Enhanced carbon dioxide drainage observed in digital rock under intermediate wetting conditions.

Carbon dioxide (CO 2 ) trapping in capillary networks of reservoir rocks is a pathway to long-term geological storage. At pore scale, CO 2 drainage displacement depends on injection pressure, temperature, and the rock's interaction with the surrounding fluids. Modeling this interaction requires adequate representations of both capillary volume and surface. For the lack of scalable representations, however, the prediction of a rock's CO 2 storage potential has been challenging. Here, we report how to represent a rock's pore space by statistically sampled capillary networks (ssCN) that preserve morphological rock characteristics. We have used the ssCN method to simulate CO 2 drainage within a representative sandstone sample at reservoir pressures and temperatures, exploring intermediate- and CO 2 -wet conditions. This wetting regime is often neglected, despite evidence of plausibility. By raising pressure and temperature we observe increasing CO 2 penetration within the capillary network. For contact angles approaching 90 ∘ , the CO 2 saturation exhibits a pronounced maximum reaching 80 % of the accessible pore volume. This is about twice as high as the saturation values reported previously. For enabling validation of our results and a broader application of our methodology, we have made available the rock tomography data, the digital rock computational workflows, and the ssCN models used in this study.

Full scale, microscopically resolved tomographies of sandstone and carbonate rocks augmented by experimental porosity and permeability values.

We report a dataset containing full-scale, 3D images of rock plugs augmented by petrophysical lab characterization data for application in digital rock and capillary network analysis. Specifically, we have acquired microscopically resolved tomography datasets of 18 cylindrical sandstone and carbonate rock samples having lengths of 25.4 mm and diameters of 9.5 mm. Based on the micro-tomography data, we have computed porosity-values for each imaged rock sample. For validating the computed porosity values with a complementary lab method, we have measured porosity for each rock sample by using standard petrophysical characterization techniques. Overall, the tomography-based porosity values agree with the measurement results obtained from the lab, with values ranging from 8% to 30%. In addition, we provide for each rock sample the experimental permeabilities, with values ranging from 0.4 mD to above 5D. This dataset will be essential for establishing, benchmarking, and referencing the relation between porosity and permeability of reservoir rock at pore scale.

CRAFTED: An exploratory database of simulated adsorption isotherms of metal-organic frameworks.

Grand Canonical Monte Carlo is an important method for performing molecular-level simulations and assisting the study and development of nanoporous materials for gas capture applications. These simulations are based on the use of force fields and partial charges to model the interaction between the adsorbent molecules and the solid framework. The choice of the force field parameters and partial charges can significantly impact the results obtained, however, there are very few databases available to support a comprehensive impact evaluation. Here, we present a database of simulations of CO2 and N2 adsorption isotherms on 690 metal-organic frameworks taken from the CoRE MOF 2014 database. We performed simulations with two force fields (UFF and DREIDING), six partial charge schemes (no charges, Qeq, EQeq, MPNN, PACMOF, and DDEC), and three temperatures (273, 298, 323 K). The resulting isotherms compose the Charge-dependent, Reproducible, Accessible, Forcefield-dependent, and Temperature-dependent Exploratory Database (CRAFTED) of adsorption isotherms.

Quantum Informed Machine-Learning Potentials for Molecular Dynamics Simulations of CO2's Chemisorption and Diffusion in Mg-MOF-74.

Among various porous solids for gas separation and purification, metal-organic frameworks (MOFs) are promising materials that potentially combine high CO2 uptake and CO2/N2 selectivity. So far, within the hundreds of thousands of MOF structures known today, it remains a challenge to computationally identify the best suited species. First principle-based simulations of CO2 adsorption in MOFs would provide the necessary accuracy; however, they are impractical due to the high computational cost. Classical force field-based simulations would be computationally feasible; however, they do not provide sufficient accuracy. Thus, the entropy contribution that requires both accurate force fields and sufficiently long computing time for sampling is difficult to obtain in simulations. Here, we report quantum-informed machine-learning force fields (QMLFFs) for atomistic simulations of CO2 in MOFs. We demonstrate that the method has a much higher computational efficiency (∼1000×) than the first-principle one while maintaining the quantum-level accuracy. As a proof of concept, we show that the QMLFF-based molecular dynamics simulations of CO2 in Mg-MOF-74 can predict the binding free energy landscape and the diffusion coefficient close to experimental values. The combination of machine learning and atomistic simulation helps achieve more accurate and efficient in silico evaluations of the chemisorption and diffusion of gas molecules in MOFs.