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DOE OSTI2023

J&L #1 Well-Pennsylvania Oriskany Characterization

Dataset for this database page represents data from technical report "Computed Tomography Scanning and Geophysical Measurements of Appalachian Basin Core from the Jones and Laughlin #1 Well, Beaver County PA " Sharma, M; Paronish, T.; Mitchell, N.; Crandall, D.; Neubaum, J. Computed Tomography Scanning and Geophysical Measurements of Appalachian Basin Core from the Jones and Laughlin #1 Well, Beaver County PA; DOE.NETL-2023.3889; NETL Technical Report Series; U.S. Department of Energy, National Energy Technology Laboratory: Morgantown, WV, 2023; p 37.

Appalachian Basin↗

DOE OSTI2024

Datasets and U-Net Model for "A Deep Learning Based Framework to Identify Undocumented Orphaned Oil and Gas Wells from Historical Maps: a Case Study for California and Oklahoma"

This dataset has results and the model associated with the publication Ciulla et al., (2024). It contains a U-Net semantic segmentation model (unet_model.h5) and associated code implemented in tensorflow 2.0 for the model training and identification of oil and gas well symbols in USGS historical topographic maps (HTMC). Given a quadrangle map (7.5 minutes), downloadable at this url: https://ngmdb.usgs.gov/topoview/, and a list of coordinates of the documented wells present in the area, the model returns the coordinates of oil and gas symbols in the HTMC maps. For reproducibility of our workflow, we provide a sample map in California and the documented well locations for the entire State of California (CalGEM_AllWells_20231128.csv) downloaded from https://www.conservation.ca.gov/calgem/maps/Pages/GISMapping2.aspx. Additionally, the locations of 1,301 potential undocumented orphaned wells identified using our deep learning framework or the counties of Los Angeles and Kern in California, and Osage and Oklahoma in Oklahoma are provided in the file found_potential_UOWs.zip. The results of the visual inspection of satellite imagery in Osage County is in the file visible_potential_UOWs.zip. The dataset also includes a custom tool to validate the detected symbols in the HTMC maps (vetting_tool.py). More details about the methodology can be found in the associated paper: Ciulla, F., Santos, A., Jordan, P., Kneafsey, T., Biraud, S.C., and Varadharajan, C. (2024) A Deep Learning Based Framework to Identify Undocumented Orphaned Oil and Gas Wells from Historical Maps: a Case Study for California and Oklahoma. Accepted for publication in Environmental Science and Technology. The geographical coordinates provided correspond to the locations of potential undocumented orphaned oil and gas wells (UOWs) extracted from historical maps. The actual presence of wells need to be confirmed with on-the-ground investigations. For your safety, do not attempt to visit or investigate these sites without appropriate safety training, proper equipment, and authorization from local authorities. Approaching these well sites without proper personal protective equipment (PPE) may pose significant health and safety risks. Oil and gas wells can emit hazardous gasses including methane, which is flammable, odorless and colorless, as well as hydrogen sulfide, which can be fatal even at low concentrations. Additionally, there may be unstable ground near the wellhead that may collapse around the wellbore. This dataset was prepared as an account of work sponsored by the United States Government. While this document is believed to contain correct information, neither the United States Government nor any agency thereof, nor the Regents of the University of California, nor any of their employees, makes any warranty, express or implied, or assumes any legal responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by its trade name, trademark, manufacturer, or otherwise, does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof, or the Regents of the University of California. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof or the Regents of the University of California.

Artificial Intelligence↗

DOE OSTI2023

Finchite, Sr(UO 2 ) 2 (V 2 O 8 )·5H 2 O, a new uranyl sorovanadate with the francevillite anion topology

Finchite (IMA2017-052), Sr(UO 2 ) 2 (V 2 O 8 )·5H 2 O, is the first uranium mineral known to contain essential Sr. The new mineral occurs as yellow-green blades up to ~10 µm in length in surface outcrops of the calcrete-type uranium deposit at Sulfur Springs Draw, Martin County, Texas, U.S.A. Crystals of finchite were subsequently discovered underground in the Pandora mine, La Sal, San Juan County, Utah, U.S.A., as diamond-shaped golden-yellow crystals reaching up to 1 mm. In this work, the crystal structure of finchite from both localities was determined using single-crystal X-ray diffraction and is orthorhombic, Pcan, with a = 10.363(6) Å, b = 8.498(5) Å, c = 16.250(9) Å, V = 1431.0(13) Å 3 , Z = 4 (R 1 = 0.0555) from Sulfur Springs Draw; and a = 10.3898(16), b = 8.5326(14), c = 16.3765(3) Å, V = 1451.8(4) Å 3 , Z = 4 (R 1 = 0.0600) from the Pandora mine. Electron-probe microanalysis provided the empirical formula (Sr 0.88 K 0.17 Ca 0.10 Mg 0.07 Al 0.03 Fe 0.02 ) Σ1.20 (UO 2 ) 2 (V 2.08 O 8 )·5H 2 O for crystals from Sulfur Springs Draw, and (Sr 0.50 Ca 0.28 Ba 0.22 K 0.05 ) Σ0.94 (U 0.99 O 2 ) 2 (V 2.01 O 8 )·5H 2 O for crystals from the Pandora mine, based on 17 O atoms per formula unit. The structure of finchite contains uranyl vanadate sheets based upon the francevillite topology. Finchite is a possible immobilization species for both uranium and the dangerous radionuclide 90 Sr because of the relative insolubility of uranyl vanadate minerals in water.

58 GEOSCIENCES↗

DOE OSTI2020

Herbaceous Feedstock 2018 State of Technology Report

The U.S. Department of Energy (DOE) promotes the production of advanced liquid transportation fuels from lignocellulosic biomass by funding fundamental and applied research that advances the state of technology (SOT). As part of its involvement with this mission, Idaho National Laboratory (INL) completes an annual SOT report for biomass feedstock logistics. This report summarizes supply system impacts of Bioenergy Technologies Office (BETO)-funded research and development efforts at INL and elsewhere (such as the High-Tonnage Feedstock Logistics projects (Webb et al. 2013a, Webb et al. 2013b, Webb et al. 2013c, Webb and Sokhansanj 2014, Sokhansanj et al. 2014) that lead to improvements in feedstock supply systems. These include improvements to and observed performance of innovative harvest and collection methods, storage technologies, transportation and handling approaches, and advanced preprocessing technologies. Biomass quality and variability, and the interface between feedstock quality and conversion performance are key drivers in addition to delivered feedstock cost. In this report, we estimate the benefits of R&D improvements to individual supply system unit operations, and present the status of feedstock logistics technology development for converting biomass into biofuels. These analyses are supported by experimental data where possible, and help to align the SOT relative to the cost goals defined in the Multi-Year Program Plan. The 2018 Herbaceous SOT aligned feedstock logistic design with current biorefinery’s design capacity utilized by biochemical conversion platform. Currently biochemical conversion platform utilizes a 725,000 dry ton/year biorefiney design for the techno economic analysis. Hence, feedstock delivered cost in the 2018 Herbaceous SOT is calculated based on biorefinery’s 725,000 dry ton design capacity instead of 800, 000 dry ton capacity utilized in the 2017 Herbaceous SOT. Biomass availabilities in this SOT were updated to year 2018 data from the 2016 Billion-Ton Report (BT16) (DOE 2016a), with the exception of switchgrass, for which the 2018 Herbaceous SOT utilized the 2019 switchgrass availability data from BT16. The BT16 report (DOE 2016a) does not project switchgrass availability in 2018; the soonest switchgrass is available in the BT16 report is 2019. Therefore, availability of switchgrass for this analysis was that projected for 2019. The 2018 Herbaceous SOT incorporates same technologies utilized in the 2017 Herbaceous SOT. However, a sensitivity analysis is performed to understand the impact of variation of process parameters on those technologies on feedstock logistic cost. New R&D data that shows the variations of process parameters affecting process performance is incorporated in the 2018 SOT to measure the variations in delivered feedstock cost. The 2018 Herbaceous SOT has also provided projected delivered feedstock of 2022 design case based on near term technical target under BETO funded R&D project. Finally, updated biorefinery size of 725,000 dry ton/year was incorporated within least-cost formulation model to select optimal siting and depot scales during optimization of the least cost blend. This modification to the optimization algorithm allows the trade-off between the cost of increased supply radius and the savings from selecting biomass from higher producing counties to be assessed. Such optimization has also showed the economic benefit of decentralized depots in comparison to centralized preprocessing co-located with the biorefinery by decoupling the biorefinery and feedstock locations. The 2018 Herbaceous SOT report documents the current modeled cost of a herbaceous feedstock supply system (from harvest to the pretreatment reactor throat, including grower payment) for hydrocarbon fuel production via biochemical conversion, based on equipment and processes now available or potentially available in the near term. The modeled cost also considers both the required quality and the availability of the biomass resources. The 2018 Herbaceous SOT predicts a modeled delivered feedstock cost of $83.67/dry ton (2016$); this is a $0.23/dry ton (2016$) decrease from the 2017 Herbaceous SOT. The modification of biorefinery’s designed capacity and increased projected biomass availability in the same supply shed contributed to this modeled cost reduction. The least-cost formulation model to optimally site and scale local distributed preprocessing depots also contributed to the cost reduction by considering county-level grower payment and distance from the biorefinery as variables in the optimization algorithm. Sensitivity analysis on various process parameters that affect delivered feedstock cost in the 2018 Herbaceous SOT shows that the delivered cost could varies from $80.45-$88.83/dry ton. The top factors that causes such variations are: effective baling rate, bale density, hammer mill throughput, interest rate and storage dry matter loss.

09 BIOMASS FUELS↗

DOE OSTI2020

Facility Location Optimization Model for COVID-19 Resources

In response to anticipated resource shortfalls related to the treatment and testing of COVID-19, many communities are planning to build additional facilities to increase capacity. These facilities include field hospitals, testing centers, mobile manufacturing units, and distribution centers. In many cases, these facilities are intended to be temporary and are designed to meet an immediate need. When deciding where to place new facilities many factors need to be considered, including the feasibility of potential locations, existing resource availability, anticipated demand, and accessibility between patients and the new facility. In this project, a facility location optimization model was developed to integrate these key pieces of information to help decision makers identify the best place, or places, to build a facility to meet anticipated resource demands. The facility location optimization model uses the location of existing resources and the anticipated resource demand at each location to minimize the distance a patient must travel to get to the resource they need. The optimization formulation is presented below. The model was designed to operate at the county scale, where patients are grouped per county. This assumption can be modified to integrate other scales or include individual patients.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2020

Inspection Status Report of Site A/Plot M (Report for 2020)

The Site A/Plot M, Decommissioned Reactor Site was inspected on September 24, 2020. Site A/Plot M is located in the Palos Area Preserves, operated by the Forest Preserve District of Cook County. The site was found to be in good condition with minimal erosional concerns across the grass covered mound at Plot M. Landscaping timbers installed along the footpath north of Plot M is effective in reducing erosion down the hill. Erosion concerns at Plot M are being addressed by Argonne National Laboratory (ANL). The bike trail installed by Cook County Forest Preserve to the east of Plot M has helped reduce traffic at the site. The Plot M monument appears solid with no visible cracks. The Site A monument has several cracks present that will possibly widen over time. No cause for a follow up inspection was identified.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Opportunities for Solar Industrial Process Heat in the United States

Energy used in the production and processing of materials and products represents a significant fraction of the overall energy footprint of the industrial sector. Solar technologies may provide suitable alternative to existing combustion technologies, but their relevance for industries in the United States has been largely unexplored at the necessary levels of spatial, temporal, and operational detail. To provide the first assessment of the technical opportunities for solar technologies to meet the demand for process heat in the United States, we first develop estimates of energy use by process temperature at the county and hourly resolutions for all manufacturing industries. We then estimate the opportunities for seven solar technology packages—comprised of solar thermal and photovoltaic-connected electrotechnologies—to meet process heat demand given solar resources and available land area by county.

14 SOLAR ENERGY↗

DOE OSTI2021

Well Stimulation Treatment in California: Evaluation of Disclosure Data, May 2015 – October 2019

In this document, disclosure data from 1,228 well stimulations occurring May 2015 to October 2019 in California are evaluated. This evaluation updates a previous study that was based on 618 well stimulations occurring May 2015 to June 2016 (Stringfellow, W.T., Camarillo, M.K., and Jordan, P. 2017, Status of Well Stimulation in California Since Implementation of SB-4 Regulations, Berkeley National Laboratory, Berkeley, CA). While the goal of the previous study was to provide a summary of information obtained since passage of California Senate Bill No. 4—Oil and Gas, the goal of the current evaluation is to provide a basis for studying chemical indicators that could potentially be used to detect aquifer contamination. We evaluate chemical indicators is a separate document using the data contained herein as well as data from monitoring wells located within oil fields. Compared with the data used by Stringfellow et al. 2017, the current evaluation is based on a more diverse data set. While the previous study contained data for only two oil fields with more than 30 well stimulations, the current data set contains four oil fields with more than 30 well stimulations, making these data more ideal for comparisons using statistical tests. More producers and a new oil field, Buena Vista Nose, are represented in the current data set. Some well stimulation practices remain relatively unchanged since the previous study. Well stimulation is mostly occurring in Kern County with the exception of a single well stimulation in Orange County. Almost all well stimulations are hydraulic fracturing. The current data set contains two acid fracturing treatments and one matrix acidizing treatment. The median number of chemicals added per well stimulation—excluding water and proppant—was previously 21; the number in the expanded data set is 20. Median water added per well stimulation was previously 89,000 gallons and is now 98,000 gallons. While this water use represents a 10% increase, water use for hydraulic fracturing in California is still lower than water use in other oil and gas fields. Chemical formulations appear to have shifted since the previous study. This shift may be related to a change in the predominant service company. We identified 26 chemicals for which the frequency of use has changed by more than 30%. The total number of chemicals used has also expanded. Previously, 178 unique chemicals were identified as being added to well stimulation fluids. The total number of unique chemicals is now 205. Analytical data available for base and recovered fluids has expanded. The number of base fluid samples has increased from 12 to 35 and the number of recovered fluid samples has increased from 1,078 to 2,166. The expanded data set is more ideal for an evaluation of indicator chemicals and comparisons across different formations. The analytical data for recovered fluid samples indicates that many ions, radioactive constituents, and organics are consistently observed, making these chemicals ideal for consideration as potential indicators of aquifer contamination.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Coal-dependent Communities in Transition: Identifying Best Practices to Ensure Equitable Outcomes

The U.S. coal industry is experiencing a sharp increase in the numbers of retired and/or decommissioned coal-fired power plants across the U.S. In the years between 2010-2019, around 102 gigawatts (GW) of coal-fired generating capacity has been announced to be decommissioned, representing more than 546 coal-fired power plant units, and an additional 17 GW is planned to be decommissioned by 2025. This change in the energy production landscape presents an impact on the social, environmental, and economic prospects of coal-dependent communities. This report examined the role of communities in the coal power plant decommissioning process and provided community-identified best practices to ensure an equitable process. The experiences of four coal-dependent communities—Wise County, VA, Muskegon, MI, Anderson County, TN, and Becker, MN—are presented as case studies to understand the impacts of the decommissioning process, and associated best practices, from the communities’ perspective. The report results highlight the need to recognize that the decommissioning decision-making process must be community-based to be equitable. Each community’s input is key to the transition away from coal power because there is no one-size-fits-all development plan. In other words, each community’s trajectory through the decommissioning process—from the retirement decision-making stage to the final site redevelopment phase—is unique because each community has distinct needs and wants from the energy transition. What is best for one community may not be suited for another. Ultimately, the framework for site development and community revitalization post-decommissioning cannot be universal because each community’s profile—from a social, cultural, and economic perspective—is different. Community impacts of power plant decommissioning are not limited to job and revenue losses. Communities are likely to be impacted culturally, socially, environmentally, and have long-term health-based impacts that should be acknowledged and addressed in post-retirement plans. Commonly identified decommissioning best practices include: Early and continued engagement throughout, with a number of mediums for communication and feedback (e.g., in-person sessions, virtual meetings, written comment opportunities); Early planning of post-decommissioning projects to replace lost jobs, revenue, and economic activity; Recognition (and mitigation, if possible) of social impacts on the community due to plant closure; Transparency throughout the process, with trusted information being provided about the decommissioning process and timeline; potential impacts on the workforce, economy, and environment; and the feasibility of alternative site uses; Identification of funding sources, technical experts, and/or strategic partnerships to support decommissioning and the affected communities upfront; and, Acknowledgment of communities as stakeholders who have a role in the conversation and right to determine their futures. Three key areas for assisting coal-dependent communities affected by the energy transition: Technical assistance: assessment of site feasibility for alternative uses or to repower with new technologies; Cross-partnership engagement and collaboration: facilitate knowledge-sharing of “lessons learned” about the decommissioning process between communities and provide guidance for decision-making processes; and, Financial assistance: access to grant and/or loan programs to assist with redevelopment survey, bolster community economic security through job creation, and cover environmental clean-up costs. Technical assistance, cross-partnership engagement, and financial aid can be mobilized to help communities throughout various stages of the decommissioning process, including the retirement decision, the site reclamation phase, and eventual revitalization of the site and surrounding community.

01 COAL, LIGNITE, AND PEAT↗

DOE OSTI2021

State Government Revenue Vulnerability Index and Local Government Revenue Vulnerability Index

The State Government Revenue Vulnerability Index (SGRVI) and Local Government Revenue Vulnerability Index (LGRVI) measure the vulnerability of government revenues by estimating monthly changes relative to a January 2020 baseline. Revenues included in both indices include: taxes on products and sales, transportation and housing revenues, individual income taxes, severance taxes and royalties, and property taxes. Local government revenues also include a local revenue from state revenue sharing component. In addition, the main index for the LGRVI for county-level governments includes revenue estimates for sub-county government units, including municipalities, school districts, and special districts. This revenue data is aggregated to produce the indices.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2021

Power Over Ethernet Investigation Report

This report provides insight into collaboration between Savannah River National Laboratory’s (SRNL) Global Security Directorate, Augusta University (AU) and the Georgia Cyber Center (GCC) team with regards to device analysis, investigation, and R&D efforts. Most recently, the GCC/AU team approached SRNL to receive cybersecurity expertise in a joint effort to identify differences between a potentially questionable PoE injector recovered by Columbia County and an identical one purchased by the GCC. The questionable device was installed at a fire station operated in Columbia County, shown in the figure 1-1 below.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗

DOE OSTI2021

Feasibility of Deep Direct-Use Geothermal on the West Virginia University Campus-Morgantown, WV

In 2010, research completed by the Southern Methodist University (SMU) Geothermal Laboratory estimated temperatures at reasonable drilling depths in the state of West Virginia (WV) were in the temperature range desirable for district heating. This higher temperature region extends from north-central WV (Monongalia County) to southeastern WV (Greenbrier County). The Morgantown campus of West Virginia University (WVU) is located within the north-central region, and as part of the 2016 study on Low-temperature Geothermal Play Fairway Analysis for the Appalachian Basin (GPFA-AB), Morgantown is identified as one of the priority locations for further analysis of the potential for deep direct-use (DDU) of geothermal energy. In this project, the feasibility of developing a Geothermal District Heating and Cooling (GDHC) system for the WVU campus in Morgantown, WV, to replace the current coal-fired steam heating and cooling system, is evaluated.

15 GEOTHERMAL ENERGY↗

DOE OSTI2022

Computed Tomography Scanning and Petrophysical Measurements of the Wellington KGS 2-32 Core

This report describes the use of the computed tomography facilities and the Multi-Sensor Core Logger at the National Energy Technology Laboratory in Morgantown, West Virginia to characterize core from the Wellington KGS 2-32 well (API 15-191-22770). Core from the well was obtained as part of the Small-Scale Field Test Demonstrating Carbon Dioxide Sequestration in Arbuckle Saline Aquifer and by CO 2 -Enhanced Oil Recovery at Wellington Field, Sumner County, Kansas (DE-FE0006821). The primary impetus of this work was to capture a detailed a digital representation of the core from the Wellington KGS 2-32 well (Sumner County, Kansas). The collaboration between the U.S. Department of Energy’s (DOE) NETL and the Kansas Geological Survey (KGS) at the University of Kansas enables other research entities to access information about this potential carbon storage location and formations. The resultant datasets are presented in this report and can be accessed from NETL's Energy Data eXchange (EDX) online system using the following link: https://edx.netl.doe.gov/dataset/wellington2-32-core.

47 OTHER INSTRUMENTATION↗

DOE OSTI2023

Building Stock Segmentation Cluster Development: Technical Reference Document

The building stock in the United States (U.S.) varies significantly as a function of several macro variables such as: climate, building type, vintage, and density. These variables change across the U.S. and can also significantly impact energy usage of the individual buildings and overall stock. For example, the square foot density and building type varies by several orders of magnitude from Manhattan to the eastern plains of Colorado. The diversity in energy use of the building stock of different areas of the U.S. is significant, and as a result, analyses that require localized results need to consider the relevant geography and the current makeup of the building stock. This document discusses the development and implementation of a stock clustering algorithm that produces a technically rigorous, consistent, and repeatable collection of geographies which are used as the basis for localized analysis. This framework considers the impact of built environment density, diversity, and climate in creating groupings of counties that create a far more nuanced analysis framework than national averages. Clustering of counties together represents a similarity of building characteristics and climate zone.