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DOE OSTI2019

An adaptive Kriging surrogate method for efficient uncertainty quantification with an application to geological carbon sequestration modeling

We present that in numerical modeling of geological carbon sequestration (GCS), uncertainty quantification (UQ) is usually needed to evaluate the impact of uncertain model parameters on model predictions caused by limited measurements and incomplete knowledge of the parameters. However, UQ for GCS is computationally expensive due to the large ensemble of complex and lengthy model simulations. In this study, we propose an adaptive Kriging method to build a fast-to-evaluate surrogate of the GCS model to alleviate the heavy computational burden. The surrogate model is efficiently generated using a Taylor expansion-based adaptive experimental design algorithm that combines a distance-based exploration criterion and an exploitation criterion to adaptively search for informative training samples. In addition, we analyze the uncertainty brought by substituting the surrogate for the actual simulation model and explore its influence on UQ results. Lastly, our method is demonstrated in a synthetic GCS model and its performance is evaluated in comparison with the conventional Monte Carlo sampling. Results indicate that our method can greatly improve the computational efficiency in UQ and provide an effective and reliable UQ solution with the consideration of surrogate uncertainty.

58 GEOSCIENCES↗

DOE OSTI2023

Physics-informed machine learning for fault-leakage reduced-order modeling

Geologic carbon storage (GCS) is a promising technology for mitigating CO 2 emissions. The overall success of GCS depends on safe operations that are informed by risk assessment and have proper mitigation plans in place. Performing quantitative probabilistic risk assessment for a GCS site using traditional reservoir simulators can be challenging due to the high computational costs. To overcome this challenge, the US Department of Energy’s National Risk Assessment Partnership (NRAP) project has developed an integrated assessment modeling approach that utilizes computationally efficient reduced-order models (ROM) for simulating various parts of a GCS storage site to quantify uncertainty. Here, in this study, we develop a reduced-order model for fault leakage risk assessment. We use a deep learning approach to build the reduced-order model. We perform a sensitivity analysis and find that the deep learning model yields high accuracy with a much smaller computational cost than full-physics simulation. We also evaluate the performance of the model in scenarios where simulations are not possible to run, providing analysis not previously performed in fault-leakage ROM analyses. Based on a sensitivity analysis of the model, we suggest a simplified conceptual model for fault leakage and site monitoring.

58 GEOSCIENCES↗

DOE OSTI2026

A decade of progress in understanding and managing legacy well integrity for geologic carbon storage

This study reviews a decade of research progress in legacy well integrity and risk management for geologic carbon storage (GCS) to commemorate the 20 th anniversary of the Intergovernmental Panel on Climate Change’s 2005 Special Report on Carbon Capture and Storage. In the past ten years, legacy well research has benefited from global efforts to constrain emissions from abandoned oil and gas wells, a continued focus on well materials performance in the presence of CO 2 -rich fluids, and practical experience gained through GCS implementation. Field measurements of abandoned well emissions show that leakage is not universal or catastrophic but forms a continuum of low-to-moderate fluxes that depend on isolation integrity and environmental attenuation. Materials research has constrained the conditions under which Portland cements exhibit self-sealing and non-sealing behaviors, and has identified the impact of geomechanical properties, non-uniform pathway apertures, multi-phase flow, and impurities in the CO 2 stream, on leakage pathways as important new areas for investigation. GCS projects at brownfield sites have inspired the creation of new workflows that integrate various tools and technologies to manage legacy well leakage risks. GCS implementation has also motivated a push towards scenario-based well modeling that directly informs permit applications. These advances inspire new research questions for the coming decade, particularly around the level of legacy well leakage risk that is environmentally acceptable and tolerable to stakeholders when sequestering millions of tonnes of CO 2 annually.

Carbon capture and storage↗

DOE OSTI2022

A robust deep learning workflow to predict multiphase flow behavior during geological C O 2 sequestration injection and Post-Injection periods

Simulation of multiphase flow in porous media is essential to manage the geologic CO 2 sequestration (GCS) process, and physics-based simulation approaches usually take prohibitively high computational cost due to the nonlinearity of the coupled physics. This paper contributes to the development and evaluation of a deep learning workflow that accurately and efficiently predicts the temporal-spatial evolution of pressure and CO 2 plumes during injection and post-injection periods of GCS operations. Based on a Fourier Neural Operator, the deep learning workflow takes input variables or features including rock properties, well operational controls and time steps, and predicts the state variables of pressure and CO 2 saturation. To further improve the predictive fidelity, separate deep learning models are trained for CO 2 injection and post-injection periods due to the difference in primary driving force of fluid flow and transport during these two phases. We also explore different combinations of features to predict the state variables. We use a realistic example of CO 2 injection and storage in a 3D heterogeneous saline aquifer, and apply the deep learning workflow that is trained from physics-based simulation data and emulate the physics process. Through this numerical experiment, we demonstrate that using two separate deep learning models to distinguish post-injection from injection period generates the most accurate prediction of pressure, and a single deep learning model of the whole GCS process including the cumulative injection volume of CO 2 as a deep learning feature, leads to the most accurate prediction of CO 2 saturation. For the post-injection period, it is key to use cumulative CO 2 injection volume to inform the deep learning models about the total carbon storage when predicting either pressure or saturation. The deep learning workflow not only provides high predictive fidelity across temporal and spatial scales, but also offers a speedup of 250 times compared to full physics reservoir simulation, and thus will be a significant predictive tool for engineers to manage the long-term process of GCS.

58 GEOSCIENCES↗

DOE OSTI2021

Glucocorticoids suppressed osteoblast differentiation by decreasing Sema3A expression via the PIK3/Akt pathway

Highlights: • Dex decreased Sema3A expression by suppressing the PI3K/Akt pathway. • Sema3A reversed Dex-induced suppression of osteoblast differentiation. • Sema3A reversed the Dex-mediated reduced transcriptional activity of β-catenin. Glucocorticoids(GCs) are extensively used to treat inflammatory and autoimmune diseases. Excessive prolonged exposure to glucocorticoids is associated with an increased risk of osteoporosis. The inhibition of osteoblast differentiation by GCs is suggested as a major cause for GCs-induced osteoporosis (GIO). However, the precise mechanism underlying the role of GCs in osteoblasts differentiation is not fully elucidated. Semaphorin 3A (Sema3A), a secreted member of the Semaphorin family, enhances bone formation and promotes fracture healing, which is known to increase osteoblastic differentiation and stimulate osteogenesis in bone metabolism. Here, the present study explored the effect of Sema3A in osteoblast differentiation using dexamethasone (Dex) treatment of bone marrow stromal cells (BMSCs). Dex treatment decreased Sema3A expression in BMSCs in a dose-dependent manner. Moreover, Dex stimulation suppressed the differentiation of osteoblasts by reducing alkaline phosphatase (ALP) activity, osteoblastic marker genes expression and mineralization, but all of these effects were ameliorated by exogenous recombinant Sema3A administration. Furthermore, exogenous Sema3A administration reversed the Dex-mediated decrease in nuclear accumulation of β-catenin and β-catenin activity in BMSCs. Meanwhile, Dex was capable of simultaneously suppressing the phosphorylation of protein kinase B(Akt) and the expression level of Sema3A in BMSCs. These changes were significantly abolished by the PI3K/Akt agonist. These results suggest that Dex inhibits osteoblast differentiation by suppressing Sema3A expression via the PI3K/Akt pathway. These data provide new insights into the molecular mechanisms of Dex-induced osteoblast differentiation inhibition.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2021

Predicting images for the dynamics of stellar clusters ( π-DOC ): a deep learning framework to predict mass, distance, and age of globular clusters

ABSTRACT Dynamical mass estimates of simple systems such as globular clusters (GCs) still suffer from up to a factor of 2 uncertainty. This is primarily due to the oversimplifications of standard dynamical models that often neglect the effects of the long-term evolution of GCs. Here, we introduce a new approach to measure the dynamical properties of GCs, based on the combination of a deep-learning framework and the large amount of data from direct N-body simulations. Our algorithm, π-DOC (Predicting Images for the Dynamics Of stellar Clusters) is composed of two convolutional networks, trained to learn the non-trivial transformation between an observed GC luminosity map and its associated mass distribution, age, and distance. The training set is made of V-band luminosity and mass maps constructed as mock observations from N-body simulations. The tests on π-DOC demonstrate that we can predict the mass distribution with a mean error per pixel of 27 per cent, and the age and distance with an accuracy of 1.5 Gyr and 6 kpc, respectively. In turn, we recover the shape of the mass-to-light profile and its global value with a mean error of 12 per cent, which implies that we efficiently trace mass segregation. A preliminary comparison with observations indicates that our algorithm is able to predict the dynamical properties of GCs within the limits of the training set. These encouraging results demonstrate that our deep-learning framework and its forward modelling approach can offer a rapid and adaptable tool competitive with standard dynamical models.

Chardin, Jonathan↗

DOE OSTI2017

Foamed Cement Interactions with CO 2

36 MATERIALS SCIENCE↗

DOE OSTI2017

Foamed Cement Interactions with CO 2

Geologic carbon storage (GCS) is a potentially viable strategy to reduce greenhouse emissions. Understanding the risks to engineered and geologic structures associated with GCS is an important first step towards developing practices for safe and effective storage. The widespread utilization of foamed cement in wells may mean that carbon dioxide (CO 2 )/brine/foamed cement reactions may occur within these GCS sites. Characterizing the difference in alteration rates as well as the physical and mechanical impact of CO 2 /brine/foamed cement is an important preliminary step to ensuring offshore and onshore GCS is a prudent anthropogenic CO 2 mitigation choice. In a typical oil and gas well, cement is placed in the annulus between the steel casing and formation rock for both zonal isolation and casing support. The cement must have sufficient strength to secure the casing in the hole and withstand the stress of drilling, perforating, and fracturing (e.g. API, 1997, 2010 Worldwide Cementing Practices). As such, measuring the mechanical and properties of cement is an important step in predicting cement behavior under applied downhole stresses (Nelson, 2006). Zonal isolation is the prevention of fluids migrating to different zones outside of the casing and is strongly impacted by the permeability of the wellbore cement (Nelson, 2006). Zonal isolation depends on both the mechanical behavior and permeability (a physical property) of the cement (Mueller and Eid, 2006; Nelson, 2006). Long-term integrity of cement depends on the mechanical properties of the cement sheath, such as Young’s Modulus (Griffith et al., 2004). The cement sheath’s ability to withstand the stresses from changes in pressure and temperature is predominantly determined by the mechanical properties, including Young’s modulus, Poisson’s ratio, and tensile strength. Any geochemical alteration may impact both the mechanical and physical properties of the cement, thus ultimately impacting the structural integrity of the wellbore. In this study, atmospheric foamed cements were generated using a neat cement and three foam qualities (volume of entrained gas in the cement) - 10%, 20%, and 30 % gas volume. The samples were immersed in a 0.25 M NaCl brine followed by the injection of supercritical CO 2 at 28.9 MPa and 50°C. Petrophysical properties were examined for representative samples using computed tomography (CT) and scanning electron microscopy (SEM). CT scanning of representative samples across the range of reacted cements revealed macroscopic changes in structure due to brine/CO 2 /cement interactions. The high foam quality samples resulted in more CO 2 -saturated brine infiltrating radially deeper into the cement and thus were more susceptible to alteration. After 56 days of exposure, the 30% foam quality sample had the most reaction resulting in an alteration depth of 8.35 ± 0.13 mm with a calculated 34.6 ± 0.2% reacted area and 5.76 ± 0.2% reacted pore space area. The neat sample on the other hand, had a reaction depth of 0.31 ± 0.13 mm with a calculated 0.15 ± 0.08% reacted area and 0.57 ± 0.05% reacted pore area. Physical measurements of the exposed samples were consistent with this degree of alteration having 47.02% porosity and the highest permeability of 0.041 mD. These results indicate that the greater surface area provided by the increase of pore space in the higher quality foam coupled with carbonate diffusion reactions enabled greater alteration.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2017

Foamed Cement Interactions with CO 2

20 FOSSIL-FUELED POWER PLANTS↗

DOE OSTI2021

Numerical Simulations of Geologic Storage Reservoir Management to Support Risk Mitigation Evaluation

This report provides a detailed description of a set of numerical simulations that represent reservoir behavior over time in response to different operational decision scenarios for detection of potential leakage and reduction or avoidance of leakage impact at a hypothetical geological carbon storage (GCS) site. These simulations serve as the basis for a series of GCS leakage risk forecasts that are to be developed using the National Risk Assessment Partnership’s Open-Source Integrated Assessment Model (NRAP-Open-IAM), and a demonstration of a simple decision support workflow for evaluation of mitigation strategies based on results of those system model forecasts. This risk assessment and decision support study is forthcoming. Four injection scenarios were considered: a constant rate carbon dioxide (CO 2 ) injection case (base case), a case with CO 2 injection rate adjustment, a case with early termination of injection operations, and a case with brine extraction. CO 2 injection operations were controlled to ensure that the pressure transient remains below the defined manageable reservoir fracture pressure, with consideration shown to hypothetical locations within the modeled spatial domain where the overburden was weaker and lower transient pressure increases were allowable. Additionally, a brine extraction alternative was considered as a reservoir management and risk mitigation option to reduce reservoir pressure, steer the plume away from any hypothetical geohazard such as fault as needed, and enhance storage capacity. Such operational actions contribute to risk management overtime. This study explores the potential utility of reservoir management for risk reduction at GCS sites. This study shows that the injection design may modify the time to CO 2 breakthrough at a legacy well; in particular, these results show that brine extraction can add value for mitigating risk both by delaying leakage and reducing pressure build-up. For the scenario considered, both pressure plots and pressure distributions demonstrate that pressure build-up was decreased by 3% with brine extraction. Additionally, extraction of brine afforded enhancement of CO 2 storage capacity by 5% compared to the base case. These findings suggest that brine extraction has substantial potential to steer the risk-related reservoir effects away from known geohazards (e.g., faults and legacy wells) by conducting pressure transient effects and CO 2 plume movement toward the production well. Injection rate adjustment scenarios considered in this study show potential value for managing both reservoir pressure transients and CO 2 plume behavior. Operational actions for reducing injection and/or early termination of injection (as compared to the base case), however, require careful design; tailoring both the extent and timing of injection rate adjustment over the injection and post-injection operational period must be thoroughly planned to balance maximizing storage and minimizing subsurface environmental risk. The study also gives preliminary consideration to the effectiveness that monitoring strategy may play in providing useful information to inform reservoir management decisions for risk reduction. Two types of monitoring were considered: 1) pressure build-up or pressure transient; and 2) potential leakage detection from a CO 2 mass or plume. Four hypothetical legacy wells, two plugged and two abandoned, were placed in the model domain. Monitoring along these wells was measured over time in individual stacked reservoir formations and shale formations to support risk mitigation decisions, especially operational decisions that assisted in risk reduction. These simulations will serve as the basis for a series of GCS leakage risk forecasts that are to be developed using NRAP’s Open-IAM, and demonstration of a simple decision support workflow for comparative assessment of mitigation alternatives based on results of those system model forecasts. This risk assessment and decision support study is forthcoming.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Offshore Geologic Carbon Storage Data Collection and Data Gaps Analysis

This is a TRS documenting the Offshore Geologic Carbon Storage Data Collection. It describes the Data Collection web application and its creation as well as an accompanying Data Gaps Assessment. We present an interactive data collection and data gaps analysis to aggregate, understand, and disseminate the data that are publicly available to support offshore GCS in the United States. This data collection and data gaps analysis can be leveraged by stakeholders to understand where GCS may be viable offshore, create GCS project analogs, and address challenges to GCS in offshore environments.

58 GEOSCIENCES↗

DOE OSTI2024

New Matrix Framework to Determine Carbon Storage Technical Viability

Carbon storage is an integral component of reducing CO2 emissions. Research over the past two decades has developed workflows for volume assessments and economic project feasibility, providing necessary and useful tools to progress geologic carbon storage (GCS) projects. These workflows and assessments have focused primarily on determining the in-situ storage resource based on geologic and engineering parameters and do not integrate subsurface characterization with surface conditions, social factors, and environmental factors that may pose a benefit or impediment to the implementation of GCS. Furthermore, the data required to assess the technical viability of GCS are myriad and disparate. There is no current methodology that identifies technical viability criteria and systematically informs how to aggregate these factors for spatial assessments. To address this gap, the National Energy Technology Laboratory has developed a Carbon Storage Technical Viability Approach (CS TVA) Matrix that incorporates a wide variety of factors to inform and accelerate screening for GCS site selection in the United States.

Mulhern, Julia↗

DOE OSTI2024

The Integration and Mapping of an Open-Source National Well Resource to Inform Geologic Carbon Storage Site Selection and Risk Prevention: The CO2-Locate Database

Geologic carbon storage (GCS) offers a way to capture and permanently store CO₂ from fossil fuel operations in underground geologic structures, aiding in the transition to a carbon-neutral energy economy. However, CO₂ injection sites can experience gas leakage through existing wells that penetrate storage reservoirs, making knowledge of well locations and characteristics crucial for permitting, infrastructure reusability, and risk assessment in GCS. Currently, public wellbore data from state, federal, and tribal entities are inconsistent and fragmented, with gaps and redundancies. To address this, the National Energy Technology Laboratory (NETL) developed CO2-Locate, an open-source, geospatial database and online application. CO2-Locate integrates over 50 data sources from federal, state, and tribal entities, creating a standardized national well database. Funded by the Bipartisan Infrastructure Law, the database is publicly available through the Energy Data eXchange (EDX) and viewable via the CO2-Locate web mapping application. This tool allows users to query, filter, and visualize well data to support GCS planning, permitting, and risk assessments. This presentation covers the methods used to create CO2-Locate, including data acquisition, processing, attribute mapping, and integration, much of which is automated for future updates. The web mapping application and its role in GCS site selection will also be discussed.

Tetteh, Daniel A.↗

DOE OSTI2025

Milky way bulge extra-tidal star survey: MWBest

The Milky Way Bulge Extra-Tidal Star Survey (MWBest) is a spectroscopic initiative aimed at identifying stars that have been tidally stripped from globular clusters in the inner regions of the Galaxy. As part of the Visiting Faculty Program at Fermilab, we have developed theoretical tools and simulations to compare against observed signatures of extra-tidal stars surrounding both bulge globular clusters (GCs) and low-luminosity clusters. By combining the spatial distributions of simulated stripped stars with new MWBest observations, we are gaining insight into the gravitational potential of the inner Galaxy and the role that bulge GCs may play in enriching the surrounding field star population. We further explore the hypothesis that some low-luminosity bulge GCs may in fact be dense clumps of debris, stripped from more massive GCs as they migrate inward due to dynamical friction.

Kunder, Andrea [Unlisted, US, WA]↗

DOE OSTI2022

Geologic Carbon Storage of Anthropogenic CO2 under the Colorado Plateau in Emery County, Utah

Geologic Carbon Storage (GCS) is a promising technology for storing large volumes of anthropogenic CO2 effectively and permanently. Numerical simulations are an integral part of site selection and characterization for any potential GCS site. As part of the DOE-funded CarbonSAFE Rocky Mountains Phase I project, a regional GCS analysis was undertaken to understand the efficacy of storing CO2 emissions from the power generation and heavy industry in central Utah’s favorable geology. In this study, the injection of CO2 for geologic storage was simulated in the Navajo Sandstone Formation in Emery County, Utah. Carbon dioxide was sourced from regional power generation stations and heavy industries throughout Utah, with an emphasis on emissions reduction at the Hunter Power Plant near Castle Dale, Utah. A simulation grid was extracted from the project’s geological model encompassing an area around Price, Huntington, and Castle Dale in central Utah. The Navajo Sandstone Member of the Glen Canyon Group was the target of CO2 injection with the overlying Carmel formation providing the primary seal. A suite of simulations was performed assessing the viability of this area for permanent CO2 storage. Results indicate that the area can not only store 46 million metric tons of anthropogenic CO2, meeting the project goals, but this area has the capacity to securely store at least 1.3 billion tons of CO2, suggesting the injection site and surrounding geology are suitable locations for commercial-scale GCS.

58 GEOSCIENCES↗

DOE OSTI2016

( Hubble Space Telescope Hubble Space Telescope) proper motion (HSTPROMO) catalogs of galactic globular clusters. IV. Kinematic profiles and average masses of blue straggler stars

We make use of the Hubble Space Telescope proper-motion catalogs derived by Bellini et al. to produce the first radial velocity dispersion profiles σ(R) for blue straggler stars (BSSs) in Galactic globular clusters (GCs), as well as the first dynamical estimates for the average mass of the entire BSS population. We show that BSSs typically have lower velocity dispersions than stars with mass equal to the main-sequence turnoff mass, as one would expect for a more massive population of stars. Since GCs are expected to experience some degree of energy equipartition, we use the relation σ∝M{sup −η}, where η is related to the degree of energy equipartition, along with our velocity dispersion profiles to estimate BSS masses. We estimate η as a function of cluster relaxation from recent Monte Carlo cluster simulations by Bianchini et al. and then derive an average mass ratio M{sub BSS}/M{sub MSTO}=1.50±0.14 and an average mass M{sub BSS}=1.22±0.12 M {sub ⊙} from 598 BSSs across 19 GCs. The final error bars include any systematic errors that are random between different clusters, but not any potential biases inherent to our methodology. Our results are in good agreement with the average mass of M{sub BSS}=1.22±0.06 M {sub ⊙} for the 35 BSSs in Galactic GCs in the literature with properties that have allowed individual mass determination.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2016

MULTIPLE POPULATIONS IN THE OLD AND MASSIVE SMALL MAGELLANIC CLOUD GLOBULAR CLUSTER NGC 121

We used a combination of optical and near-UV Hubble Space Telescope photometry and FLAMES/ESO-VLT high-resolution spectroscopy to characterize the stellar content of the old and massive globular cluster (GC) NGC 121 in the Small Magellanic Cloud (SMC). We report on the detection of multiple stellar populations, the first case in the SMC stellar cluster system. This result enforces the emerging scenario in which the presence of multiple stellar populations is a distinctive-feature of old and massive GCs regardless of the environment, as far as the light-element distribution is concerned. We find that second-generation (SG) stars are more centrally concentrated than first-generation (FG) ones. More interestingly, at odds with what is typically observed in Galactic GCs, we find that NGC 121 is the only cluster so far to be dominated by FG stars that account for more than 65% of the total cluster mass. In the framework where GCs were born with 90%–95% of FG stars, this observational finding would suggest that either NGC 121 experienced a milder stellar mass-loss with respect to Galactic GCs or it formed a smaller fraction of SG stars.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2016

GLOBULAR CLUSTERS INDICATE THAT ULTRA-DIFFUSE GALAXIES ARE DWARFS

We present an analysis of archival HST /ACS imaging in the F475W ( g {sub 475}), F606W ( V {sub 606}), and F814W ( I {sub 814}) bands of the globular cluster (GC) system of a large (3.4 kpc effective radius) ultra-diffuse galaxy (DF17) believed to be located in the Coma Cluster of galaxies. We detect 11 GCs down to the 5 σ completeness limit of the imaging ( I {sub 814} = 27 mag). Correcting for background and our detection limits yields a total population of GCs in this galaxy of 27 ± 5 and a V -band specific frequency S {sub N} = 28 ± 5. Based on comparisons to the GC systems of local galaxies, we show that both the absolute number and the colors of the GC system of DF17 are consistent with the GC system of a dark-matter-dominated dwarf galaxy with virial mass ∼9.0 × 10{sup 10} M {sub ⊙} and a dark-to-stellar mass ratio M {sub vir}/ M {sub star} ∼ 1000. Based on the stellar mass growth of the Milky Way, we show that DF17 cannot be understood as a failed Milky-Way-like system, but is more similar to quenched Large-Magellanic-Cloud-like systems. We find that the mean color of the GC population, g {sub 475}– I {sub 814} = 0.91 ± 0.05 mag, coincides with the peak of the color distribution of intracluster GCs and is also similar to those of the blue GCs in the outer regions of massive galaxies. We suggest that both the intracluster GC population in Coma and the blue peak in the GC populations of massive galaxies may be fed—at least in part—by the disrupted equivalents of systems such as DF17.

79 ASTRONOMY AND ASTROPHYSICS↗