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All sources NASA NTRS DOE OSTI

DOE OSTI2023

Bridging adsorption behavior of confined CH 4 -CO 2 binary mixtures across scales

An accurate understanding of the competitive adsorption of CH 4 -CO 2 binary mixtures in nano-confined systems is critical for engineering CO 2 storage in shale gas reservoirs. Due to difficulties in making reliable experimental observations in nano-scale, atomistic simulations (ASs), such as the Grand Canonical Monte Carlo (GCMC) method, provide a viable approach to studying the adsorption behavior of confined fluids. ASs are, however, limited in the size of the compositional domain due to the high computational cost. This work proposes a framework that combines AS and the lattice Boltzmann (LB) method to bridge the physics of confined fluids across scales. The Peng–Robinson equation of state (PR-EoS) produces fugacity coefficients, which serve as input for conducting multi-component GCMC simulations. These GCMC simulations explore the competitive adsorption behavior of CH 4 -CO 2 in nano-slits at various composition, pressure, and channel-width conditions. Both components generate adsorption layers with high densities near the walls with CO 2 preferentially adsorbing compared to CH 4 on the organic walls of carbon sheets. At the mesoscale, a pseudopotential model represents the intermolecular forces in multi-component, multiple-relaxation-time LB simulations. The LB simulations are in good agreement with the GCMC results, allowing us to obtain values for tunable LB parameters. We then extend the use of LB to simulate adsorption behavior in complex networks with nano-sized channels. The phase behavior and fluid properties in the complex geometries of nano-channels differ from nano-slits and bulk systems. Furthermore, the bridging of physics from GCMC (microscale) to LB (mesoscale) via the macroscale PR-EoS connects the adsorption behavior of binary systems across scales.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Deep-learning-based workflow for boundary and small target segmentation in digital rock images using UNet++ and IK-EBM

We report three-dimensional (3D) X-ray micro-computed tomography (μCT) has been widely used in petroleum engineering because it can provide detailed pore structural information for a reservoir rock, which can be imported into a pore-scale numerical model to simulate the transport and distribution of multiple fluids in the pore space. The partial volume blurring (PVB) problem is a major challenge in segmenting raw μCT images of rock samples, which impacts boundaries and small targets near the resolution limit. We developed a deep-learning (DL)-based workflow for accurate and fast partial volume segmentation. The DL model's performance depends primarily on the training data quality and model architecture. This study employed the entropy-based-masking indicator kriging (IK-EBM) to segment 3D Berea sandstone images as training datasets. The comparison between IK-EBM and manual segmentation using a 3D synthetic sphere pack, which had a known ground truth, showed that IK-EBM had higher accuracy on partial volume segmentation. We then trained and tested the UNet++ model, a state-of-the-art supervised encoder-decoder model, for binary (i.e., void and solid) and four-class segmentation. We compared the UNet++ with the commonly used U-Net and wide U-Net models and showed that the UNet++ had the best performance in terms of pixel-wise and physics-based evaluation metrics. Specifically, boundary-scaled accuracy demonstrated that the UNet++ architecture outperformed the regular U-Net architecture in the segmentation of pixels near boundaries and small targets, which were subjected to the PVB effect. Feature map visualization illustrated that the UNet++ bridged the semantic gaps between the feature maps extracted at different depths of the network, thereby enabling faster convergence and more accurate extraction of fine-scale features. The developed workflow significantly enhances the performance of supervised encoder-decoder models in partial volume segmentation, which has extensive applications in fundamental studies of subsurface energy, water, and environmental systems.

02 PETROLEUM↗

DOE OSTI2021

A Mixed Fracture-Matrix Model for Evaluating Well Orientation and Completion Options for the Utah FORGE Site

58 GEOSCIENCES↗

DOE OSTI2021

"Hybrid fracture/matrix modeling for well completion options evaluation"

Orientation and completion for well pairs that have been subjected to multi-zonal stimulation play a critical role in the long-term performance of an Enhanced Geothermal Reservoir. Here we present the development of a methodology to rapidly and efficiently numerically simulate mixed fracture-matrix flow systems for evaluation of well design and completion options. The methodology is based on a loose coupling framework, allowing the fracture and matrix systems to be meshed separately. The fracture system includes the integration of fracture growth and aperture data from well stimulation simulations of stochastically generated fracture networks. Automatic mesh refinement is used in the matrix simulation to resolve heat transfer near the fracture network. This simulation framework is used to efficiently determine optimal production and injection well placement using adaptive sampling.

15 GEOTHERMAL ENERGY↗

DOE OSTI2021

A Mixed Fracture-Matrix Model for Evaluating Well Orientation and Completion Options for the Utah FORGE Site

15 GEOTHERMAL ENERGY↗

DOE OSTI2026

Differentiable multiphase flow model for physics-informed machine learning in reservoir pressure management

Accurate subsurface reservoir pressure control is extremely challenging due to geological heterogeneity and multiphase fluid-flow dynamics. Predicting behavior in this setting relies on high-fidelity physics-based simulations that are computationally expensive. Yet, the uncertain, heterogeneous properties that control these flows make it necessary to perform many of these expensive simulations, which is often prohibitive. To address these challenges, we introduce a physics-informed machine learning workflow that couples a fully differentiable multiphase flow simulator, which is implemented in the DPFEHM framework with a convolutional neural network (CNN). The CNN learns to predict fluid extraction rates from heterogeneous permeability fields to enforce pressure limits at critical reservoir locations. By incorporating transient multiphase flow physics into the training process, our method enables more practical and accurate predictions for realistic injection-extraction scenarios compared to previous works. To speed up training, we pretrain the model on single-phase, steady-state simulations and then finetune it on full multiphase scenarios, which dramatically reduces the computational cost. We demonstrate that high-accuracy training can be achieved with fewer than three thousand full-physics multiphase flow simulations – compared to previous estimates requiring up to ten million. This drastic reduction in the number of simulations is achieved by leveraging transfer learning from much less expensive single phase simulations.

25 ENERGY STORAGE↗

DOE OSTI2022

Proxy-Based Bayesian Inversion Of Poroelastic Simulations To Interpret Strain Tensor Data Measured During Well Testing

The long runtimes of 3D poroelastic numerical simulators makes it impractical to interpret deformation datasets using many inversion schemes. Recent advances in instrumentation have made it possible to measure the strain tensor during well testing, but the lack of robust inversion methods is limiting the ability to interpret these data. We have developed an inversion workflow that reduces the number of computations required to complete a Bayesian inversion using DREAMzs. The workflow trains a KNN model using output from the poroelastic simulator, and then uses the KNN model as a proxy for the simulator during inversion. The workflow also includes a strategy for ensuring the results from the proxy model converge to the results from the simulator, ensuring the accuracy of the final results. An idealized example configured to represent a well test in a deep aquifer is used to verify that the workflow correctly identifies parameters and characterizes noise. Field data measured using strainmeters during an injection test at an oil reservoir in Oklahoma are used to evaluate performance with a real dataset. The workflow identified 265 history matching solutions out of 1240 total simulation runs (21% acceptance ratio), and the results are used to characterize posterior parameter distribution and evaluate the prediction uncertainty. This approach makes it feasible to invert strain data measured during well testing and this has the potential to improve the characterization of aquifers and reservoirs.

Roudini, Soheil↗

DOE OSTI2022

Quantifying Northern High Latitude Gross Primary Productivity (GPP) Using Carbonyl Sulfide (OCS)

The northern high latitude (NHL, 40°N to 90°N) is where the second peak region of gross primary productivity (GPP) other than the tropics. The summer NHL GPP is about 80% of the tropical peak, but both regions are still highly uncertain. Carbonyl sulfide (OCS) provides an important proxy for photosynthetic carbon uptake. Here we optimize the OCS plant uptake fluxes across the NHL by fitting atmospheric concentration simulation with the GEOS-CHEM global transport model to the aircraft profiles acquired over Alaska during NASA's Carbon in Arctic Reservoirs Vulnerability Experiment (2012–2015). We use the empirical biome-specific linear relationship between OCS plant uptake flux and GPP to derive the six plant uptake OCS fluxes from different GPP data. Such GPP-based fluxes are used to drive the concentration simulations. We evaluate the simulations against the independent observations at two ground sites of Alaska. The optimized OCS fluxes suggest the NHL plant uptake OCS flux of -247 Gg S year -1 , about 25% stronger than the ensemble mean of the six GPP-based OCS fluxes. GPP-based OCS fluxes systematically underestimate the peak growing season across the NHL, while a subset of models predict early start of season in Alaska, consistent with previous studies of net ecosystem exchange. The OCS optimized GPP of 34 PgC yr -1 for NHL is also about 25% more than the ensembles mean from six GPP data. Further work is needed to fully understand the environmental and biotic drivers and quantify their rate of photosynthetic carbon uptake in Arctic ecosystems.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Above-cloud concentrations of cloud condensation nuclei help to sustain some Arctic low-level clouds

Abstract. Previous studies have found that low-level Arctic clouds often persist for long periods even in the face of very low surface cloud condensation nuclei (CCN) concentrations. Here, we investigate whether these conditions could occur due to continuous entrainment of aerosol particles from the free troposphere (FT). We use an idealized large eddy simulation (LES) modeling framework, where aerosol concentrations are low in the boundary layer (BL) but increased up to 50× in the free troposphere. We find that the tests with higher tropospheric aerosol concentrations simulated clouds, which persisted for longer and maintained higher liquid water paths (LWPs). This is due to direct entrainment of the tropospheric aerosol into the cloud layer, which results in a precipitation suppression from the increase in cloud droplet number and in stronger cloud-top radiative cooling, which causes stronger circulations maintaining the cloud in the absence of surface forcing. Together, these two responses result in a more well-mixed boundary layer with a top that remains in contact with the tropospheric aerosol reservoir and can maintain entrainment of those aerosol particles. The surface aerosol concentrations, however, remained low in all simulations. The free-tropospheric aerosol concentration necessary to maintain the clouds is consistent with concentrations that are frequently seen in observations.

Environmental Sciences & Ecology↗

DOE OSTI2022

Carbon dioxide-enhanced metal release from kerogen

Heavy metals released from kerogen to produced water during oil/gas extraction have caused major environmental concerns. To curtail water usage and production in an operation and to use the same process for carbon sequestration, supercritical CO 2 (scCO 2 ) has been suggested as a fracking fluid or an oil/gas recovery agent. It has been shown previously that injection of scCO 2 into a reservoir may cause several chemical and physical changes to the reservoir properties including pore surface wettability, gas sorption capacity, and transport properties. Using molecular dynamics simulations, we here demonstrate that injection of scCO 2 might lead to desorption of physically adsorbed metals from kerogen structures. This process on one hand may impact the quality of produced water. On the other hand, it may enhance metal recovery if this process is used for in-situ extraction of critical metals from shale or other organic carbon-rich formations such as coal.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Utilizing GEOPHIRES-X Beyond Electricity

The GEOPHIRES tool is a techno-economic simulator for evaluating the thermal performance and cost-competitiveness of geothermal plants for electricity, heating, and/or cooling. The tool combines reservoir, wellbore, and surface plant cost and performance models to estimate overall techno-economic metrics such as net present value or levelized cost of electricity, heating, or cooling. We recently upgraded the tool to an object-oriented Python framework, presented in an accompanying paper. As part of the upgrade, we enhanced the capability to simulate the performance of geothermal plants for heating and cooling, which is the topic of this paper. Specifically, we (1) integrated absorption chillers to investigate the performance of utilizing geothermal heat for cooling, (2) integrated a heat pump module to boost the geothermal temperature and thermal output, (3) integrated a district heating module to estimate heating demand for a district based on local weather data, and simulated heat supply with geothermal energy and peaking boilers, and (4) integrated GEOPHIRES as an engine in the dGeo simulator to perform a geospatial analysis of geothermal district heating feasibility across a large region (e.g., a state or the entire United States) utilizing resource and thermal demand maps. This paper presents background information and case studies for several of these heating and cooling end-use options in GEOPHIRES.

absorption chiller↗

DOE OSTI2021

Simulation of Hurricane Harvey flood event through coupled hydrologic-hydraulic models: Challenges and next steps

Using the 2017 Hurricane Harvey flood event as a test case, this study set up a series of sensitivity analyses to highlight three challenges associated with large-scale flood inundation modeling, including (a) model parameterization, (b) errors in digital elevation models, and (c) effects of reservoir retention. Driven by radar-based hourly rainfall data, a series of hydrologic-hydraulic models including the VIC hydrologic model, RAPID routing model, and Flood2D-GPU hydrodynamic model are set up over Harris County, Texas, to simulate flood inundation and hazards. The results demonstrate the importance of hydrologic parameters in improving flood modeling. For a large flood event such as Hurricane Harvey, the effect of the initial water depths is insignificant. The Manning's n values may increase the peak water depth by ~1%, the flood extents by 65km 2 , and the high danger zone by ~6%. On the contrary, the bathymetry correction factors may reduce the flood extent by ~1.4% and the high-danger zone by ~4%. Reducing the reservoir storage capacity to 1% may increase the flood extent by ~4% and the high-danger zone by ~17%. This study may provide supporting information to guide and prioritize the development of future high-performance computing hydrodynamic large-scale flood simulations.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Potential Formation Damage: An Integrated Reservoir Characterization Study of the Naturally Fractured Carbonate Middle Duperow Formation at the Kevin Dome, Montana

In this study, we integrate geologic and engineering data of a naturally fractured carbonate reservoir at the Kevin Dome, Montana. Well test data are correlated with core description, geochemical and lithology study to determine the flow behavior and communication within the injection test interval and to the surrounding area. Based on a dual-continuum geologic model, numerical brine injection simulations are carried out to validate the interpretation results from our well test analytical models and forecast the probability of CO 2 injection success using current reservoir properties. As a result, our well test analytical models as well as lithology/core description suggest that fluid flow may be mainly restricted to the injection interval and the assumption of radial (horizontal) flow may be appropriate. The well test models also indicate that there is potentially formation damage with a positive skin factor although prior to brine injection well tests, well stimulation through acid treatment was performed. Our numerical simulation results appear to confirm this formation damage by showing additional pressure buildup in the injection data during later test periods. To explain this, acid may have dissolved dolomite then dolomite or calcite may have been formed again further into the matrix/fracture system. Another possible explanation is mechanical clogging of the fractures due to acid dissolving dolomite and dislodging fine grains. Our work also predicts that if no additional well stimulation is performed, the project will have a lower probability of successfully injecting 1 million tons of CO 2 into the Middle Duperow formation over 4 years.

42 ENGINEERING↗

DOE OSTI2020

Self-Assembly of Silica Nanoparticles at Water–Hydrocarbon Interfaces: Insights from In Operando Small-Angle X-ray Scattering Measurements and Molecular Dynamics Simulations

To achieve tunable controls on the interactions of siliceous materials in subsurface environments bearing water and hydrocarbons, it is essential to determine the influence of hydrocarbon–water interfaces on the self-assembly of the silica particles. The hydrophilic silica particle have a tendency to aggregate on the water front of the interface. Self-assembly of silica nanoparticles proceeds via the migration of these nanoparticles to the water–hydrocarbon interface followed by aggregation at the interface. Fractal-like morphologies of assembled silica nanoparticles at water–hydrocarbon interfaces are observed. Rapid assembly of the hydrophilic silica nanoparticles at water–hydrocarbon interfaces corresponds to an overall reduction in the surface tension of water–toluene and water–heptane systems. These studies demonstrate the silica aggregation that is the precursor of silica polymerization, or nucleation and growth is influenced by the presence of hydrocarbons in subsurface geologic environments. These insights were derived from in operando ultrasmall and small-angle X-ray scattering (USAXS/SAXS) measurements, cryo-scanning electron microscopy (Cryo-SEM) imaging, and classical molecular dynamics simulations. As a result, these studies are intended to inform current and future efforts aimed at tuning silica reactivity in subsurface geologic environments to enhance permeability, the design and use of silica-based proppants to enhance fractures, and the development of effective strategies to control silica-based scaling behavior in subsurface reservoirs.

42 ENGINEERING↗

DOE OSTI2020

On the use of adjoints in the inversion of observed quasi-static deformation

An adjoint-based conjugate gradient algorithm provides an efficient means for imaging sources of deformation within the Earth, such as volume stresses associated with fluid flow in aquifers and reservoirs. For time intervals over which the overburden deforms elastically, one can calculate the gradient elements for a single model update using just two numerical simulations. The first is a forward run that is used to compute the residuals associated with the given iteration. The second simulation is to evaluate the application of the adjoint operator to the residuals. In this adjoint calculation, the residual displacements are applied as sources at the measurement locations, driving the deformation in the simulation. The volume stress on the source grid blocks, in response to the residual displacements, provide the gradient components. We apply this technique to satellite-based interferometric synthetic aperture radar (InSAR) line-of-sight displacements that were observed over an oil reservoir in California’s Central Valley. Furthermore, we find that the adjoint-based gradient estimates, requiring 18 CPU seconds, agree with conventional numerical calculations that take over 3700 CPU seconds to compute. Conjugate gradient algorithms utilizing the conventional approach and adjoint-based gradient computations give roughly the same reductions in misfit and similar final estimates of reservoir volume change.

58 GEOSCIENCES↗

DOE OSTI2023

Probabilistic Evaluation of Geomechanical Risks in CO2 Storage: An Exploration of Caprock Integrity Metrics Using a Multilaminate Model

The probabilistic uncertainty assessment of geomechanical risk—specifically, caprock failure—attributable to CO2 injection, as presented in a simplified hypothetical geological model, was the focus of this study. Our approach amalgamates the implementation of a multilaminate model, the creation of a response surface model in conjunction with the Box–Behnken sampling design, the execution of associated numerical modeling experiments, and the utilization of Monte Carlo simulations. Probability distributions to encapsulate the inherent variability (elastic and mechanical properties of the caprock and reservoir) and uncertainty in prediction estimates (vertical displacement, total strain, and F value) were employed. Our findings reveal that the Young modulus of the caprock is a key factor controlling equivalent total strain but is insufficient as a stand-alone indicator of caprock integrity. It is confirmed that the caprock can accommodate significant deformation without failure, if it possesses a low Young’s modulus and high mechanical strength properties, such as the friction angle and uniaxial compressive strength. Similarly, vertical displacement was found to be an unreliable indicator for caprock integrity, as caprock failure can occur across a broad spectrum of vertical displacements, particularly when both the Young modulus and mechanical strength properties have wide ranges. This study introduces the F value as the most dependable indicator for caprock failure, although it is a theoretical attribute (the shortest distance between the Mohr circle and the nearest failure envelope used to measure the sensitivity to failure) and not physically measurable in the field. Deviatoric stress levels were found to vary based on stress regimes, with the maximum levels observed under extensive and compressive stress regimes. In conjunction with the use of the response surface method, this study demonstrates the efficacy of the multilaminate framework and the Mohr–Coulomb constitutive model in providing a simplified, yet effective, probabilistic model of the mechanical behavior of caprock failure, reducing mathematical and computational complexities.

Energy & Fuels↗

DOE OSTI2024

A Novel Workflow for Coupled Simulation of Hydraulic Stimulation with Simultaneous Injection of Proppant

Enhanced Geothermal Systems (EGS) are located in impermeable host rock. Therefore, to achieve promising circulation, hydraulic stimulation is a key factor. Stimulation involves injecting a combination of fluids and proppant particles into existing fractures under high pressure. The effectiveness of stimulation depends on a variety of different factors, including the pressurization scheme and the optimal choice of carrying fluid and proppant particles to be used. For example, high-density particles can settle out of the injected slurry quickly, whereas lighter particles as well as a dense carrying fluid allow for a long transport into the reservoir. A numerical tool is therefore essential to take all these factors into account and to make robust predictions about the performance of the hydraulic stimulation. We present a novel computational application for coupled numerical simulation of hydraulic stimulation with concurrent injection of proppant in a fractured geothermal reservoir. This application is based on the open-source MOOSE framework that has been enriched with a set of equations to account for the circulation of proppant-laden slurries. It accounts for both the fluid and proppant components of the mixture, as well as particle-driven processes that include gravity settling, particle-particle interactions, and strong density and viscosity contrasts. A particular challenge is the mutual coupling with fracture mechanical processes in a propagating fracture using the FE code Defmod. Aperture and fluid pressure on one side and slurry density and viscosity changes on the other side interact and affect each other. Their basic understanding is therefore essential for the success of the stimulation. We compare our application to common numerical benchmark problems for both slurry flow and hydraulic stimulation.

15 GEOTHERMAL ENERGY↗

DOE OSTI2025

Simulation insights into wetting properties of hydrogen-brine-clay for hydrogen geo-storage

Hydrogen geo-storage is attracting substantial interdisciplinary interest as a cost-effective and sustainable option for medium- and long-term storage. Hydrogen can be stored underground in diverse formations, including aquifers, salt caverns, and depleted oil and gas reservoirs. The wetting dynamics of the hydrogen-brine-rock system are critical for assessing both structural and residual storage capacities, and ensuring containment safety. Through molecular dynamics simulations, we explore how varying concentrations of cushion gases (CO 2 or CH 4 ) influence the wetting properties of hydrogen-brine-clay systems under geological conditions (15 MPa and 333 K). We employed models of talc and the hydroxylated basal face of kaolinite (kaoOH) as clay substrates. Our findings reveal that the effect of cushion gases on hydrogen-brine-clay wettability is strongly dependent on the clay-brine interactions. Notably, CO 2 and CH 4 reduce the water wettability of talc in hydrogen-brine-talc systems, while exerting no influence on the wettability of hydrogen-brine-kaoOH systems. Detailed analysis of free energy of cavity formation near clay surfaces, clay-brine interfacial tensions, and the Willard-Chandler surface for gas-brine interfaces elucidate the molecular mechanisms underlying wettability changes. Our simulations identify empirical correlations between wetting properties and the average free energy required to perturb a flat interface when clay-brine interactions are less dominant. Here, our thorough thermodynamic analysis of rock-fluid and fluid-fluid interactions, aligning with key experimental observations, underscores the utility of simulated interfacial properties in refining contact angle measurements and predicting experimentally relevant properties. These insights significantly enhance the assessment of gas geo-storage potential. Prospectively, the approaches and findings obtained from this study could form a basis for more advanced multiscale simulations that consider a range of geological and operational variables, potentially guiding the development and improvement of geo-storage systems in general, with a particular focus on hydrogen storage.

25 ENERGY STORAGE↗