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All sourcesNASA NTRSDOE OSTI
NASA NTRS2023

Launch Complex 34, SWMU CCO542022 DNAPL Source Zone Operations, Maintenance, and Monitoring, Site-Wide Long-Term Monitoring, and Hot Spot 6 Air Sparge System Annual Performance Monitoring Report Cape Canaveral Space Force Station, Florida

This Annual Performance Monitoring Report (PMR) for the Dense Non-Aqueous Phase Liquid (DNAPL) Source Zone (DSZ), Site-Wide Long-Term Monitoring (LTM), and Hot Spot (HS) 6 Air Sparge (AS) System presents the results of Year 13 operation of the hydraulic containment (HC) Interim Measure (IM), the results of performance monitoring direct-push technology (DPT) sampling and monitoring well sampling conducted in the DSZ, results of the biennial site-wide LTM event, and the results of operations and performance sampling of the HS 6 AS IM at Launch Complex 34 (LC34), located at Cape Canaveral Space Force Station (CCSFS), Florida. This site has been designated Solid Waste Management Unit (SWMU) CC054 under the Kennedy Space Center (KSC) Resource Conservation and Recovery Act (RCRA) Corrective Action Program. For the site-wide biennial LTM event, a total of 55 monitoring wells were sampled for volatile organic compounds (VOCs) in February 2023 and one well was sampled for polychlorinated biphenyls (PCBs) in December 2022. One well planned for VOC sampling was found to be destroyed and could not be sampled (CW0002). The LTM wells are screened in two lithologic zones: Layer 1 (0 to 25 feet below land surface [bls]) and Layer 2 (25 to 30 ft bls), and are located in the outlying areas of LC34 to monitor groundwater conditions within the Low-Concentration Plume (LCP), defined as concentrations exceeding Groundwater Cleanup Target Levels (GCTLs), and the High-Concentration Plume (HCP), defined as concentrations exceeding Natural Attenuation Default Concentrations (NADCs). Results from the biennial sampling event indicated overall plume stability and delineation for the plume, which extends over 300 acres. The operational period for Year 13 of the HCS was from April 1, 2022 to March 31, 2023. Operational runtime for the system was 90 percent during Year 13, with downtime events attributed to planned maintenance, system repairs, and power outages. As of March 31, 2023, a total of 313,673,241 cumulative gallons of groundwater containing 88,337 pounds of VOCs have been removed by the HCS. Influent concentrations of trichloroethene (TCE) have decreased since startup from approximately 280,000 μg/L (January 2010) to 12,000 μg/L (March 2023). During the reporting period, all effluent concentrations from the HCS (aqueous and vapor) were below regulatory reporting limits, indicating the system continues to operate as intended. Performance monitoring was conducted in December 2022 within the DSZ to evaluate TCE contamination. Groundwater samples were collected via DPT at nine locations, consistent with previous events between 2017 and 2021. Full vertical profile sampling was completed at each DPT from 8 to 98 feet bls, at 5-foot intervals. The DPT performance monitoring results are summarized in this PMR. The results revealed TCE remains at concentrations greater than 11,000 μg/L in the DSZ (1-percent solubility, indicative of DNAPL) at eight of the nine DPT locations and at depths ranging from 8 to 98 feet bls. In addition to DPT sampling, groundwater samples were collected from deep monitoring wells in the DSZ area (Layers 7 and 8) in December 2022 to verify vertical delineation. Layer 7/8 monitoring well results were non-detect in December 2022, with exception of three wells (IW0162, IW043D2, and IW044D2), where TCE, cis-1,2-dichloroethene (cDCE), and/or vinyl chloride (VC) were detected above GCTLs. These wells are screened 105 to 115 feet bls, which is below the existing recovery well capture zone. TCE was first detected in IW0162 in December 2021 and has since been sampled at least monthly to monitor TCE concentrations. The maximum TCE concentration during this operational period was 15,000 μg/L at IW0162 in March 2023. Because of the increased TCE concentrations in this well, a new recovery well (RW21D), screened 86 to 106 feet bls, was installed in January 2023 and incorporated into existing HCS operations. The HS 6 AS IM was initiated in 2018 with 160 AS wells, and expanded in 2019 with an additional 140 AS wells. Quarterly performance monitoring was reduced to semi-annual in 2020. The HS 6 AS system remained operational during the reporting period covered under this report. The results of the HS 6 system operation and semi-annual performance monitoring are summarized in this report. Semi-annual monitoring results collected in April and October 2022 show concentrations of contaminants of concern (cDCE, trans-1,2-dichloroethene, and vinyl chloride) are generally decreasing and not impacting the surface water drainage canal, indicating the HS 6 IM is meeting objectives. A Phase Two Expansion of the HS 6 AS IM was recently completed. As of the date of this report, the expansion became operational in August 2023 and the first quarter of monitoring was conducted in November 2023. Details of the construction, start-up, and performance monitoring will be included in a future Annual PMR. Overall, the tasks associated with Year 13 operation of the HC IM, operation of the HS 6 AS IM, and biennial site-wide sampling were performed in accordance with recommendations included in the previous 2021 LC34 (Year 12) annual report. Evaluation of results from the HC IM and HS 6 IM show that these systems are operating as designed and meeting performance objectives. Results from the site-wide biennial LTM program also show that the overall network of monitoring wells is adequate to continue monitoring plume-wide conditions.

Complex 34↗
NASA NTRS1999

Work on Planetary Atmospheres and Planetary Atmosphere Probes

A summary final report of work accomplished is presented. Work was performed in the following areas: (1) Galileo Probe science analysis, (2) Galileo probe Atmosphere Structure Instrument, (3) Mars Pathfinder Atmosphere Structure/Meteorology instrument, (4) Mars Pathfinder data analysis, (5) Science Definition for future Mars missions, (6) Viking Lander data analysis, (7) winds in Mars atmosphere Venus atmospheric dynamics, (8) Pioneer Venus Probe data analysis, (9) Pioneer Venus anomaly analysis, (10) Discovery Venus Probe Titan probe instrument design, and (11) laboratory studies of Titan probe impact phenomena. The work has resulted in more than 10 articles published in archive journals, 2 encyclopedia articles, and many working papers. This final report is organized around the four planets on which there was activity, Jupiter, Mars, Venus, and Titan, with a closing section on Miscellaneous Activities. A major objective was to complete the fabrication, test, and evaluation of the atmosphere structure experiment on the Galileo probe, and to receive, analyze and interpret data received from the spacecraft. The instrument was launched on April 14, 1989. Calibration data were taken for all experiment sensors. The data were analyzed, fitted with algorithms, and summarized in a calibration report for use in analyzing and interpreting data returned from Jupiter's atmosphere. The sensors included were the primary science pressure, temperature and acceleration sensors, and the supporting engineering temperature sensors. Computer programs were written to decode the Experiment Data Record and convert the digital numbers to physical quantities, i.e., temperatures, pressures, and accelerations. The project office agreed to obtain telemetry of checkout data from the probe. Work to extend programs written for use on the Pioneer Venus project included: (1) massive heat shield ablation leading to important mass loss during entry; and (2) rapid planet rotation, which introduced terms of motion not needed on Venus. When the Galileo Probe encountered Jupiter, analysis and interpretation of data commenced. The early contributions of the experiment were to define (1) the basic structure of the deep atmosphere, (2) the stability of the atmosphere, (3) the upper atmospheric profiles of density, pressure, and temperature. The next major task in the Galileo Probe project was to refine, verify and extend the analysis of the data. It was the verified, and corrected data, which indicated a dry abiabatic atmosphere within measurement accuracy. Temperature in the thermosphere was measured at 900 K. Participation in the Mars atmospheric research included: (1) work as a team member of the Mars Atmosphere Working Group, (2) contribution to the Mars Exobiology Instrument workshop, (3) asssistance in planning the Mars global network and (4) assitance in planning the Soviet-French Mars mission in 1994. This included a return to the Viking Lander parachute data to refine and improve the definition of winds between 1.5 and 4 kilometer altitude at the two entry sites. The variability of the structure of Mars atmosphere was addressed, which is known to vary with season, latitude, hemisphere and dust loading of the atmosphere. This led to work on the Pathfinder project. The probe had a deployable meteorology mast that had three temperature sensors, and a wind sensor at the tip of the mast. Work on the Titan atmospheric probe was also accomplished. This included developing an experiment proposal to the European Space Agency (ESA), which was not selected. However, as an advisor in the design and preparation of the selected experiment the researcher interacted with scientist on the Huygens Probe Atmosphere Structure Experiment. The researcher also participated in the planning for the Venus Chemical Probe. The science objectives of the probe were to resolve unanswered questions concerning the minor species chemistry of Venus' atmosphere that control cloud formation, greenhouse effectiveness, and the thermal structure. The researcher also reviewed problems with the Pioneer Venus Probe, that caused anomalies which occurred on the Probes at and below 12.5 km level of the Venus' atmosphere. He convened and participated in a workshop that concluded the most likely hardware cause was insulation failure in the electrical harness outside the Probes' pressure vessels. It was discovered that the shrink tubing material failed at 600K. This failure could explain the anomalies experienced by the probes. The descent data of the Pioneer probes, and the Soviet Vega Lander was analyzed to evaluate the presence of small scale gravity waves in and below the Venus cloud layer.

Lester, Peter↗
DOE OSTI2024

GeoThermalCloud: Cloud Fusion of Big Data and Multi-Physics Models using Machine Learning for Discovery, Exploration, and Development of Hidden Geothermal Resources

The primary goals of this project are exploring hidden geothermal resources in the U.S.A. and designing profitable enhanced geothermal systems (EGS). Many processes and parameters control geothermal exploration and energy production from geothermal fields. Diverse datasets (e.g., geology, geochemistry, geophysics, satellite, airborne geophysics) are available to help characterize subsurface geothermal conditions. Sparse and multi-scale characteristics of these datasets prohibit properly leveraging these datasets for geothermal exploration and profitable EGS design. Recent advancements in machine learning (ML) promise to resolve these issues. The tremendous challenges and risks of geothermal exploration and production bring the demand for novel ML methods and tools that can (1) analyze large field datasets, (2) assimilate model simulations (large inputs and outputs), (3) process sparse datasets, (4) perform transfer learning (between sites with different exploratory levels), (5) extract hidden geothermal signatures in the field and simulation data, (6) label geothermal resources and processes, (7) identify high-value data acquisition targets, and (8) guide geothermal exploration and production by selecting optimal exploration, production, and drilling strategies. To address these necessities, ML-based geothermal resources exploration and enhanced geothermal systems (EGS) design tools have been developed. The exploration tool is called GeoThermalCloud and EGS design tool is called GeoDT-ML. GeoThermalCloud (https://github.com/SmartTensors/GeoThermalCloud.jl) utilizes a LANL unsupervised ML platform called SmartTensors (https://tensors.lanl.gov/) to automate data analyses and interpretations by extracting hidden signatures to identify geothermal prospects. Also, it enables the identification of critical measurements needed to identify geothermal resource signatures. Alternatively, GeoDT-ML (https://github.com/SmartTensors/GeoThermalCloud.jl/tree/master/EGS) is an ML-based alternative to GeoDT (https://github.com/GeoDesignTool/GeoDT.git), a fast, simplified multi-physics solver to evaluate EGS project designs in uncertain geologic systems. GeoDT-ML leverages recent advances in deep learning and high-performance computing. It is a faster and simpler version of GeoDT. To make this project a success, we used capabilities of LANL, PNNL, Google, Stanford, and Julia Computing. We analyzed eight datasets of the U.S.A. using GeothermalCloud and demonstrated potential highly prospective geothermal resources and identified key factors defining highly prospective sites. The first data set includes 44 locations in southwest New Mexico and 18 geological, hydrogeological, geophysical, geothermal, geochemical attributes. We defined low- and medium-temperature hydrothermal systems and discovered a new highly prospective site. The second data set analyzed 18 shallow water chemistry attributes at 14,342 locations in the Great Basin. It demarcated modestly, moderately, and highly prospective sites including key attributes for each type of prospectivity. The third data set analyzed Utah FORGE data including satellite (InSAR), geophysical (gravity, seismic), geochemical, and geothermal attributes. Here, we performed prospectivity analysis to identify future drilling locations using geological, geochemical, and geophysical attributes. Maps of temperature at depth and heat flow are constructed based on the available data. Prospectivity maps were generated, and drilling locations were proposed for future geothermal field exploration. The fourth data set analyzed 21 attributes at 120 locations in Tularosa Basin, New Mexico; data comes from past play fairway analyses in this region. ML analyses identified geothermal signatures associated with modestly, moderately, and highly hydrothermal systems. We also defined dominant attributes and spatial distribution of the geothermal signatures. The fifth, sixth, seventh, and eighth datasets include Tohatchi Springs, New Mexico, Hawaii, Brady site, Nevada, and EGS Collab, respectively. Moreover, we coupled GeothermalCloud and magnetotellurics data to pinpoint drilling locations for developing geothermal projects in the Tularosa Basin, New Mexico. GeothermalCloud found potential prospective locations for geothermal resources near White Sands Missile Range and McGregor Range at Fort Bliss. Magnetotellurics data determined the potential depth (~1800m) of geothermal prospects at McGregor Range based on apparent resistivity structures/layers in the subsurface. The McGregor Range consists of three resistivity layers and two resistivity structures. Magnetotellurics data also helps identify that the western portion of the McGregor Range has thick and low-resistivity earth materials. The low resistivity to the west is most likely for a fault system. Assuming temperature is consistent with a geothermal reservoir, the west-central part of the McGregor Range has the highest geothermal potential because of the increase in porosity and associated permeability attributed to the interpreted fault system. Also, we devised a coupling strategy between a process model and GeothermalCloud to characterize hydrogeological conditions and geothermal conditions, respectively. The process model characterizes hydrogeological and geothermal conditions on highly prospective geothermal sites provided by GeothermalCloud. We developed a physics-informed neural network (PINN) version of the Burns equation that can be easily coupled with GeothermalCloud. Furthermore, we performed an optimal design decision maximizing the economic value of an EGS power plant. This study optimized the range of well spacing between injection and production wells maximizing net present value in dollars (NPV). For this task, we used the GeoDT to simulate the Utah FORGE EGS development cycle from the initial well design to the end of production. Next, we accomplished another crucial task, which is predicting permeability of geothermal reservoirs. Predicting permeability of geothermal reservoirs is a non-trivial task because of huge computational runtime of simulation and lack of measurements. To avoid these limitations, we used easy-to-measure chemical concentrations in the subsurface as measurement data and convolutional neural network based ML model of a high-fidelity model. Next, we predicted permeability using Markov chain Monte Carlo simulation. We found that Markov chain Monte Carlo simulation predicts permeability with a high certainty if the prediction zone in the simulation area has chemical concentration data. Finally, we analyzed the DOE funded INGENIOUS and GeoDAWN projects data. For discovering hidden geothermal systems in the Great Basin, the INGENIOUS project accumulated old data, collected new data, and released them in 2022. The dataset includes a total of 24 geological, geophysical, and geochemical attributes. Data resolution and scale significantly vary prohibiting an appropriate usage. To avoid such limitations, we brought all data in the same resolution and scale by applying the inverse distance weighting interpolation technique for predicting data in unsampled locations. Subsequently, we analyzed LiDAR data of the GeoDAWN project. We received data in tiles format. The DOE’s overarching goal is to use ML on LiDAR data for finding favorable geological structures (e.g., step up faults in Brady, Nevada). To serve the purpose, we need to label favorable geologic structures that correspond to LiDAR data. We wrote an algorithm to label the LiDAR data with the favorable geologic structures.

15 GEOTHERMAL ENERGY↗