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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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At least 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Impacts of urbanization, antecedent rainfall event, and cyclone tracks on extreme floods at Houston reservoirs during Hurricane Harvey

The objective of this study is to evaluate the effects of urbanization, antecedent rainfall event (ARE), and varying cyclone tracks on the streamflow—and thus the subsequent reservoir status—during the floods caused by Hurricane Harvey in August-September 2017. Through a hydrological modeling approach, we examined how these factors influenced the inflows, peak pool elevations and outflows of the two most important detention reservoirs in the Houston region, the Addicks and Barker Reservoirs. A high-resolution rainfall reanalysis dataset for extreme storm events, along with a suite of synthetic rainfall values from a variety of storm tracks, were adopted to represent both the truth and the maximum possible rainfall during the Hurricane Harvey period. Results showed the following: Urbanization only led to slight increases in peak inflows, not necessarily leading to an increase in peak pool elevations. ARE contributed to the peak inflow and pool elevation slightly. In contrast, if the cyclone had followed the most adverse track consistent with earlier forecasts (all else being equal), the total volumetric flow into the two reservoirs could have been significantly larger (37.00% and 48.70% respectively), thus increasing the peak pool elevations by 1.06 and 1.37 meters respectively. These results suggest that large uncertainties exist for flood management at a watershed scale during hurricanes, because of the uncertainties with regard to the cyclone track. This is true even if storm-relative precipitation rates could be predicted perfectly.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Magnetotelluric Investigations of the Kilauea Volcano, Hawaii

In 2002 and 2003 a collaborative effort was undertaken between Lawrence Berkeley National Laboratory, Sandia National Laboratories, the U.S. Geological Survey (USGS) Menlo Park, the USGS Hawaiian Volcano Observatory, and Electromagnetic Instruments Inc. to study the Kilauea volcano in Hawaii using the magnetotelluric (MT) technique. The work was motivated by a desire to improve understanding of the magma reservoirs and conduits within Kilauea and the East and Southwest Rift zones, which has implications for understanding Kilauea's plumbing system. An improved understanding of the rift zones has implications in understanding large-scale landslides that are generated in the Hilina Slump, which produce significant impacts on coastal communities. Up to eight stations operated simultaneously, with multiple remote reference sites, and data were processed using multi-station robust processing techniques. Data was acquired at 70 sites over the Southwest and East rift zones. Good to excellent quality data were obtained even in the harshest conditions, such as those encountered on the fresh lava flows of the East Rift Zone, where electrical contact resistances are on the order of 100 kΩ. A three-dimensional (3D) MT model study was done to guide interpretation of the observed MT measurements. Synthetic modeling demonstrates that conductive bodies in the upper 3 km can be spatially resolved where MT station sampling is good. Resistivity anomalies in the 3D inversions have a high degree of spatial correlation with previously published seismic velocity anomalies beneath Kilauea. Melt fractions between 0.096 and 0.117 are calculated for the Kilauea and Pu'u'ō'ō low resistivity anomalies, respectively.

58 GEOSCIENCES↗
NASA NTRS2002

Analysis of Salinity Intrusion in the San Francisco Bay-Delta using a GA- Optimized Neural Net, and Application of the Model to Prediction in the Elkhorn Slough Habitat

The San Francisco Bay Delta is a large hydrodynamic complex that incorporates the Sacramento and San Joaquin Estuaries, the Burman Marsh, and the San Francisco Bay proper. Competition exists for the use of this extensive water system both from the fisheries industry, the agricultural industry, and from the marine and estuarine animal species within the Delta. As tidal fluctuations occur, more saline water pushes upstream allowing fish to migrate beyond the Burman Marsh for breeding and habitat occupation. However, the agriculture industry does not want extensive salinity intrusion to impact water quality for human and plant consumption. The balance is regulated by pumping stations located alone the estuaries and reservoirs whereby flushing of fresh water keeps the saline intrusion at bay. The pumping schedule is driven by data collected at various locations within the Bay Delta and by numerical models that predict the salinity intrusion as part of a larger model of the system. The Interagency Ecological Program (IEP) for the San Francisco Bay/Sacramento-San Joaquin Estuary collects, monitors, and archives the data, and the Department of Water Resources provides a numerical model simulation (DSM2) from which predictions are made that drive the pumping schedule. A problem with this procedure is that the numerical simulation takes roughly 16 hours to complete a C:~ prediction. We have created a neural net, optimized with a genetic algorithm, that takes as input the archived data from multiple stations and predicts stage, salinity, and flow at the Carquinez Straits (at the downstream end of the Burman Marsh). This model seems to be robust in its predictions and operates much faster than the current numerical DSM2 model. Because the system is strongly tidal driven, we used both Principal Component Analysis and Fast Fourier Transforms to discover dominant features within the IEP data. We then filtered out the dominant tidal forcing to discover non-primary tidal effects, and used this to enhance the neural network by mapping input-output relationships in a more efficient manner. Furthermore, the neural network implicitly incorporates both the hydrodynamic and water quality models into a single predictive system. Although our model has not yet been enhanced to demonstrate improve pumping schedules, it has the possibility to support better decision-making procedures that may then be implemented by State agencies if desired. Our intention is now to use this model in the smaller Elkhorn Slough complex near Monterey Bay where no such hydrodynamic model currently exists. At the Elkhorn Slough, we are fusing the neural net model of tidally-driven flow with in situ flow data and airborne and satellite remote sensation data. These further constrain the behavior of the model in predicting the longer-term health and future of this vital estuary.

Thompson, David E.↗
DOE OSTI2025

Complex Dependence of Calcite Crack Kinetics on Salinity: The Role of DLVO and Hydration Forces

Abstract Subcritical crack growth (SCG) plays an important role in many geological processes such as delayed earth rupture and rock weathering. The complex dependency of SCG on the in‐crack fluid chemistry, however, is still poorly understood. In this study, we utilize the newly developed surface force‐based fracture theory (SFFT) to elucidate the relative contributions of surface forces and solute transport to the crack growth kinetics of calcite in NaCl solutions. Expanding on Barenblatt's cohesive crack model, SFFT introduces an effective stress intensity at the crack tip that encompasses all the relevant intermolecular forces across the crack in addition to the external far‐field stresses. The nonlinear system of equations portraying the crack opening profile, the solute distribution in a propagating crack, and the crack growth velocity are numerically solved via an implicit scheme. After carefully calibrating the model for calcite‐water systems, the SFFT is used to predict the SCG response of calcite at different NaCl concentrations, based on various hypotheses. These predictions are then compared to existing SCG data from the literature. We demonstrate that the experimentally observed variation of SCG rate with NaCl concentration cannot be explained solely by DLVO forces (electrostatic and Van der Waals interactions). This can be remediated by introducing an exponentially decaying hydration force with a nonlinear, nonmonotonic dependence on NaCl concentration. Furthermore, we demonstrate that accounting for both diffusive and advective transport of ions is important in explaining the absence of a stage‐II SCG response for calcite in electrolyte solutions. Plain Language Summary Subcritical crack growth (SCG) refers to the slow propagation of cracks in materials under a stress below the threshold for catastrophic failure. SCG is a key process in many geological events, for example, delayed earth ruptures and rock weathering. New initiatives such as underground CO 2 and H 2 storage in carbonate reservoirs further call for better understanding of SCG in carbonate minerals subjected to varying fluid chemistry. This study examines the SCG of calcite, a key mineral found in carbonate rocks, intergranular cement in sandstones, and filling material in mineral veins and faults, determining their deformation and strength. A mathematical model is developed to describe how the crack opens and propagates, how solutes (like salts) distribute within the crack, and how the crack surfaces interact with each other. We used the model to predict calcite SCG in water at different salt concentrations and compared it with experimental data. Our results revealed that the hydration force is the dominating factor in determining the complex, non‐linear dependency of SCG on salinity. We also found that both the movement of ions by diffusion and by bulk water flow are crucial for explaining the SCG rates, especially when the cracks grow quickly. Key Points Surface Force‐Based Fracture Theory predicts the complex subcritical crack growth patterns of calcite crystals immersed in NaCl solutions Results highlight the dominant role of hydration forces in altering the fracture behavior of calcite compared to VdW and electric double‐layer forces Advective solute transport explains the absence of stages‐II and ‐III subcritical crack growth responses in solid‐liquid systems

DLVO↗
NASA NTRSDate not supplied

Seeding Method for Velocimetry and Visualization of Supersonic Retropropulsion Nozzle Plumes

In the current work, the “Venturi seeder” method for nozzle plumes is improved and studied to determine suitability for particle image velocimetry. This seeding method involves independently pressurizing the primary flow path and a separate seed liquid reservoir, connecting them at the throat of a Venturi contraction upstream of the nozzle. The pressure differential at the throat causes the liquid to enter the nozzle channel where it is atomized into particles by strong shearing forces. This new seeding system was characterized by conducting a series of diagnostics using different exit nozzle pressure ratios and seed reservoir pressures. Shadowgraph imaging confirmed the nozzle plumes to be underexpanded jets. A particle sizing device determined that generated DEHS oil particles had median aerodynamic diameters of 0.67, 0.69, and 0.73 μm for seed liquid reservoir pressure supplies of 0.67, 0.60, and 0.93 MPa and primary flow path pressures of 0.67, 0.76, and 0.93 MPa measured downstream of the Venturi throat, respectively. These diameters were within the threshold for acceptable response time in typical gas flows (≤ 1 μm). PIV experiments were conducted on the nozzle plume for all presented cases; mean axial and transverse velocities appeared as expected for the underexpanded jet structure, including the Mach disk and re-acceleration regions. Additional sizing analysis based on the particle response times across the normal shock again showed particle diameters to be well less than 1 μm for all cases. The results suggest that this method is an inexpensive and relatively simple solution to the problem of seeding nozzle plumes. The method will be used in testing of supersonic retropropulsion models in the Unitary Plan Wind Tunnel at The NASA Langley Research Center in 2022.

particle image velocimetry↗
DOE OSTI2026

Optimizing district energy systems by integrating Borehole Thermal Energy Storage Using a Mixed-Integer Linear Programming g-function framework with a Multi-Timescale Rolling Horizon method

Shallow geothermal has gained increasing attention in recent years; however, a reliable framework for its accurate incorporation into large-scale energy system optimization remains lacking. This study proposes a Mixed-Integer Linear Programming (MILP) framework combined with the g-function approach to integrate Borehole Thermal Energy Storage (BTES) technology into energy system optimization. Validation against a Modelica-based reservoir network simulation demonstrates that the proposed framework effectively captures the ground thermal response under varying energy loads and accurately estimates the borefield energy supply. To enhance scalability, a Rolling Horizon with Multi-Timescale (RH-MTS) method is further introduced, reducing computational time by 73 % for the 1-year optimization model with only minor loss of optimality. The framework is demonstrated through the case study of the UC Berkeley campus. Results indicate that BTES is a cost-effective and low-carbon solution: two borefields comprising 382 boreholes can meet 8.0 % and 6.6 % of the total campus heating and cooling demand, respectively, at an average energy rate of 0.70–0.77 USD/kWh and carbon intensity of 0.54 kg-CO2/kWh. Short-term analysis reveals a 35%–65% decline in BTES energy flow after 3–6 months of continuous heating/cooling operation, while long-term simulation shows that annual energy production of BTES can vary by up to 12.0 % after four years before stabilizing. Overall, this study develops a novel optimization framework that couples physics-based g-function method with MILP optimization framework, thereby advancing methodological development for shallow-geothermal integration and providing actionable guidance for BTES deployment in district-energy systems.

Yang, Jiahui↗
DOE OSTI2023

Using Data-Driven Prediction of Downstream 1D River Flow to Overcome the Challenges of Hydrologic River Modeling

Methods for downstream river flow prediction can be categorized into physics-based and empirical approaches. Although based on well-studied physical relationships, physics-based models rely on numerous hydrologic variables characteristic of the specific river system that can be costly to acquire. Moreover, simulation is often computationally intensive. Conversely, empirical models require less information about the system being modeled and can capture a system’s interactions based on a smaller set of observed data. This article introduces two empirical methods to predict downstream hydraulic variables based on observed stream data: a linear programming (LP) model, and a convolutional neural network (CNN). We apply both empirical models within the Colorado River system to a site located on the Green River, downstream of the Yampa River confluence and Flaming Gorge Dam, and compare it to the physics-based model Streamflow Synthesis and Reservoir Regulation (SSARR) currently used by federal agencies. Results show that both proposed models significantly outperform the SSARR model. Moreover, the CNN model outperforms the LP model for hourly predictions whereas both perform similarly for daily predictions. Although less accurate than the CNN model at finer temporal resolution, the LP model is ideal for linear water scheduling tools.

13 HYDRO ENERGY↗
DOE OSTI2021

Surfactant-Aided Low-Salinity Waterflooding for Low-Temperature Carbonate Reservoirs

Carbonate reservoirs tend to be oil-wet/mixed-wet and heterogeneous because of mineralogy and diagenesis. The objective of this study is to improve oil recovery in low-temperature dolomite reservoirs using low-salinity and surfactant-aided spontaneous imbibition. The low-salinity brine composition was optimized using ζ-potential measurements, contact-angle (CA) experiments, and a novel wettability-alteration measure. Significant wettability alteration was observed on dolomite rocks at a salinity of 2,500 ppm. We evaluated 37 surfactants by performing CA, interfacial-tension (IFT), and spontaneous-imbibition experiments. Here, three (quaternary ammonium) cationic and one (sulfonate) anionic surfactants showed significant wettability alteration and produced 43–63% of original oil in place (OOIP) by spontaneous imbibition. At a low temperature (35°C), oil recovery by low-salinity effect is small compared with that by wettability-altering surfactants. Coreflood tests were performed with a selected low-salinity cationic surfactant solution. A novel coreflood was proposed that modeled heterogeneity and dynamic imbibition into low-permeability regions. The results of the “heterogeneous” coreflood were consistent with that of spontaneous-imbibition tests. These experiments demonstrated that a combination of low-salinity brine and surfactants can make originally oil-wet dolomite rocks more water-wet and improve oil recovery from regions bypassed by waterflood at a low temperature of 35°C.

02 PETROLEUM↗
DOE OSTI2023

Integration of seismic-pressure-petrophysics inversion of continuous active-seismic monitoring data for monitoring and quantifying CO 2 plume (Final Report)

The overall objective of this project is to develop and validate an integrated package of joint seismic-pressure-petrophysics inversion (jSPPI) of continuous active-source seismic monitoring dataset capable of providing real-time monitoring of CO 2 plume during geologic carbon sequestration (GCS). The three specific developments include: (a) the methodologies for fast seismic full waveform inversion of continuous active source seismic monitoring, (CASSM) datasets for simultaneously estimating velocity and attenuation, and with data assimilation; (b) joint Bayesian petrophysical inversion of seismic models and pressure data for providing and updating CO 2 saturation models; (c) the methods using multiple datasets including (Crainfield and Frio-II borehole) synthetic, laboratory, and field CASSM datasets. The outcomes of jSPPI include (a) a workflow for processing CASSM data, (b) Bayesian inversion algorithms using CASSM data and pressure response data, and (c) integration with data assimilation algorithms for continuously updating site-specific models used for prediction and reservoir management. The validation of joint FWI will be conducted using synthetic models based on the Cranfield and Frio experiments as well as field CASSM datasets collected as part of the Frio-II pilot injection. To quantify and map the mass and distribution of CO 2 (saturation), we will jointly invert velocity and attenuation measurements from the FWI with a Bayesian approach using a rock physics model for attenuation (e.g., White’s attenuation model with two selected patch sizes (White, 1976; Dutta and Seriff, 1979)). The Bayesian inversion will be applied to each time step in the CASSM survey in an updating scheme, which integrates with an ensemble of reservoir simulations at each step. A more complete experimental validation dataset will be collected as part of a mesoscale (2-3 m) gas-CO 2 injection experiment utilizing a higher frequency version of the CASSM system developed for laboratory studies; the integrated inversion will be demonstrated using this dataset which will provide both a dense geometry as well as more precise secondary confirmation measurements (e.g. saturation) typically not available in the field. The resulting real-time map of CO 2 saturation is able to provide a deeper scientific understanding of the complex, time-varying dynamics of subsurface fluid flow migration path as well as the rapid detection of CO 2 leakage hazards.

25 ENERGY STORAGE↗
DOE OSTI2022

Development of lean, efficient, and fast physics-framed deep-learning-based proxy models for subsurface carbon storage

In this work, we present deep-learning-based surrogate models for CCUS developed with four different algorithms and a physics-framed two-phase flow problem involving displacement of water by CO 2 . The deep-learning models were trained using 3D datasets describing the pressure plume, CO 2 saturation plume, and water extraction rate generated by numerical simulation. The hyperparameters defining the architecture of the neural networks were optimized to determine the slimmest network size and training parameters that give the most efficient performance at the least training cost. To develop a robust model that closely mimics the governing physical laws, the discretized form of the two-phase fluid transport equation was used to formulate the supervised deep-learning task. The algorithms investigated in this study predicted the data to above 95% accuracy, with the multi-layer perceptron model demonstrating the best performance by balancing training speed, prediction time, and prediction accuracy with lean network capacity. Furthermore, the surrogate models simultaneously predict reservoir pressure and CO 2 saturation in every grid block, including the surface well extraction rate and bottomhole pressure, at all simulation times for a given static model realization in just a few seconds on a standard desktop computer. A key outcome of this study is that limits can be placed on network design parameters to avoid over designing neural networks, with associated efficiencies in training and prediction times. This is very useful because large volumes of data may be generated in CCUS projects and over-design of neural network architectures imposes penalties that are antithetical to the goal of near-real time forecasting.

58 GEOSCIENCES↗
DOE OSTI2021

Thermal Stimulation and Injectivity Testing at Raft River, ID EGS Site

The Raft River geothermal field is the site of an innovative Department of Energy Enhanced Geothermal System (EGS) project to determine the viability of using combined thermal and hydraulic stimulation techniques to improve energy production. Well RRG-9 is currently undergoing a stimulation program using injectate from the US Geothermal Raft River Power Plant and cold water from a cooling tower make-up water well. The stimulation began on 13 June 2013 with injection from the power plant at a temperature of about 39 °C and a pressure of 275 psig. Next, two positive displacement plunger type pumps were used to increase the injection pressure and flow rate for about one month. The highest rate achieved was 258 gpm at a pressure of 741 psig. During this time, fluid from the cooler water well was injected for about 2 weeks at various pressures. Then, the pumps were removed and plant injection resumed on 25 September. Plant injection will continue until the spring of 2014, when a high pressure hydraulic stimulation will be conducted. A series of seismic monitoring stations deployed around the well are providing data on seismic events occurring at the site. Over the past year, 51 microseismic events have been recorded, all less than Magnitude 1. During injection, several diagnostic tests were conducted to gain a better understanding of the well and reservoir. A step-rate test was performed on 22 August to measure the in-situ stress and aid in modeling in-situ fractures. A tracer was injected into the well on 9 September. No tracer was detected in adjacent production wells after several months. A second borehole televiewer survey was conducted for comparison to pre-stimulation images. Here, a third borehole televiewer survey is planned after the high pressure stimulation. Injection test data is evaluated in real time. A modified Hall plot analysis indicates the effective permeability is increasing. The injectivity index supports the results of the modified Hall plot analysis. As the thermal stimulation has continued, the injectivity index has consistently followed an upward trend from 0.1 gpm/psi to 0.53 gpm/psi.

enhanced geothermal systems↗
NASA NTRS2024

Influence of SMAP Soil Moisture Retrieval Assimilation on Runoff Estimation Across South Asia

This study was designed to characterize and quantify the influence of surface soil moisture assimilation on estimated runoff (surface flow and baseflow) and hydraulically-routed streamflow across three large river basins in South Asia that are at risk of impending water stress. Soil Moisture Active Passive (SMAP) surface soil moisture retrievals were assimilated into the Noah-MP land surface model. The gridded runoff was hydraulically routed to obtain volumetric streamflow values for a river network using a runoff routing module (Hydrological Modeling and Analysis Platform, HyMAP). The open loop (OL, model-only) and data assimilated (DA, includes soil moisture retrieval assimilation) Noah-MP runoff estimates highlighted the improvements in estimated total runoff across irrigated areas. Soil moisture assimilation impacted baseflow more relative to surface runoff. The HyMAP-based OL and DA streamflow generally underestimated the streamflow at upstream stations and overestimated the streamflow at downstream stations within the Indus basin due to missing physics related to reservoir operations. For stations located in the Ganges–Brahmaputra basins, the OL and DA estimation performance varied. The OL and DA results showed that the assimilation of soil moisture retrievals improves gridded runoff and volumetric streamflow across irrigated areas. Considerable relative change in streamflow (>70% increase in magnitude relative to OL) is noted after assimilation across the highly irrigated lower Indus basin and high precipitation regions in Bangladesh. Improving the modeling system’s representativeness of ground conditions via inclusion of water management information could improve large-scale streamflow modeling across South Asia.

soil moisture↗
DOE OSTI2019

A visco-plastic framework for interface processes in sedimentary reservoir rocks at HPHT conditions

As energy operations face the challenge of reservoirs at ever-increasing depths, modelling the response of reservoir rocks at high pressure and high temperature (HPHT) conditions is a crucial step for successfully unlocking new resources. At these conditions, rocks can experience thermal and pressure sensitivity, as well as admit internal phase changes at the pore-solid interfaces that determine their macroscopic response to external loading. It is therefore expected that constitutive laws of Geomechanics should be enriched to accommodate such effects. In this work, a multi-physics constitutive theory for sedimentary rocks is proposed within the general framework of viscoplasticity. The viscosity of the material is assumed to be a function of the temperature, pore-pressure and energy required to alter the inter-granular interfaces. This energy is expressed through the chemical potentials of the phases involved during processes like chemical dissolution/precipitation of the interfaces or mechanical debonding. The resulting flow law and corresponding stress equilibrium are coupled to the energy and mass conservation laws, constituting a closed system of equations, which is solved using the Finite Element simulator REDBACK. A series of numerical tests for performance and calibration is then successfully performed against different types of reservoir rocks, confining pressures and temperatures (from room temperature to over 800K). Here, we show that the mechanical response of sedimentary porous rocks at strains usually achieved in laboratory testing can be explained by accounting for the interface processes taking place at the cementitious material bonding the grains, a process that can also determine the brittle-to-ductile transition of sedimentary rocks.

58 GEOSCIENCES↗
DOE OSTI2021

Energy-Flow-Environment Linkage Map

Understanding how flexibility in environmental requirements can facilitate co-optimization of hydropower production outcomes and environmental outcomes is critical for future grid decision-making and operations as renewable energy resources increase. The environmental and power system outcomes connectivity linkage maps presented here provide a framework for conceptually and quantitatively linking power system outcomes to environmental outcomes through hydropower flow decisions. The Executive Summary Map serves as a starting for exploring the links between hydropower system performance outcomes and environmental outcomes. The centralized topic is “Flow from Hydropower System,” and connects the hydropower operations through “Flow through turbines” and “Non-turbine flows”, and to environmental outcomes through Reservoir elevation” and “Flow downstream of the hydropower system”. To the left of these central topics, “Hydro-mechanical operations” are linked through “Hydro-electrical operations” to “Hydropower performance outcomes” (“Reliability”, “Resilience”, “Revenue”, “Emissions”). On the right, environmental outcomes are grouped together by their physical location: “Upstream Outcomes” (“Upstream geomorphology”, “Upstream recreation”, “Upstream habitat”, “Upstream biota and biodiversity”, “Upstream water quality and greenhouse gas”), Outcomes relevant to both “Upstream/downstream or dam interface” (“Navigation”, “Dam safety and maintenance", “Human health”, “Water supply”, “Flood control”, ”Fish passage”), and “Downstream outcomes“ (e.g., “Downstream geomorphology”, “Downstream recreations”, “Downstream habitat”, “Downstream biota and biodiversity”, “Downstream water quality and greenhouse gas”). Each of these subtopics (e.g., “Hydro-mechanical operations”, “Hydro-electrical operations”, “Upstream geomorphology”, “Upstream recreation”) is further explored through their corresponding submaps. The “Read Me” file provides more detail information on map navigation. The “Models and tools database” file provides detailed information of models and tools presented in the maps.

13 HYDRO ENERGY↗
DOE OSTI2020

Transient flow modeling in fractured media using graphs

In this work, we describe a method to simulate transient fluid flows in fractured media using an approach based on graph theory. Our approach builds on past work where the graph-based approach was successfully used to simulate steady-state fluid flows in fractured media. We find a mean computational speedup of the order of 1400 from an ensemble of a 100 discrete fracture networks in contrast to the O(10 4 ) speedup that was obtained for steady-state flows earlier. However, the transient flows considered here involve an additional degree of complexity that was not present in the steady-state flows considered previously with a graph-based approach, that of time marching and solution of the flow equations within a time-stepping scheme. We verify our method with an analytical test case and demonstrate its use on a practical problem related to fluid flows in hydraulically fractured reservoirs. By enabling the study of transient flows, we create an opportunity for a wide set of possibilities where a steady-state approximation is not sufficient, such as the example motivated by hydraulic fracturing that we present here. This work validates the concept that graphs are able to reliably capture the topological properties of the fracture network and serve as effective surrogates in an uncertainty-quantification framework.

58 GEOSCIENCES↗
DOE OSTI2020

Reactive chemical transport simulations of geologic carbon sequestration: Methods and applications

Chemical reaction simulations are considerably used to quantitatively assess the long-term geologic carbon sequestration (GCS), such as CO 2 sequestration capacity estimations, leakage pathway analyses, enhanced oil recovery (EOR) efficiency studies, and risk assessments of sealing formations (caprocks), wellbores, and overlying underground water resources. All these require a deep understanding of the CO 2 - associated chemical reactions. To ensure long-term, safe CO 2 sequestration in the intended formations, modeling is the only way to plausibly assess the CO 2 flow, reaction, and transport over thousands of years. This review summarizes the multiple methodologies for describing homogeneous and heterogeneous chemical reaction patterns and multiscale application examples, the recent progress and current status of chemical reaction simulations for GCS, and the impact of such simulations on geological CO2 sequestration performance. Technical gaps and future challenges are also discussed for further study. The trends and challenges of such studies include: (1) the combination of coupled chemical, mechanical, and transport processes with calibrated experiments and associated uncertainty/risk assessments; (2) enhancement of the ability to simulate detailed geophysical and geochemical equations to mimic in situ conditions; and (3) characterization of multiscale subsurface systems with detailed conceptual models and assignment of suitable boundary conditions for field-scale sequestration fields. One major gap remaining is the current lack of accurate (and scale-justified) kinetic and equilibrium chemical reaction parameters under reservoir conditions. Advanced models that couple chemical, mechanical, and transport processes with scale-justified parameters, from lab to field-scale experiments, are required for quantitative assessments of sequestration capacity and the long-term safety of GCS projects.

58 GEOSCIENCES↗
DOE OSTI2021

2D-to-3D image translation of complex nanoporous volumes using generative networks

Image-based characterization offers a powerful approach to studying geological porous media at the nanoscale and images are critical to understanding reactive transport mechanisms in reservoirs relevant to energy and sustainability technologies such as carbon sequestration, subsurface hydrogen storage, and natural gas recovery. Nanoimaging presents a trade off, however, between higher-contrast sample-destructive and lower-contrast sample-preserving imaging modalities. Furthermore, high-contrast imaging modalities often acquire only 2D images, while 3D volumes are needed to characterize fully a source rock sample. In this work, we present deep learning image translation models to predict high-contrast focused ion beam-scanning electron microscopy (FIB-SEM) image volumes from transmission X-ray microscopy (TXM) images when only 2D paired training data is available. We introduce a regularization method for improving 3D volume generation from 2D-to-2D deep learning image models and apply this approach to translate 3D TXM volumes to FIB-SEM fidelity. We then segment a predicted FIB-SEM volume into a flow simulation domain and calculate the sample apparent permeability using a lattice Boltzmann method (LBM) technique. Results show that our image translation approach produces simulation domains suitable for flow visualization and allows for accurate characterization of petrophysical properties from non-destructive imaging data.

58 GEOSCIENCES↗
DOE OSTI2025

Hero Carbonsafe Phase 2 Project in the Columbia River Basalt Group: Technical Program Overview

The Hermiston, Oregon Basalt CarbonSAFE Phase II project (HERO CarbonSAFE) seeks to accelerate the deployment of commercial carbon dioxide (CO2) storage projects in basaltic rocks. Hermiston is located near the center of the Columbia River Basalt Group (CRBG), which is one of the largest basalt flows in the US. Basalt CO2 storage has potential advantages to conventional saline storage reservoirs including 1. The potential for rapid mineralization of CO2, 2. associated decreases in pressure and CO2 migration risks, 3. reduced long-term monitoring requirements with respect to plume tracking, 4. widespread geographic distribution and, 5. large storage potential due to thickness, porosity, and CO2 interactions with basalt. For locations such as the Pacific Northwest (PNW), Hawaii, Iceland, India and Japan, whose localities are isolated from large sedimentary basins offering conventional saline storage options, basalt may offer the only feasible option for local CO2 storage. However, mineralization/basalt storage still has many uncertainties, as there are limited field-scale assessments of CO2 storage in basalt. There are significant uncertainties hindering the effective implementation of carbon capture utilization and storage (CCUS) in basalt. These include the lack of proven storage capacities, challenges in methodologies for modeling the area of review in igneous formations, limited understanding of mineralization kinetics and timing, and uncertainties in injectivity. Additionally, the domestic availability of specialized services and drilling expertise is constrained, and existing CCUS permitting and regulatory frameworks, originally developed for conventional saline reservoirs, may not adequately address the unique requirements of basalt systems. HERO CarbonSAFE is designed to address major research gaps and uncertainties associated with basalt storage. Specifically, the project will assess the feasibility of CO2 injection in the deep layered basalts of the CRBG, long-term storage (mineralization), practical approaches for large-scale implementation (50+ million metric tons of CO2 over 30 years), lithology-specific risks, and the technoeconomic potential for CO2 storage in basalts.

58 GEOSCIENCES↗