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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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We present reactive flow and transport simulations of core-scale serpentinization of forsteritic, Fe-bearing olivine under hydrothermal conditions. The model captures fluid flow, evolving porosity–permeability, and redox-controlled H 2 production in a cylindrical core over 55 days at 245 °C and 37 bar. Key processes include olivine dissolution, precipitation of serpentine and magnetite, and oxidation of Fe2+ to Fe 3+ . Here, results show strong coupling among flow velocity, alteration front propagation, and spatially heterogeneous H 2 generation. Elevated H2 concentrations align with inlet-localized magnetite precipitation, consistent with experiments. Secondary mineral formation reduces porosity and permeability, altering transport pathways. Reactive flow at the fluid–solid interface governs H 2 generation rates and distribution. Dissolved SiO2 promotes serpentine and talc formation by suppressing brucite, while bicarbonate extends reaction duration and moderates surface complexation effects.
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Abstract In the Arctic, the spatiotemporal variation of net methane uptake in upland soils depends on unresolved interactive controls between edaphic and microbial factors not yet included in current models, underpinning the uncertainty of upscaling the Arctic methane budget. Here we show that upland soils in Greenland are consistent methane sinks (−1.83 ± 0.19 nmol methane g −1 dw d −1 ) across a N-S (64–83 °N) pedoclimatic transect. We demonstrate that methane oxidizers abundance, soil pH, and available soil copper are important controls on the spatial variation in methane oxidation. We revised a soil biogeochemical model with a high-resolution land classification and meteorological data for Greenland and tested it against our methane uptake measurements. The model simulated well the magnitudes of observed methane uptake but not the spatial variation across all sites. This work provides novel insights into the controls of methane uptake, which are critical for the accuracy of methane budgets.
This study presents a comprehensive experimental dataset on the temperature-dependent diffusion of hydrogen (H 2 ) in reservoir rocks. The results demonstrate that H 2 diffuses through reservoir rocks up to 100 times faster than methane (CH 4 ).
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Conference presentation at 2023 Rocky Mountain Section-American Association of Petroleum Geologists (RMS-AAPG) Annual Meeting, Bismarck, ND, June 4–6, 2023. Successful Carbon Capture, Utilization, and Storage (CCUS) projects depend on clear and applicable regulatory requirements that are thoroughly understood by project developers. It is evident that states are best positioned to regulate CO 2 storage through comprehensive regulations and permitting efficiencies. Further, tax incentives and an emphasis on low-carbon fuels compound to make CCUS more attractive to investors and states alike.
Ensemble-based stochastic gradient methods, such as the ensemble optimization (EnOpt) method, the simplex gradient (SG) method, and the stochastic simplex approximate gradient (StoSAG) method, approximate the gradient of an objective function using an ensemble of perturbed control vectors. These methods are increasingly used in solving reservoir optimization problems because they are not only easy to parallelize and couple with any simulator but also computationally more efficient than the conventional finite-difference method for gradient calculations. In this work, we show that EnOpt may fail to achieve sufficient improvement of the objective function when the differences between the objective function values of perturbed control variables and their ensemble mean are large. On the basis of the comparison of EnOpt and SG, we propose a hybrid gradient of EnOpt and SG to save on the computational cost of SG. We also suggest practical ways to reduce the computational cost of EnOpt and StoSAG by approximating the objective function values of unperturbed control variables using the values of perturbed ones. We first demonstrate the performance of our improved ensemble schemes using a benchmark problem. Results show that the proposed gradients saved about 30–50% of the computational cost of the same optimization by using EnOpt, SG, and StoSAG. As a real application, we consider pressure management in carbon storage reservoirs, for which brine extraction wells need to be optimally placed to reduce reservoir pressure buildup while maximizing the net present value. Results show that our improved schemes reduce the computational cost significantly.
We perform a set of high-fidelity simulations of geochemical reactions within three-dimensional discrete fracture networks (DFN) and use various machine learning techniques to determine the primary factors controlling mineral dissolution. The DFN are partially filled with quartz that gradually dissolves until quasi-steady state conditions are reached. At this point, we measure the quartz remaining in each fracture within the domain as our primary quantity of interest. We observe that a primary sub-network of fractures exists, where the quartz has been fully dissolved out. This reduction in resistance to flow leads to increased flow channelization and reduced solute travel times. However, depending on the DFN topology and the rate of dissolution, we observe substantial variability in the volume of quartz remaining within fractures outside of the primary subnetwork. This variability indicates an interplay between the fracture network structure and geochemical reactions. We characterize the features controlling these processes by developing a machine learning framework to extract their relevant impact. Specifically, we use a combination of high-fidelity simulations with a graph-based approach to study geochemical reactive transport in a complex fracture network to determine the key features that control dissolution. We consider topological, geometric and hydrological features of the fracture network to predict the remaining quartz in quasi-steady state. We found that the dissolution reaction rate constant of quartz and the distance to the primary sub-network in the fracture network are the two most important features controlling the amount of quartz remaining. This study is a first step towards characterizing the parameters that control carbon mineralization using an approach with integrates computational physics and machine learning.
Mineral reactive surface area (RSA) is one of the key factors that control mineral reactions, as it describes how much mineral is accessible and can participate in reactions. This work aims to evaluate the impact of mineral RSA on numerical simulations for CO2 storage at depleted oil fields. The Farnsworth Unit (FWU) in northern Texas was chosen as a case study. A simplified model was used to screen representative cases from 87 RSA combinations to reduce the computational cost. Three selected cases with low, mid, and high RSA values were used for the FWU model. Results suggest that the impact of RSA values on CO2 mineral trapping is more complex than it is on individual reactions. While the low RSA case predicted negligible porosity change and an insignificant amount of CO2 mineral trapping for the FWU model, the mid and high RSA cases forecasted up to 1.19% and 5.04% of porosity reduction due to mineral reactions, and 2.46% and 9.44% of total CO2 trapped in minerals by the end of the 600-year simulation, respectively. The presence of hydrocarbons affects geochemical reactions and can lead to net CO2 mineral trapping, whereas mineral dissolution is forecasted when hydrocarbons are removed from the system.
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Presentation for meeting with Deep Fission.
Fact sheet explaining the process of drilling a test hole to collect data for the North Dakota CarbonSAFE project. North Dakota CarbonSAFE is an Energy & Environmental Research Center project, conducted in partnership with the U.S. Department of Energy.
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