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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Geospatial Data Platform for All

Spatiotemporal data has evolved in scale due to augmented use in cross-domain applications. Simultaneously, there is substantial growth in the availability of Geographic Information Systems (GIS) data provided by the United States Geological Survey (USGS) along with other federal, state, county, or local agencies through open-data portals and public access APIs. However, data availability does not equate with accessibility. Large-scale analyses and applications require robust, performant data management with co-location of data storage and computing. The insufficiency of data management infrastructure compels researchers to adopt ad hoc project- specific GIS data storage solutions (e.g., copying data to High-Performance computer file systems). As an ad hoc storage strategy does not scale, it hampers cross-domain analyses causing difficulty in data reuse and utilizing existing code bases. Furthermore, GIS data is complex and requires expertise to analyze and manipulate due to its intricate data structures and data-specific projection transformations. Despite the challenges, we recognize that derived GIS data products, e.g., satellite or LIDAR-based images, can be used in downstream applications such as AI by domain, but non-GIS experts. To address the data needs and overcome the challenges, we are working towards a GIS Data Platform focused on efficient data storage, data discovery and access, and an API to enable common workflows. We propose a knowledge-graph (KG) approach for data discovery, whereby datasets are semantically linked to higher- level constructs such as projects and research areas. The semantic data links enable researchers to explore datasets in a top-down approach by specifying relevant and meaningful terms (assists in finding hidden data). An advantage is that the nodes and edges in a knowledge graph create built-in semantic documentation. Deeper spatiotemporal connections between data sources can be encoded via Graph Neural Networks (GNN) (Zhang et al., 2021). The KG approach can be extended to integrate the data itself in a Virtual KG (VKG). Our work will derive inspiration from large-scale VKG efforts that have been undertaken or are currently underway as part of the OpenStreetMap project (Ding et al., 2021). For DOE Data Days, we share the proposed geospatial data platform hybrid (cloud/on-prem) architecture, our work-to-date on storing, retrieving, and transforming LiDAR and raster data relevant to two important NREL use-cases, including the Renewable Energy Potential (reV) Model, and present our proposal for a KG based data discovery engine.

data platform↗
DOE OSTI2024

An integrated experimental–modeling approach to identify key processes for carbon mineralization in fractured mafic and ultramafic rocks

Abstract Controlling atmospheric warming requires immediate reduction of carbon dioxide (CO2) emissions, as well as the active removal and sequestration of CO2 from current point sources. One promising proposed strategy to reduce atmospheric CO2 levels is geologic carbon sequestration (GCS), where CO2 is injected into the subsurface and reacts with the formation to precipitate carbonate minerals. Rapid mineralization has recently been reported for field tests in mafic and ultramafic rocks. However, unlike saline aquifers and depleted oil and gas reservoirs historically considered for GCS, these formations can have extremely low porosities and permeabilities, limiting storage volumes and reactive mineral surfaces to the preexisting fracture network. As a result, coupling between geochemical interactions and the fracture network evolution is a critical component of long-term, sustainable carbon storage. In this paper, we summarize recent advances in integrating experimental and modeling approaches to determine the first-order processes for carbon mineralization in a fractured mafic/ultramafic rock system. We observe the critical role of fracture aperture, flow, and surface characteristics in controlling the quantity, identity, and morphology of secondary precipitates and present where the influence of these factors can be reflected in newly developed thermo-hydro-mechanical–chemical models. Our findings provide a roadmap for future work on carbon mineralization, as we present the most important system components and key challenges that we are overcoming to enable GCS in mafic and ultramafic rocks.

58 GEOSCIENCES↗
DOE OSTI2022

Leakage From Coexisting Geologic Forcing and Injection‐Induced Pressurization: A Semi‐Analytical Solution for Multilayered Aquifers With Multiple Wells

Abstract Abnormal fluid pressures (above or below hydrostatic pressure) can develop and persist in sedimentary basins. The common occurrence of abnormal pressures may cause challenges for project permitting of geological carbon sequestration (GCS), particularly in reservoirs with pre‐injection overpressure. The leaky wells that may exist in some sedimentary basins can provide flow paths between deep brine aquifers and shallower freshwater aquifers. Pre‐injection relative overpressures can cause brine leakage through leaky wells even before any injection occurs. The tendency for flow through leaky wells is coupled with the process of pressure dissipation that occurs through aquitards. Specifically, with non‐zero permeability, aquitards can dissipate pressure over large areal extents and thereby reduce leakage rates through leaky wells. This study presents development of a semi‐analytical solution for hydraulic head and brine leakage in multilayered aquifer–aquitard systems with geologic pressure forcing. The geologic forcing that causes abnormal pressures in the multilayered system can coexist with any number of injection, extraction, and leaky wells that also affect fluid pressure. The semi‐analytical model is applied to explore how leakage through leaky wells varies as functions of pressurization rate, along with aquitard and leaky well properties in an overpressured multilayered system. The results show that although injection‐induced pressures can dissipate rapidly through suitably permeable aquitards, coexisting geologic forcing may create sustained rates of brine leakage into freshwater aquifers through leaky wells. In GCS, a very low‐permeability aquitard with high capillary entry pressure to free‐phase CO 2 is desired to serve as the caprock to prevent leakage of CO 2 from the storage reservoir. Nevertheless, the results from this study show that the brine leakage impact to shallow freshwater aquifers through leaky wells might substantially decrease with increasing aquitard permeability values, as long as small aquitard permeability and high capillary entry pressure serve to prevent CO 2 leakage.

Cihan, A.↗
DOE OSTI2023

Overview of System Integration Analysis Activities for Integrated Waste Management

Spent nuclear fuel (SNF) generated by the current fleet of commercial nuclear reactors is being stored at the reactor sites in spent fuel pools (SFPs) and in dry independent spent fuel storage installations (ISFSIs). The US Department of Energy Office of Nuclear Energy (DOE-NE) is developing an Integrated Waste Management Program (IWMP) comprising a suite of options and supporting analyses to enable future informed choices. The IWMP is organized into the following five major areas: 1) Consent-Based Siting, 2) IWM Facilities and Equipment Concepts and Development, 3) Transportation Capability Analysis and Support, 4) Information Technology Solutions and Support, and 5) System Integration Analysis and Support. This paper discusses the activities ongoing in the IWMP System Integration Analysis and Support area. Two main areas of research in system integration are data and tools development, as well as system analysis assessments. One of the tools being developed in the system integration area is the Used Nuclear Fuel-Storage, Transportation & Disposal Analysis Resource and Data System (UNF-ST&DARDS) tool. It is being developed as a foundational resource for DOE-NE to manage SNF data, along with several compatible analysis tools for time-dependent characterization of SNF and related systems. UNF-ST&DARDS has the unparalleled ability to track SNF through the entire back end of the fuel cycle—from the time the fuel is discharged from a reactor through its disposal in a geological repository. UNF ST&DARDS interfaces with the SCALE code system for nuclear analysis and COBRA-SFS for thermal analysis. Another main tool being developed is the Next Generation System Analysis Model (NGSAM). NGSAM is an agent-based simulation software tool expressly designed to be capable of modeling the waste management system. NGSAM has been developed to enable informed decision-making by providing the capability of analyzing various potential system options for the management of SNF and HLW. Using NGSAM, system architecture analyses are being conducted to support the future deployment of a comprehensive nuclear waste management system that considers all major back-end aspects of the nuclear fuel cycle (i.e., transportation, storage, and disposal). System analysis assessments may investigate the implications of various strategies such as different acceptance rates, acceptance queues, facility capacities and options, standardized canisters, and different assumed system operation start dates. Recently, some system analysis effort has begun to look at how the waste management system might operate for advanced reactor fuel cycles.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

SECARB Post Injection Assessment Report

The Southern States Energy Board (SSEB) managed the overall Southeast Regional Carbon Sequestration Partnership (SECARB) Phase III project and The Bureau of Economic Geology (BEG), at the University of Texas at Austin, managed all activities associated with the Early Test field site near Cranfield, Mississippi (the Early Test or Cranfield Project). The SECARB project at Cranfield was focused downdip of the oil rim and on demonstrating the stacked nature of the storage potential at this location: enhanced oil recovery (EOR) up-dip and saline downdip. Because the injection for EOR continued after the SECARB project ended, no actual post-injection period occurred. However, information collected during the SECARB-monitored injection period, including pressure and CO2 plume position, was utilized in an EOR carbon lifecycle assessment. The carbon lifecycle assessment provided insight on tracking injection fronts and production efficiencies across the field. To complete the post injection assessment, taking into account the continuing EOR period after closeout of SECARB field activities, this report provides data and analysis done under another DOE-funded project. The follow up project focused on the carbon lifecycle of EOR and included an assessment of EOR at as-operated and under possible future scenarios. This work was published by Hossieni et al. (2018) and Hosseininoosheri (2019).

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2020

Efficient Ensemble-Based Stochastic Gradient Methods for Optimization Under Geological Uncertainty

Ensemble-based stochastic gradient methods, such as the ensemble optimization (EnOpt) method, the simplex gradient (SG) method, and the stochastic simplex approximate gradient (StoSAG) method, approximate the gradient of an objective function using an ensemble of perturbed control vectors. These methods are increasingly used in solving reservoir optimization problems because they are not only easy to parallelize and couple with any simulator but also computationally more efficient than the conventional finite-difference method for gradient calculations. In this work, we show that EnOpt may fail to achieve sufficient improvement of the objective function when the differences between the objective function values of perturbed control variables and their ensemble mean are large. On the basis of the comparison of EnOpt and SG, we propose a hybrid gradient of EnOpt and SG to save on the computational cost of SG. We also suggest practical ways to reduce the computational cost of EnOpt and StoSAG by approximating the objective function values of unperturbed control variables using the values of perturbed ones. We first demonstrate the performance of our improved ensemble schemes using a benchmark problem. Results show that the proposed gradients saved about 30–50% of the computational cost of the same optimization by using EnOpt, SG, and StoSAG. As a real application, we consider pressure management in carbon storage reservoirs, for which brine extraction wells need to be optimally placed to reduce reservoir pressure buildup while maximizing the net present value. Results show that our improved schemes reduce the computational cost significantly.

58 GEOSCIENCES↗
DOE OSTI2025

Establishing an Early CO 2 Storage Complex in Kemper County, Mississippi: Project ECO 2 S (Phase III) (Final Progress Report)

“Establishing an Early CO 2 Storage Complex in Kemper County, Mississippi (Project ECO 2 S)” is an early mover project within the U.S. Department of Energy (DOE) and National Energy Technology Laboratory’s Carbon Storage Assurance Facility Enterprise, or CarbonSAFE, initiative. Phase III of Project ECO2S advanced the foundational work of Phase II, which confirmed the potential for safely, permanently, and economically storing commercial volumes of carbon dioxide (CO 2 ) in the regionally significant saline reservoir system near Mississippi Power Company’s (MPC) Kemper County Energy Facility. The primary objective of Phase III was to complete detailed site characterization necessary to obtain an Underground Injection Control (UIC) Class VI Permit to Construct from the U.S. Environmental Protection Agency. This involved transitioning from regional geologic assessments to high-resolution site-specific analyses.

03 NATURAL GAS↗
DOE OSTI2023

Superstoichiometric Alloying of H and Close‐Packed Fe‐Ni Metal Under High Pressures: Implications for Hydrogen Storage in Planetary Core

Although high pressure enables alloying between hydrogen and iron, hydrogen-to-iron molar ratio (H/Fe) so far found in experiments is mostly limited to 1 in the close-packed iron metal under high pressure. We report a H/(Fe + Ni) ratio of 1.8 ± 0.1 from (Fe,Ni)H x (or x ≥ 1.8) quenched from liquid, exceeding the amounts so far reported for densely packed Fe alloys. From the metastable behavior of the frozen (Fe,Ni)Hx liquid during decompression, we infer that the amount is a lower bound and therefore even a greater amount of H can be dissolved in the liquid part of Fe-rich cores of planets. The significant H storage capacity of liquid Fe-Ni alloy is important to consider for potential storage of H in the interiors of low-density planets as well as rocky planets.

58 GEOSCIENCES↗
DOE OSTI2024

Editorial: Subsurface microbiology within hydrocarbon resources or stored gases

A Research Topic on the microbiology of hydrocarbon and gas storage reservoirs has far reaching industrial applications. In recent decades, there has been a growing interest in understanding microbial communities in subsurface energy reservoirs, such as coal, oil, and shale beds. This area of research has broadened to include gas storage reservoirs for hydrogen and CO 2 . Scientists are beginning to unravel the unexpected impact microorganisms have on these systems, through changing the fluid geochemistry, the gas content, and even the permeability. By recognizing the influence of these tiny organisms on our engineered environments, we can develop better risk assessments, target mitigation strategies, expand energy production, and refine operational guidance, ultimately contributing to a more sustainable energy future.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Assessment of Enhanced Oil Recovery Using Carbon Dioxide in Michigan Basin Silurian Pinnacle Reefs

The Midwest Regional Carbon Sequestration Partnership (MRCSP) has incorporated the work of geologic research teams (Geoteams) in its regional characterization, project planning and carbon dioxide (CO 2 ) injection implementation work since the partnership was established by the U.S. Department of Energy (DOE) in 2003. Over this 16-year period, the cohort of Geoteams has grown from five to ten states and has contributed to the characterization of geologic sequestration opportunities, refinement of reservoir and seal data, and supported injection efforts through both predictive and post-injection assessments. The Michigan team at Western Michigan University has provided technical analysis and support of the enhanced oil recovery (EOR) using carbon dioxide (CO 2 ) in the Northern Michigan Silurian Pinnacle reefs. With Core Energy, LLC as the industry partner, this project has assessed the potential for EOR in known fields in the Michigan Pinnacle reefs and the capacity for CO2 storage in the reef reservoirs as they are depleted and after reservoir abandonment. This project managed by Core Energy, LLC, involves the only commercial CO 2 Enhanced Oil Recovery floods within the MRCSP region. The Silurian pinnacle reef trend shows significant potential for CO 2 -EOR: this play is relatively new, with approximately 60% of the wells in the largest Niagaran fields still accessible. Most wells have modern logs and there are many cores, core analyses, pressure tests and produced water analyses available for study from these fields. These carbonate reservoirs in Michigan have produced sufficient oil to warrant evaluation for additional recovery efforts. The role of the Michigan team was to collect and analyze all relevant data about reservoir properties, including lithologic and depositional characteristics of the reservoir formations and porosity and fluid flow properties through different compartments in the reservoir. The team has also developed models that represent the geological and physical characteristics of the reservoir and seal system.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2021

Development of a Regional Roadmap for Source Network and Storage Deployment for CarbonSAFE Illinois - Macon County (Task 10)

Research documented in this report includes (1) resource estimates for the Lower Mt. Simon Sandstone in the Illinois Basin, including storage and cost estimates, and (2) regional roadmaps for carbon capture and storage (CCS) deployment using five capture facilities throughout the Illinois Basin: Archer Daniels Midland Decatur (ADM Decatur), Abbott Power Plant, City Water, Light, and Power (CWLP), One Earth Energy, and Prairie State Energy Campus (PSEC). Resource estimates were developed using the Sequestration of CO 2 Tool (SCO2T) and data provided by the Illinois State Geological Survey (ISGS), and regional roadmaps for CCS deployment were developed in SimCCS Gateway, a decision-support tool for designing CCS infrastructure.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

Development of a Framework for Data Integration, Assimilation, and Learning for Geological Carbon Sequestration (DIAL-GCS) (Final Report)

This project aimed to develop and demonstrate a Data Integration, Assimilation, and Learning framework for geologic carbon sequestration projects (DIAL-GCS). DIAL-GCS is an intelligence monitoring system (IMS) for automating GCS closed-loop management by leveraging recent developments in machine learning technologies, complex event processing (CEP), and reduced-order modeling. The safe and efficient operation of GCS repositories requires integrated monitoring to track the injected CO¬2 as it moves within a storage reservoir. GCS projects are data intensive, as a result of proliferation of digital instrumentation and smart-sensing technologies. GCS projects are also resource intensive, often requiring multidisciplinary teams performing different monitoring, verification, accounting (MVA) tasks throughout the lifecycle of a project to ensure secure containment of injected CO2. The success of GCS thus depends in a large part on our ability to access, assimilate, and analyze heterogeneous data and information sources in a timely manner. This project included a number of meaningful and necessary tasks to transform the human domain knowledge into machine-interpretable rules for automating knowledge extraction and discovery in GCS. The specific technical objectives of the proposed DIAL-GCS project were to develop an ontology-driven GCS data management module for storing, querying, and exchanging GCS data (both historic and live sensor data) from multiple sources and in heterogeneous formats. Incorporate a CEP engine for detecting abnormal situations by seamlessly combining expert knowledge, rule-based reasoning, and machine learning. Enable uncertainty quantification and predictive analytics using a combination of coupled-process modeling, AI/ML methods, and reduced-order modeling, and integrate and demonstrate the system’s capabilities with both real and simulated data. As far as we know, this is one of the first projects aimed to develop intelligent monitoring systems (IMS) targeting the GCS. Under this project, the team had developed a large number of web applications and scientific algorithms that contribute the main theme of intelligent monitoring. The team has published more than a dozen peer reviewed papers and disseminated the research results at multiple technical meetings.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

Well–based monitoring of CO 2 geological sequestration operations in saline aquifers: Critical insights into key questions

Geological carbon sequestration in saline aquifers is one of the most promising strategies to help mitigate emissions of CO 2 to the atmosphere. Significant challenges in ensuring the security of the sequestration process rest in the evolution and expansion of the CO 2 plume in the subsurface. The ability to track the movement of the injected CO 2 poses another challenge. Critical questions related to the integrity of the sequestration operations in saline aquifers relate to plume characteristics that can we monitor using well-based variables. We addressed this and related questions using an integrated modeling framework through a numerical investigation of carbon sequestration in saline aquifers during long-term and post-injection periods. This modeling paradigm incorporates the effect of structural, geological, and petrophysical characteristics. That way, we can account for critical physicochemical processes, rock-fluid interactions, and lithology dependencies. The well fluid variables investigated include fluid composition, pH, fluid density, and ion activity. We learned that fluid property analytics and diagnostics can be powerful tools to estimate the movement of CO 2 and its storage in different trapping mechanisms. These analytics can help optimize operational aspects and simplify reservoir-scale models while still reflecting the complex nature of the CO 2 interactions underground and offering insights into plume evolution.

58 GEOSCIENCES↗
DOE OSTI2023

Continued Development and Advanced Testing of DPC Filler Cements (on FY22 R&D and Demonstration Activities) (Progress Report)

Commercial generation of energy by nuclear power plants in the United States (U.S.) has produced thousands of metric tons of spent nuclear fuel (SNF), the disposal of which is the responsibility of the U.S. Department of Energy (DOE). Utilities typically utilize the practice of storing this SNF in dual-purpose canisters (DPCs). DPCs were designed, licensed, and loaded to meet Nuclear Regulatory Commission (NRC) requirements that preclude the possibility of a criticality event during SNF storage and transport, but were not designed or loaded to preclude the possibility of a criticality event during the regulated post-closure period following disposal, which could be up to 1,000,000 years (Price, 2019). There are several options being investigated that could facilitate the disposal of SNF stored in DPCs in a geologic repository (Hardin et al., 2015; SNL 2020b; SNL 2021b). These include: (1) repackage the SNF into canisters that are designed to prevent criticality during the regulated post-closure period following disposal, but with an increased disposal cost estimated at approximately $\$$20B in United States dollars (USD) (Freeze et al., 2019); (2) analysis of the probability and consequences of criticality from the direct disposal of DPCs during a 1,000,000-year post-closure period in several geologic disposal media (Price, 2019); and (3) filling the void space of a DPC with a material before its disposal that significantly limits the potential for criticality over the post-closure regulatory period. This report further investigates the third option, filling DPC already containing SNF with a material to limit the potential for criticality over the post-closure regulatory period.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

Carbon content of soil fractions varies with season, rainfall, and soil fertility across a lowland tropical moist forest gradient

Tropical forests contain some of the largest soil carbon (C) stocks on Earth, making them broadly relevant to terrestrial-climate feedbacks, yet our understanding of how their soil organic C (SOC) fractions vary over space and time is limited. We studied effects of season, fertility, and mean annual precipitation (MAP) on the C contents of soil fractions across 14 lowland forests in Panama. We measured free-debris, occluded-debris, and mineral-associated SOC fractions, as well as soluble C associated with each fraction. We hypothesized that mineral-associated SOC would be greatest in infertile, strongly weathered soils with large reactive mineral contents. We also hypothesized that the debris SOC fractions would accumulate during the dry season, reflecting seasonal increases in litterfall. To address this, we compared soil fractions in wet and dry seasons from fertile and infertile soils across a range of 1809 – 2864 mm MAP. The C content (mg C / g soil) of all soil fractions varied with fertility and MAP: specifically, free-debris SOC was greatest in wet, high-fertility soils, and occluded-debris SOC was greater in high-fertility than low-fertility soils. The mineral-associated SOC fraction, which contained the majority of bulk soil C, showed increasing C content with greater MAP in infertile sites, presumably driving similar spatial patterns in the bulk soil. Only the free-debris SOC fraction showed strong seasonal variation, increasing in mass during the dry season. Nitrogen behaved similarly to C. In summary, soil C contents increased with MAP in infertile sites but not fertile sites, driven by the mineral-associated SOC fraction. The dry season had greater free-debris SOC, but this seasonal trend was not apparent in bulk soil C, likely because of the small size of the free-debris SOC fraction. Furthermore, changes in the quantity and seasonality of precipitation, which are projected for tropical forests, might shift spatial and temporal patterns of soil C storage, which would in turn influence forest-climate feedbacks for this C-rich biome.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Machine learning-based inversion for acoustic impedance with large synthetic training data: Workflow and data characterization

Where wells are sparse or training data are difficult to label with high-quality wireline-derived impedance logs, machine learning (ML)-based inversion of acoustic impedance typically depends on small training data sets, leading to biased prediction. We have advanced a novel workflow that applies large synthetic seismic training data to reduce facies-related bias. Using a geologically realistic model as the truth model, we randomly select sparse seed wells to perform sequential Gaussian simulation (SGS) for impedance models of the same geometry and simulate facies variability. We implement random forest regression on 30 features extracted from the synthetic volume. We observe that more seed wells tend to reduce facies-induced bias by sampling more types of facies, resulting in a better prediction. We then focus on the responses of SGS models to facies changes, the number of seed wells necessary for a useful synthetic model, and how much a synthetic model can help ML-based inversion. Here, we observe that the SGS synthetic training model outperforms well-direct training in general. For modeled clastic shore-zone systems in Miocene Gulf of Mexico, two or more seed wells are necessary for a significant reduction of root-mean-square error and outliners, and improvement of facies imaging. In a field-data test, we apply a similar workflow to quantitatively predict acoustic impedance, which is then converted to a sand-volume map at a high-frequency sequence (10–100 m), revealing detailed facies and sandstone patterns. Such results are valuable in many geologic and engineering applications, such as hydrocarbon and CO 2 reservoir prospecting, reserve estimation, simulation, etc.

3D seismic↗