computing clusters and are generally limited in their domain size. In this work, we develop a technique to represent 3D fracture geometries and fluid distributions in 2D without losing any information. Using this innovative approach, we present a specialized machine learning model which only requires a few simulations for training but still accurately predicts fluid flow through 3D fractures. We demonstrate our technique using simulations of a water filled fracture being displaced by supercritical CO${}_2$. By generating highly efficient simulations of micro-scale multiphase flow in fractures, we hope to investigate a wide range of fracture types and generalize our method to be incorporated into larger discrete fracture network simulations.

There is a growing need for disposal of high‐level nuclear waste. To reduce uncertainty associated with brine availability to repository excavations in salt formations, a collaboration between Sandia, Los Alamos, and Lawrence Berkeley National Laboratories is performing a series of borehole‐scale coupled process tests. Here, we report on the first round of the Brine Availability Test in Salt (BATS) project, a “shakedown” experiment called Phase 1s. Experimental testing included placing a resistive heater, a 260‐W radiative heater, and a 750‐W radiative heater within previously drilled horizontal boreholes at the Waste Isolation Pilot Plant (WIPP) while monitoring temperature and water inflow. The experiments successfully achieved the targeted temperature of 120 °C when using the 750‐W radiative heater. Simulations using FEHM (Finite Element Heat and Mass transfer code) and TOUGH‐FLAC (Transport Of Unsaturated Groundwater and Heat–Fast Lagrangian Analysis of Continua) were able to accurately predict the coupled thermo–hydro–mechanical–chemical response of salt, matching the observed temperature and brine production. Due to the extremely low permeability of salt, these systems take many years to reach steady state when perturbed by mining activities. Long‐term numerical simulations are used to develop the initial pressure and saturation conditions. The inclusion of a damaged rock zone with higher permeability around the borehole also affects the saturation and pressure distributions and plays an important role in dissipating the potential for thermal pressurization. Knowledge gained from this round of experimentation and simulation will be used to conduct the next BATS project experiment in new boreholes at WIPP.

This study introduces a hybrid fluid simulation approach that integrates generative diffusion models with physics‐based simulations, aiming at reducing the computational costs of flow simulations while still honoring all the physical properties of interest. Pore‐scale simulations enhance our understanding of applications such as assessing hydrogen and storage efficiency in underground reservoirs. Nevertheless, they are computationally expensive and the presence of non‐unique solutions can require multiple simulations within a single geometry. To overcome the computational cost hurdle, we propose a method that couples generative diffusion models and physics‐based simulations. While training the data‐driven model, we simultaneously generate initial conditions and perform physics‐based simulations using these. This integrated approach enables us to receive real‐time feedback on a single compute node equipped with both CPUs and GPUs. By efficiently managing these processes within a single compute node, we can continuously monitor performance and halt training once the model meets the specified criteria. To test our model, we generate realizations in a real Berea sandstone fracture which shows that our technique is up to 4.4 times faster than commonly used flow simulation initializations.