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All sources NASA NTRS DOE OSTI

DOE OSTI2024

Computed Tomography Scanning and Petrophysical Measurements of Oriskany Core across Eastern Ohio

The computed tomography (CT) facilities and the Multi-Sensor Core Logger (MSCL) at the National Energy Technology Laboratory (NETL) in Morgantown, West Virginia were used to characterize core material from three Ohio wells. These wells are listed below along with their American Petroleum Institute (API) and Ohio Geological Survey (OGS) identification numbers: - New York Central System 1 well (API 34085200170000, OGS Core 855) - Herren Well (API 34099201650000, OGS Core 2914) - Garvin-King Well (API 34121215610000, OGS Core 2939) The primary impetus of this work was to capture a detailed digital representation of the available core from all three wells. The collaboration between the U.S. Department of Energy’s (DOE) NETL and the Ohio Department of Natural Resources, Division of Geological Survey enables other research entities to access information about this potential carbon storage location and its surrounding formations.

58 GEOSCIENCES↗

DOE OSTI2024

AI-Enhanced Co-Design for Next-Generation Microelectronics: Innovating Innovation (Workshop Report)

The Artificial Intelligence Enhanced Co-Design for Next Generation Microelectronics virtual workshop was held April 4-5, 2023, and attended by subject matter experts from universities, industry, and national laboratories. This was the third in a series of workshops to motivate the research community to identify and address major challenges facing microelectronics research and production. The 2023 workshop focused on a set of topics from materials to computing algorithms, and included discussions on relevant federal legislation and such as the Creating Helpful Incentives to Produce Semiconductors and Science Act (CHIPS Act) which was signed into law in the summer of 2022. Talks at the workshop included edge computing in radiation environments, new materials for neuromorphic computing, advanced packaging for microelectronics, and new AI techniques. We also received project updates from several of the Department of Energy (DOE) microelectronics co-design projects funded in the fall of 2021, and from three of the Energy Frontier Research Centers (EFRCs) that had been funded in the fall of 2022. The workshop also conducted a set of breakout discussions around the five principal research directions (PRDs) from the 2018 Department of Energy workshop report: 1) define innovative material, device, and architecture requirements driven by applications, algorithms, and software; 2) revolutionize memory and data storage; 3) re-imagine information flow unconstrained by interconnects; 4) redefine computing by leveraging unexploited physical phenomena; 5) reinvent the electricity grid through new materials, devices, and architectures. We tasked each breakout group to consider one primary PRD (and other PRDs as relevant topics arose during discussions) and to address questions such as whether the research community has embraced co-design as a methodology and whether new developments at any level of innovation from materials to programming models requires the research community to reevaluate the PRDs developed back in 2018.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

SMART – A Comprehensive Research and Development Program to Demonstrate Application of Machine Learning for Supporting CCS Deployment

The objective of the US Department of Energy’s SMART Initiative, i.e., Science-informed Machine Learning (ML) for Accelerating Real-Time Decisions in Subsurface Applications, is to showcase how the utilization of ML can significantly improve efficiency and effectiveness of field-scale commercial carbon storage operations. This paper will present the results from the current phase of SMART (field deployment) for demonstrating the applicability of ML-based tools and workflows for: (a) virtual learning during the pre-injection permitting phase, (b) advanced storage reservoir imaging to better characterize fractures and faults, and (c) dynamic storage reservoir modelling and optimization to inform operational decision making and visualization of system evolution.

Siriwardane, Hema↗

DOE OSTI2024

PCOR Partnership Atlas 6th Edition, Revised

The Plains CO 2 Reduction (PCOR) Partnership Atlas provides a profile of CO 2 sources and potential storage locations across the PCOR Partnership region. The revised sixth edition of the Atlas provides an up-to-date look at PCOR Partnership initiative activities, including additional regional characterization and updates on commercial projects. Additional background information on carbon capture, utilization, and storage (CCUS) is included to give a better understanding of how CCUS addresses concerns about climate change while allowing future energy needs to be met.

01 COAL, LIGNITE, AND PEAT↗

DOE OSTI2020

The Data Foundry: Secure Collaboration for the Geothermal Industry: Preprint

The Data Foundry provides secure, cloud-based storage and universal access to digital information, enabling the greater geothermal industry to collaborate seamlessly with the Department of Energy (DOE), national labs, universities, and private organizations. Originally developed to support the EGS Collab project, the Data Foundry has been expanded to support FORGE, EDGE, and other DOE-funded projects, some collaborative, some private, by providing each project with a secure space and the ability to fine-tune individual access controls. In response to user feedback, it now also features improved integration with DOE’s Geothermal Data Repository (GDR), to provide a clear and convenient pathway from collaboration to publication, and to register collaborative data projects with well-known data registries like Data.gov and the National Geothermal Data System (NGDS). This paper will explore how recent concerns raised by data-centric, proprietary projects have informed development on the Data Foundry and highlight improvements designed to streamline workflows, improve access control, and promote the timely dissemination of information to the geothermal industry.

Data Foundry↗

DOE OSTI2020

Leveraging operational information from wastewater injection wells to evaluate CO 2 injection performance for carbon storage applications in the Appalachian Basin

Abstract Geologic parameters, geophysical logging, injection testing, and operational metrics from wastewater injection wells were integrated to develop a preliminary design of a carbon storage facility in the Appalachian Basin. A scattered group of 10–20 commercial wastewater injection wells dispose off produced water from oil and gas wells in the region, utilizing a sequence of stacked deep saline formations for injection zones. These wastewater injection wells provide practical benchmarks for understanding the feasibility of carbon dioxide (CO 2 ) storage. Geologic models were developed based on characterization data from the wastewater injection wells. Reservoir simulations were calibrated according to injection testing and operational data from the wastewater injection wells. Long‐term operational data on injection flow rates and pressures measured in the wastewater injection wells were especially useful to evaluate the performance of carbon storage applications. The simulations were used to estimate injection pressures, radius of CO 2 saturation, and pressure response for industrial scale CO 2 storage applications. Results were also used to provide a design basis in terms of number of injection wells, well spacing, area of review, injection system components, monitoring plan, and CO 2 pipeline distribution system. The analysis demonstrates that there is sufficient injectivity in the deep saline formations in the west‐central Appalachian Basin to store commercial volumes of anthropogenic CO 2 . The geologic system appears suitable for supporting CO 2 injection rates of 0.5–1.0 million metric tons per year at injection pressures below formation fracture pressure in a single well. The long‐term operational data of wastewater injection wells within the study area suggested a lower permeability‐thickness values than indicated by initial reservoir tests. A workflow for developing realistic permeability values for input into reservoir simulations is presented. © 2020 Society of Chemical Industry and John Wiley & Sons, Ltd.

Valluri, Manoj↗

DOE OSTI2021

Hydropower Energy Storage Capacity Dataset

The Hydropower Energy Storage Capacity (HESC) Dataset catalogues estimates of nominal energy storage capacity based on varying levels of detail. Dams and reservoirs selected were selected based on those reported in the National Inventory of Dams (NID 2019) and/or the Global Reservoir and Dam (GRanD v1.3) datasets. These data provide a foundation for understanding available resources at existing hydropower facilities and their potential to provide storage of energy and more flexible generation. Current estimates include Level 1 (based on maximum storage capacities and hydraulic head) and Level 2 (based on historical models or observations of reservoir volume and hydraulic head). For facilities where installed capacity is known, there are also estimates for discharge duration or the length of time when a facility could provide generation at a given capacity. Essential information used to calculate the energy storage capacity and discharge duration (volume, hydraulic head, and details about the sources or records used to obtain those parameters) and summaries of historical generation (for context) are also included.

13 HYDRO ENERGY↗

DOE OSTI2022

Data reduction through optimized scalar quantization for more compact neural networks

Raw data generation for several existing and planned large physics experiments now exceeds TB/s rates, generating untenable data sets in very little time. Those data often demonstrate high dimensionality while containing limited information. Meanwhile, Machine Learning algorithms are now becoming an essential part of data processing and data analysis. Those algorithms can be used offline for post processing and post data analysis, or they can be used online for real time processing providing ultra low latency experiment monitoring. Both use cases would benefit from data throughput reduction while preserving relevant information: one by reducing the offline storage requirements by several orders of magnitude and the other by allowing ultra fast online inferencing with low complexity Machine Learning models. Moreover, reducing the data source throughput also reduces material cost, power and data management requirements. In this work we demonstrate optimized nonuniform scalar quantization for data source reduction. This data reduction allows lower dimensional representations while preserving the relevant information of the data, thus enabling high accuracy Tiny Machine Learning classifier models for online fast inferences. We demonstrate this approach with an initial proof of concept targeting the CookieBox, an array of electron spectrometers used for angular streaking, that was developed for LCLS-II as an online beam diagnostic tool. We used the Lloyd-Max algorithm with the CookieBox dataset to design an optimized nonuniform scalar quantizer. Optimized quantization lets us reduce input data volume by 69% with no significant impact on inference accuracy. When we tolerate a 2% loss on inference accuracy, we achieved 81% of input data reduction. Finally, the change from a 7-bit to a 3-bit input data quantization reduces our neural network size by 38%.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2026

Shelter Island, New York, Energy Reliability Assessment and Local Generation Options

Shelter Island's Green Options Committee (GOC), an all-volunteer committee tasked with considering all environmental conservation issues, requested technical assistance under the U.S. Department of Energy (DOE)-funded Energy Technology Innovation Partnership Project (ETIPP). The primary goals of this technical assistance project included: 1. Informing the GOC about energy use trends within the community as well as distributed solar, wind and storage opportunities; 2. Providing additional resources and technical feasibility information for geothermal, agrivoltaics and tidal energy; 3. Supporting the development of a community engagement plan; and 4. Supporting collaboration between the GOC and PSEG in order to find mutually beneficial follow-on projects. Assistance from NLR to help the GOC meet these overall project goals was provided through the following primary tasks: 1. Collaborate with the local utility in order to collect energy use data and inform the community on energy use patterns through a baseline assessment; 2. Technical analysis of distributed renewable and resiliency opportunities (solar, wind and storage) that best align with the community's energy priorities; 3. Provide high level feasibility support for future renewable energy scenarios that include agrivoltaic solutions, tidal energy, and geothermal projects on Shelter Island, and 4. Integrate all findings into a Community Outreach presentation to help the GOC engage with community stakeholders to build support and awareness of chosen resilience strategies.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2025

The CO2 mineralization potential of New Mexico basalts and associated critical element behavior and mobility.

This presentation reports on laboratory experiments assessing the carbon dioxide (CO₂) mineralization potential of New Mexico basalts, including trachybasalt and basalt lithologies. Using simulated groundwater and controlled conditions, the study evaluates the kinetics of CO₂ sequestration, mineral saturation indices, and secondary mineral formation. It also investigates the mobility of critical and trace elements such as Ni, Zn, Cu, and Li during basalt dissolution. Results indicate that New Mexico basalts can effectively sequester CO₂ through carbonate formation, with grain size and mineral composition influencing reaction rates and elemental behavior. These findings support the viability of geologic carbon storage in mafic volcanic terrains and inform strategies for critical element recovery.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Viability of S3 Object Storage for the ASC Program at Sandia

Recent efforts at Sandia such as DataSEA are creating search engines that enable analysts to query the institution’s massive archive of simulation and experiment data. The benefit of this work is that analysts will be able to retrieve all historical information about a system component that the institution has amassed over the years and make better-informed decisions in current work. As DataSEA gains momentum, it faces multiple technical challenges relating to capacity storage. From a raw capacity perspective, data producers will rapidly overwhelm the system with massive amounts of data. From an accessibility perspective, analysts will expect to be able to retrieve any portion of the bulk data, from any system on the enterprise network. Sandia’s Institutional Computing is mitigating storage problems at the enterprise level by procuring new capacity storage systems that can be accessed from anywhere on the enterprise network. These systems use the simple storage service, or S3, API for data transfers. While S3 uses objects instead of files, users can access it from their desktops or Sandia’s high-performance computing (HPC) platforms. S3 is particularly well suited for bulk storage in DataSEA, as datasets can be decomposed into object that can be referenced and retrieved individually, as needed by an analyst. In this report we describe our experiences working with S3 storage and provide information about how developers can leverage Sandia’s current systems. We present performance results from two sets of experiments. First, we measure S3 throughput when exchanging data between four different HPC platforms and two different enterprise S3 storage systems on the Sandia Restricted Network (SRN). Second, we measure the performance of S3 when communicating with a custom-built Ceph storage system that was constructed from HPC components. Overall, while S3 storage is significantly slower than traditional HPC storage, it provides significant accessibility benefits that will be valuable for archiving and exploiting historical data. There are multiple opportunities that arise from this work, including enhancing DataSEA to leverage S3 for bulk storage and adding native S3 support to Sandia’s IOSS library.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

DOE OSTI2024

Investigation of acoustic waves under subsurface conditions to improve the predictions of rock mechanical properties and natural fracture characteristics

Mechanical properties and natural fracture characteristics are critical to investigate for subsurface engineering applications, including carbon storage, well drilling, and stimulation, as they govern rock stability, fluid flow, and mechanical behavior under stress. This dissertation integrates experimental and machine learning approaches to enhance the prediction and understanding of these properties by analyzing acoustic wave behavior under varied subsurface conditions. First, the influence of temperature, pore pressure, and supercritical CO2 (scCO2) saturation on poroelastic properties is examined using Gray Berea sandstone samples. The results show that temperature and pore pressure significantly affect the bulk modulus and Biot’s coefficient, while scCO2 saturation impacts rock compressibility, informing strategies for effective geological carbon storage. The study extends this understanding by experimentally evaluating the impact of reservoir depletion on the dynamic mechanical properties of the emerging Caney shale in South Oklahoma with the employment of unsupervised machine learning to predict static mechanical properties across the Caney shale. Integrating petrophysical data and chemostratigraphy, the workflow—featuring K-means clustering, principal component analysis (PCA), and inverse distance weighting (IDW)—improves stratigraphic characterization and the estimation of static-to-dynamic modulus ratios, which is vital for optimizing drilling and stimulation strategies. Finally, the work explores how natural fracture characteristics in shale influence acoustic waveforms and shear wave splitting (SWS) analysis. Experimental data on fractured samples under different stress and temperature conditions, combined with machine learning models such as K-nearest neighbors (KNN) and extreme gradient boosting (XGBoost), reveal key fracture properties impacting SWS and wave propagation. Together, these studies provide a comprehensive framework for linking acoustic wave behavior with rock properties, advancing the methods for monitoring and predicting geomechanical changes. The insights offered valuable implications for safer, more efficient CO2 injection, hydrocarbon extraction, and subsurface management.

Elkholy, Sherif↗

DOE OSTI2026

Williston basin associated co 2 storage field laboratory

The Williston Basin Associated CO 2 Storage Field Laboratory (WBCFL), led by the Energy & Environmental Research Center (EERC) in partnership with Denbury (now ExxonMobil), was established to advance understanding of CO 2 enhanced oil recovery (EOR) and associated geologic CO 2 storage in the Williston Basin. The specific objective of the WBCFL was to conduct field and laboratory activities to inform decisionmakers regarding the 1) efficacy of associated storage in stacked geologic reservoirs during EOR operations; 2) CO 2 injectivity, oil production, and storage in residual oil zones (ROZs); 3) use of a novel CO 2 storage monitoring, verification, and accounting (MVA) technology (active seismoelectric [ASE] monitoring), and 4) life cycle and techno-economic factors for EOR development scenarios targeting ROZs. The project location was the Pennel Field in southeast Montana, which is part of the Cedar Creek Anticline (CCA) geologic structure within the Williston Basin.

02 PETROLEUM↗

DOE OSTI2024

Si/SiGe QuBus for single electron information-processing devices with memory and micron-scale connectivity function

The connectivity within single carrier information-processing devices requires transport and storage of single charge quanta. Single electrons have been adiabatically transported while confined to a moving quantum dot in short, all-electrical Si/SiGe shuttle device, called quantum bus (QuBus). Here we show a QuBus spanning a length of 10 μm and operated by only six simply-tunable voltage pulses. We introduce a characterization method, called shuttle-tomography, to benchmark the potential imperfections and local shuttle-fidelity of the QuBus. The fidelity of the single-electron shuttle across the full device and back (a total distance of 19 μm) is (99.7 ± 0.3) %. Using the QuBus, we position and detect up to 34 electrons and initialize a register of 34 quantum dots with arbitrarily chosen patterns of zero and single-electrons. The simple operation signals, compatibility with industry fabrication and low spin-environment-interaction in 28 Si/SiGe, promises long-range spin-conserving transport of spin qubits for quantum connectivity in quantum computing architectures.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Evaluating Corrosion Effects on the Stainless Steel Components of the SAVY-4000/Hagan Nuclear Material Storage Containers: FY2022 Update

This report summarizes the work completed and underway for the Corrosion Working Group (CWG) for FY2022 in support of evaluating a greater than a 15 year design life for the SAVY4000. The CWG meets on an as needed basis to review new work, results or information regarding the corrosion of SAVY-4000 and Hagan nuclear material storage containers. The goal when analyzing corrosion is to develop a design life appropriate for the containers and to identify when a container should be removed from service. There are many parallel efforts associated with identifying and extending the service life of nuclear material storage containers. This report includes information from research and development efforts to address the lifetime. The CWG is closely related to surveillance activities and continues to play a role in identification of issues and paths forward for analysis of surveillance containers. This work summarizes the completed tasks for FY22 of the CWG while keeping in mind that some efforts continue into future years. Brief summaries of identified new avenues of work have been identified for FY23.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Data reduction in deterministic neutron transport calculations using machine learning

Neutron cross section matrices for fission and scattering data are required for each material, temperature, and enrichment level to calculate the neutron transport equation accurately. Here, this information can be a limiting factor when using the multigroup discrete ordinates (S N ) method when the number of energy groups is large. Machine Learning (ML) can be used to replace the need for the cross section matrices by reproducing the function that maps the scalar flux to the scattering and fission sources. Through the use of autoencoders and Deep Jointly-Informed Neural Networks (DJINN), the data storage requirements are reduced by 94% of the original data for a 618 group problem. This is accomplished while preserving the scalar flux, maintaining generality, and decreasing wall clock times.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2021

Satellite Observations of the Tropical Terrestrial Carbon Balance and Interactions With the Water Cycle During the 21st Century

A constellation of satellites are now in orbit providing information about terrestrial carbon and water storage and fluxes. These combined observations show that the tropical biosphere has changed significantly in the last two decades from the combined effects of climate variability and land use. Large areas of forest have been cleared in both wet and dry forests, increasing the source of carbon to the atmosphere. Concomitantly, tropical fire emissions have declined, at least until 2016, from changes in land-use practices and rainfall, increasing the net carbon sink. Measurements of carbon stocks and fluxes from disturbance and recovery and of vegetation photosynthesis show significant regional variability of net biosphere exchange (NBE) and gross primary productivity (GPP) across the tropics and are tied to seasonal and interannual changes in water fluxes and storage. Comparison of satellite based estimates of evapotranspiration (ET), photosynthesis, and the deuterium content of water vapor with patterns of total water storage and rainfall demonstrate the presence of vegetation-atmosphere interactions and feedback mechanisms across tropical forests. However, these observations of stocks, fluxes and inferred interactions between them do not point unambiguously to either positive or negative feedbacks in carbon and water exchanges. These ambiguities highlight the need for assimilation of these new measurements with Earth System models for a consistent assessment of process interactions, along with focused field campaigns that integrate ground, aircraft and satellite measurements, to quantify the controlling carbon and water processes and their feedback mechanisms.

58 GEOSCIENCES↗

DOE OSTI2022

Multiple ferroelectric nematic phases of a highly polar liquid crystal compound

Ferroelectric nematic liquid crystals represent not only fascinating, fundamental science, but they also hold promise for new technologies including high-density power storage or sub-millisecond switching information displays. In this work, we describe the synthesis and measurements of the physical properties of a new compound, 4-nitrophenyl 4-[(2,4-dimethoxylbenzoyl)oxy]-2-fluorobenzoate (RT11001). This material exhibits multiple, highly polar, ferroelectric nematic phases that have not been previously reported. Here we employ a wide range of physical characterisation methods including differential scanning calorimetry (DSC), mass density measurement, optical birefringence, polarising optical microscopy (POM), dielectric spectroscopy, electric current analysis, electro-optical switching, small-angle and wide-angle x-ray scattering measurements to show that RT11001 has multiple, distinct ferroelectric phases. We argue that the highest temperature phase is a polar nematic fluid with non-polar smectic clusters. Directly below appears to be a transition to another polar nematic phase containing polar positionally ordered clusters. Lastly, there are indications of an additional, polar biaxial liquid crystal phase at lower temperatures.

36 MATERIALS SCIENCE↗