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DOE OSTI2025

Advancing the STS Neutron Moderator Design with an Automated Optimization Workflow and Unstructured Mesh Modeling

With the Second Target Station approaching its final design phase, a detailed neutronics evaluation of its critical components is necessary. Optimizing the dimensions of the two cold-source moderators that are at the heart of this facility presents a multi-objective optimization problem for which an accurate geometric description is crucial. We have applied a fully automated optimization workflow in which a detailed unstructured mesh geometry is automatically generated with Attila4MC, starting from a parametrized CREO geometry followed by preprocessing with SpaceClaim. With this geometry, a MCNP run is performed to calculate the brightness metrics, which are subsequently provided to the optimization algorithm in DAKOTA that provides new parameters and drives the optimization loop until convergence. In this paper, we show the results of the analysis that are used for the final design of the cylindrical and tube moderator. The optimization simulations provide a refinement to and confirmation of the conclusions of the previous design iteration. Additional to the optimization, a sensitivity study is performed to study the effect of minor geometry changes, which is important for the final engineering design. In conclusion, with these studies, we demonstrate that the automated workflow and high-fidelity unstructured mesh modeling are efficient tools for a thorough design evaluation.

DAKOTA↗

DOE OSTI2021

EGS-Collab Experiment 1: Time-lapse ERT Data and E4D Inversion Files for 10-24-2018 through 11-07-2018 Flow Test

This data submission includes the raw time-lapse ERT (electrical resistivity tomography) monitoring data, flow system data, operator logs, E4D (https://e4d.pnnl.gov) inversion files, and meta-data necessary to reproduce the 4D ERT inversion for the Oct. 24 through Nov. 7 2018 post-stimulation flow test in test bed 1. The tests were done at the Sanford Underground Research Facility at Homestake Mine in South Dakota.

15 GEOTHERMAL ENERGY↗

DOE OSTI2025

Machine learning model inputs, outputs, and scripts associated with “Artificial intelligence-guided iterations between observations and modeling significantly improve environmental predictions”

NOTE: The manuscript associated with this data package is currently in review. The data may be revised based on reviewer feedback. Upon manuscript acceptance, this data package will be updated with the final dataset and additional metadata. This data package is associated with the manuscript “Artificial intelligence-guided iterations between observations and modeling significantly improve environmental predictions” (Malhotra et al., in prep). This effort was designed following ICON (integrated, coordinated, open, and networked) principles to facilitate a model-experiment (ModEx) iteration approach, leveraging crowdsourced sampling across the contiguous United States (CONUS). New machine learning models were created every month to guide sampling locations. Data from the resulting samples were used to test and rebuild the machine learning models for the next round of sampling guidance. Associated sediment and water geochemistry and in situ sensor data can be found at https://data.ess-dive.lbl.gov/datasets/doi:10.15485/1923689, https://data.ess-dive.lbl.gov/datasets/doi:10.15485/1729719, and https://data.ess-dive.lbl.gov/datasets/doi:10.15485/1603775. This data package is associated with two GitHub repositories found at https://github.com/parallelworks/dynamic-learning-rivers and https://github.com/WHONDRS-Hub/ICON-ModEx_Open_Manuscript. In addition to this readme, this data package also includes two file-level metadata (FLMD) files that describes each file and two data dictionaries (DD) that describe all column/row headers and variable definitions. This data package consists of two main folders (1) dynamic-learning-rivers and (2) ICON-ModEx_Open_Manuscript which contain snapshots of the associated GitHub repositories. The input data, output data, and machine learning models used to guide sampling locations are within dynamic-learning-rivers. The folder is organized into five top-level directories: (1) “input_data” holds the training data for the ML models; (2) “ml_models” holds machine learning (ML) models trained on the data in “input_data”; (3) “examples” contains files for direct experimentation with the machine learning model, including scripts for setting up “hindcast” run; (4) “scripts” contains data preprocessing and postprocessing scripts and intermediate results specific to this data set that bookend the ML workflow; and (5) “output_data” holds the overall results of the ML model on that branch. Each trained ML model resides on its own branch in the repository; this means that inputs and outputs can be different branch-to-branch. There is also one hidden directory “.github/workflows”. This hidden directory contains information for how to run the ML workflow as an end-to-end automated GitHub Action but it is not needed for reusing the ML models archived here. Please see the top-level README.md in the GitHub repository for more details on the automation. The scripts and data used to create figures in the manuscript are within ICON-ModEx_Open_Manuscript. The folder is organized into four folders which contain the scripts, data, and pdf for each figure. Within the “fig-model-score-evolution” folder, there is a folder called “intermediate_branch_data” which contains some intermediate files pulled from dynamic-learning-rivers and reorganized to easily integrate into the workflows. NOTE: THIS FOLDER INCLUDES THE FILES AT THE POINT OF PAPER SUBMISSION. IT WILL BE UPDATED ONCE THE PAPER IS ACCEPTED WITH ANY REVISIONS AND WILL INCLUDE A DD/FLMD AT THAT POINT. We thank the United States Forest Service, Washington Department of Fish and Wildlife, Washington Department of Natural Resources, Cowiche Canyon Conservatory, Washington State Parks and Recreation Commission (Scientific Research Permit #210901), and the Confederated Tribes and Bands of the Yakama Nation for access to field locations where the samples labeled “SSS” were collected. We also thank the Yakama Nation Tribal Council and Yakama Nation Fisheries for working with us to facilitate sample collection and optimization of data usage according to their values and worldview. WHONDRS consortium members were asked to provide any acknowledgments for the collection of samples labeled “CM” and the following is a list of acknowledgments that were submitted with their corresponding Site IDs: (MART) Research activities were conducted in part on the Wind River Experimental Forest within the Gifford Pinchot National Forest; (MP- 100379) Philadelphia is part of Lenapehoking, the ancestral homelands of the Lenape peoples; (MP-102398) Land surveyed is the ancestral homelands of the Nookhose'iinenno (Arapaho), Tsis tsis'tas (Cheyenne), and Nuuchu (Ute); (MP-100749 and MP- 100747) Georgia Coastal Ecosystem LTER, OCE-1832178; (SP-70 and SP-72) Eastern Shoshone, Shoshone-Bannock; (MP- 102944) Funded by Oregon Watershed Enhancement Board. On the traditional lands of the Confederated Tribes of the Siletz, Confederated Tribes of the Grand Rhonde, and the Clatsop-Nehalem Confederated Tribe; (MP- 100607) Holiday Creek is located on the traditional territory of the Monacan Indian Nation; (SP-45) Lafayette Blue Springs State Park; (MP-102420) NSF DEB-2016749; (MP-100019) New Hampshire Agriculture Experiment Station; (SP-35) Rayonier (land owner; https://www.rayonier.com/); (MP- 101276) US Department of Energy, Office of Science, Biological and Environmental Research, Subsurface Biogeochemical Research, Watershed Dynamics and Evolution SFA at ORNL; (MP- 103224) Watershed Dynamics and Evolution SFA at ORNL; (MP- 101584) Traditional lands of the Oceti Sakowin (Dakota, Lakota, Nakoda) and Anishinaabe Peoples.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Tunable electrochemical pathway for high-purity REMs and CMs

This report details a comprehensive research initiative focused on developing a technical research plan for the efficient and sustainable production of rare earth elements (REEs) and critical materials (CMs) from coal-based resources, specifically lignite coal from the Williston Basin in North Dakota. The project’s methodology centers on the design and evaluation of a tunable electrochemical pathway (TEP), an innovative approach aimed at achieving high-purity rare earth oxides (REOs), rare earth salts (RES), and CMs. This research is crucial for addressing the growing demand for these materials in clean energy technologies, electric vehicles, and high-tech applications, while reducing the United States’ reliance on foreign supply chains.

01 COAL, LIGNITE, AND PEAT↗

DOE OSTI2025

Tunable Electrochemical Pathway for High-Purity REMs and CMs

01 COAL, LIGNITE, AND PEAT↗

DOE OSTI2025

Williston Basin Resource Study for Commercial-Scale Subsurface Hydrogen Storage

This closeout presentation summarizes a Department of Energy–funded study that evaluated whether large amounts of hydrogen can be safely stored underground in the North Dakota portion of the Williston Basin. The project combined lab testing, computer simulations, and basin‑wide analysis to assess saline formations, depleted oil and gas reservoirs, and salt formations for hydrogen storage capacity, recovery, and risks. Results show that underground hydrogen storage is technically feasible across multiple formation types, with depleted oil and gas reservoirs offering higher recovery and saline formations providing large long‑term storage potential. The study also identifies key challenges—such as wellbore material durability and gas purity management—and recommends pilot projects and further site‑specific studies to support future commercialization.

08 HYDROGEN↗

DOE OSTI2026

R$\&$D of Power Over Fiber in harsh environments and its novel application for the DUNE Photon Detection System

The Deep Underground Neutrino Experiment (DUNE) is a next generation long-baseline neutrino experiment that will send an intense beam of neutrinos through two detector complexes: a near detector complex located at Fermilab (Chicago), and a far detector complex located $\sim$ 1.5 km underground at Sanford Underground Research Facility (SURF) in South Dakota. One of the DUNE Far Detector (FD) modules will employ the Vertical Drift (VD) Technology, which will vertically drift the ionized electrons from the cathode plane suspended at the mid-height of the active volume of the cryostat. The Photon Detection System (PDS) will be installed along the cathode and behind the field cage to increase the photon detection coverage. Due to the high voltage ($\sim$300 kV) present at the cathode, conventional copper cables cannot be used to power the photon detectors. Therefore, Power-over-Fiber (PoF) technology will be deployed to power the PDS based on optical power transmission over optical fibers. This poster presents the R$&$D campaign on different PoF components under harsh environments and its novel application in the DUNE PDS.

Martinez caicedo, David Alejandro [South Dakota Sc↗

DOE OSTI2026

Photon Detection System Calibration for DUNE

Photon Detection System Calibration for DUNE Not scheduled 20m Conference Center (University of California, Irvine) Poster New Technologies for Neutrino Physics Poster session Speaker Denis Torres (South Dakota School of Mines and Technology) Description The Deep Underground Neutrino Experiment (DUNE) is a long baseline neutrino oscillation experiment that relies on a precise Photon Detection System (PDS) to provide accurate timing information, enhance sensitivity to low-energy and non-beam events, and support detector performance studies in liquid argon time projection chambers. Achieving these goals requires a well-understood and stable optical calibration strategy that operates reliably under cryogenic conditions. In this poster, I will present PDS calibration studies performed in ProtoDUNE, focusing on the characterization of the ultraviolet (UV) light calibration system and key optical components in the light-delivery chain. I will discuss measurements of optical fiber transmission, SMA-to-SMA connector and feedthrough interface losses, and diffuser assemblies, emphasizing wavelength dependence, attenuation, and performance under cryogenic thermal cycling and stability tests. These studies provide quantitative inputs for understanding light transport, uniformity, and long-term reliability of the PDS in large-scale liquid argon detectors, and they directly inform calibration strategies for the DUNE Far Detector.

Torres Muñoz, Denis [South Dakota Sch. Mines Tech.↗

NASA NTRS1996

The Pine Ridge-Mayo National Aeronautics and Space Administration Telemedicine Project: Program Activities and Participant Reactions

OBJECTIVE: To determine the response of participants to the Pine Ridge-Mayo National Aeronautics and Space Administration telemedicine project. DESIGN: We describe a 3-month demonstration project of medical education and clinical consultations conducted by means of satellite transmission. Postparticipation questionnaires and a postproject survey were used to assess the success of the activity. MATERIAL AND METHODS: Patients and employees at the Pine Ridge Indian Health Service Hospital in southwestern South Dakota and employees at Mayo Clinic Rochester participated in a telemedicine project, after which they completed exit surveys and a postproject questionnaire to ascertain the acceptability of this mode of health care. RESULTS: Almost all Pine Ridge and Mayo Clinic participants viewed the project as beneficial. The educational sessions received favorable evaluations, and almost two-thirds of the patients who completed evaluations thought the consultation had contributed to their medical care. More than 90% of the respondents from Pine Ridge and more than 85% of the respondents from Mayo Clinic Rochester said that they would recommend participation in this project to others. More than 90% of respondents from Pine Ridge and 80% of Mayo respondents agreed with the statement that the project should continue. CONCLUSION: These data suggest that a program of clinical consultation services, professional education, and patient education available by telemedicine might be viewed as beneficial.

United States Indian Health Service↗

DOE OSTI2021

Hydro-mechanical measurements and x-ray imaging of sheared crystalline rock fractures from EGS Collab experiments 1 & 2

Measuring hydro-mechanical properties of natural fractures is a prerequisite for optimizing hydraulic stimulation design and well placement. We completed experiments to characterize shear on natural fractures in schist, amphibolite, and rhyolite specimens drilled from EGS Collab Project’s field sites at the Sanford Underground Research Facility (SURF) in South Dakota. A triaxial direct shear method and coupled x-ray imaging were used to perform hydroshearing and mechanical shearing at the site’s in-situ stress conditions. This produced simultaneous measurements of fracture and matrix strength, permeability, stress-dependent aperture, dilation, and friction strength. Our results identified that only a subset of the natural fractures was weak enough for hydroshearing. Generally, hydroshearing increases fracture permeability by a factor of 10 or more and the enhancement is retainable over time. However, the shear slip does not always result in permeability enhancement. High content of phyllosilicates was found to associate with exceptionally weak fractures that also exhibited poor or even negative enhancement after stimulation. Combining our measurements with site data, we can predict that most observable fractures at the two EGS Collab sites do not meet the criteria for hydroshearing before tensile opening. In some cases, the visible fractures are low permeability and as strong as the adjacent rock. To induce hydroshearing before tensile opening, injection must target known weak and favorably oriented fractures with confirmed pre-existing permeability.

58 GEOSCIENCES↗

DOE OSTI2021

Risk-based area of review estimation in overpressured reservoirs to support injection well storage facility permit requirements for CO 2 storage projects

This paper by the Energy & Environmental Research Center presents a workflow and modeling approach for delineating a risk-based area of review (AOR) to support a U.S. Environmental Protection Agency (EPA) Class VI permit for a carbon dioxide (CO 2 ) storage project. The approach combines semianalytical solutions for estimating formation fluid leakage through a hypothetical leaky wellbore with the results of numerical reservoir simulations to define the AOR. The modeling utilizes 1) semianalytical solutions from the peer-reviewed literature for formation fluid leakage through abandoned wellbores by Raven (1990) and Avci (1994), 2) a FORTRAN model compiled and described in Cihan et al. (2011, 2012) called ASLMA (Analytical Solution for Leakage in Multilayered Aquifers), and 3) a computational framework for estimating a risk-based AOR first proposed by Oldenburg et al. (2014, 2016). Therefore, the approach builds upon well-established research and underlying hydrogeological principles that have been upheld for nearly three decades. Moreover, the ASLMA model has been broadly applied to an array of storage projects. The work presented herein extends these earlier works using a custom wrapper written in the software environment, R (R Core Team, 2020), which was developed to perform multiple runs of the ASLMA model using given ranges for one or more input parameters. In addition, the current work simulates the pressure buildup within the storage reservoir in response to CO 2 injection using a compositional simulator to better accommodate the temporospatial evolution of pressure buildup within the storage reservoir that is more accurately modeled using a heterogeneous geologic model and a compositional simulator that accounts for the multiphase interactions. The workflow is demonstrated using a case study for a 180,000-metric-ton-per-year storage project located in the PCOR (Plains CO 2 Reduction) Partnership region. For the storage project evaluated here, under the scenario where the leaky wellbore is open to a saline aquifer (thief zone) between the overlying seal (cap rock) and the underground sources of drinking water (USDW), the risk-based AOR essentially collapses to the areal extent of the CO 2 plume in the storage reservoir because the pressure buildup in the storage reservoir beyond the CO 2 plume is insufficient to drive formation fluids up a hypothetical leaky wellbore into the USDW. However, even under the conservative assumption that the leaky wellbore is not open to a thief zone, beyond the areal extent of the CO 2 plume, the incremental leakage is less than 400 m 3 over 20 years, which represents ~0.0001% or less of the total volume of water contained within the USDW rock volume. As discussed in the text, the threshold criterion for defining the risk-based AOR is site-specific and should be informed by the results of the sensitivity analysis and available site characterization data. The approach outlined in this paper is designed to be protective of USDWs and, therefore, comply with the Safe Drinking Water Act requirements and provisions for the U.S. EPA Class VI Underground Injection Control (UIC) Program (Class VI Rule) and North Dakota Administrative Code Chapter 43-05-01.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Machine Learning Analysis of Impact of Western US Fires on Central US Hailstorms

Fires, including wildfires, harm air quality and essential public services like transportation, communication, and utilities. These fires can also influence atmospheric conditions, including temperature and aerosols, potentially affecting severe convective storms. Here, we investigate the remote impacts of fires in the western United States (WUS) on the occurrence of large hail (size: $\geqslant$ 2.54 cm) in the central US (CUS) over the 20-year period of 2001–20 using the machine learning (ML), Random Forest (RF), and Extreme Gradient Boosting (XGB) methods. The developed RF and XGB models demonstrate high accuracy (> 90%) and F1 scores of up to 0.78 in predicting large hail occurrences when WUS fires and CUS hailstorms coincide, particularly in four states (Wyoming, South Dakota, Nebraska, and Kansas). The key contributing variables identified from both ML models include the meteorological variables in the fire region (temperature and moisture), the westerly wind over the plume transport path, and the fire features (i.e., the maximum fire power and burned area). Importantly, the results confirm a linkage between WUS fires and severe weather in the CUS, corroborating the findings of our previous modeling study conducted on case simulations with a detailed physics model.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Conceptual core design of the molten metal fueled microreactor with self-regulating capability

The microreactor concept has received significant attention in the United States for its lower capital investment, siting flexibility and high mobility. A microreactor aims to provide reliable electricity in remote communities, mining areas, or isolated islands, which will save the high fuel transportation cost. Thus, minimizing the staff level for operating and maintaining a microreactor is desirable. The objective of the study is to design a self-regulated Molten Metal fueled microReactor core (MMR) and to demonstrate the self-regulating capability of the MMR core. The control of the reactor is solely relying on reactivity feedbacks from fuel temperature perturbation. Molten UMn (liquid form at the temperature over 720°C) was utilized as the fuel material in MMR due to the larger thermal expansion coefficient compared to the conventional solid fuel. The MMR core has a fast neutron spectrum, which corresponds to a small burnup reactivity swing. The core design parameters were selected based on DAKOTA, REBUS, and ANLHTP calculations. The MMR is designed to run for 10 years at a power level of 15 MWth. Furthermore, no refueling is needed during the reactor lifetime. The system dynamic analyses were performed, and it was found that the reactor can be self-regulating within a temperature range of 800 ± 80°C for a loss of heat sink transient.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2020

Measurement of the gamma ray background in the Davis cavern at the Sanford Underground Research Facility

Deep underground environments are ideal for low background searches due to the attenuation of cosmic rays by passage through the earth. However, they are affected by backgrounds from γ -rays emitted by 40 K and the 238 U and 232 Th decay chains in the surrounding rock. The LUX-ZEPLIN (LZ) experiment will search for dark matter particle interactions with a liquid xenon TPC located within the Davis campus at the Sanford Underground Research Facility, Lead, South Dakota, at the 4850-foot level. In order to characterise the cavern background, in-situ γ -ray measurements were taken with a sodium iodide detector in various locations and with lead shielding. The integral count rates (0–3300 keV) varied from 596 Hz to 1355 Hz for unshielded measurements, corresponding to a total flux from the cavern walls of 1.9 ± 0.4 γ cm −2 s −1 . The resulting activity in the walls of the cavern can be characterised as 220 ± 60 Bq/kg of 40 K, 29 ± 15 Bq/kg of 238 U, and 13 ± 3 Bq/kg of 232 Th.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2020

Multilevel and multifidelity uncertainty quantification for cardiovascular hemodynamics

Standard approaches for uncertainty quantification in cardiovascular modeling pose challenges due to the large number of uncertain inputs and the significant computational cost of realistic three-dimensional simulations. Here, we propose an efficient uncertainty quantification framework utilizing a multilevel multifidelity Monte Carlo (MLMF) estimator to improve the accuracy of hemodynamic quantities of interest while maintaining reasonable computational cost. This is achieved by leveraging three cardiovascular model fidelities, each with varying spatial resolution to rigorously quantify the variability in hemodynamic outputs. We employ two low-fidelity models (zero- and one-dimensional) to construct several different estimators. Our goal is to investigate and compare the efficiency of estimators built from combinations of these two low-fidelity model alternatives and our high-fidelity three-dimensional models. We demonstrate this framework on healthy and diseased models of aortic and coronary anatomy, including uncertainties in material property and boundary condition parameters. Our goal is to demonstrate that for this application it is possible to accelerate the convergence of the estimators by utilizing a MLMF paradigm. Therefore, we compare our approach to single fidelity Monte Carlo estimators and to a multilevel Monte Carlo approach based only on three-dimensional simulations, but leveraging multiple spatial resolutions. We demonstrate significant, on the order of 10 to 100 times, reduction in total computational cost with the MLMF estimators. We also examine the differing properties of the MLMF estimators in healthy versus diseased models, as well as global versus local quantities of interest. As expected, global quantities such as outlet pressure and flow show larger reductions than local quantities, such as those relating to wall shear stress, as the latter rely more heavily on the highest fidelity model evaluations. Similarly, healthy models show larger reductions than diseased models. In all cases, our workflow coupling Dakota’s MLMF estimators with the SimVascular cardiovascular modeling framework makes uncertainty quantification feasible for constrained computational budgets.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2020

Characterization of paste microstructure for durability properties of concrete

In this study, paste samples from different cementitious systems were characterized by using X-Ray Micro Computed Tomography (µ-CT) and Fluorescence Microscopy techniques. Quantitative and qualitative aspects of defects from paste samples were investigated to develop indicators of durability related properties of corresponding concrete samples. In the first phase of this study, hour glass shaped 3D printed molds were used to investigate five cementitious combinations consisting of three cement types (Coarse Ground portland cement, Type IL Portland Limestone Cement and Type I/II ordinary portland cement) and fly ash as a supplementary cementitious material. In the second phase, metal molds were used to compare the results of Coarse Ground portland cement with Type I ordinary portland cement, 5% of which was replaced with colloidal nanosilica. Potential correlations between paste and concrete have been presented. This work was supported by US Department of Energy and North Dakota Department of Commerce.

36 MATERIALS SCIENCE↗

DOE OSTI2020

A data-driven model for thermodynamic properties of a steam generator under cycling operation

The varying electricity demand from coal power plants due to the intermittent nature of renewable sources leads to load-follow and on/off operations referred to as cycling. Cycling causes transients of properties such as pressure and temperature within various components of the steam generation system.These transients cause increased damage because of fatigue and creep-fatigue interactions shortening the life of components. An algorithm is developed to identify cycling operations from the gross power data. The data-driven model based on artificial neural networks (ANN) is developed using 10 years data from Coal Creek Station power plant located in North Dakota, USA to estimate properties of the steam generator components during cycling operations. Furthermore, the uniqueness of this model is the ability to predict component properties for the cycling as well as base-load operations and is reported for the first time. The ANN model estimates the component properties, for a given gross power profile and initial conditions, as they vary during cycling operations. As a representative example, the ANN estimates are presented for the superheater outlet pressure, reheater inlet temperature, and flue gas temperature at the air heater inlet. The changes in these variables as a function of the gross power over the time duration are compared with measurements to assess the predictive capability of the model. Mean square errors of 4.49E-04 for superheater outlet pressure, 1.62E-03 for reheater inlet temperature, and 4.14E-04 for flue gas temperature at the air heater inlet were observed.

01 COAL, LIGNITE, AND PEAT↗

DOE OSTI2024

Monitoring spatiotemporal evolution of fractures during hydraulic stimulations at the first EGS collab testbed using anisotropic elastic-waveform inversion

The EGS Collab project acquired continuous active-source seismic monitoring (CASSM) data before, during, and after hydraulic stimulations at the first testbed at the depth of 4850 ft (1478 m) at the Sanford Underground Research Facility in Lead, South Dakota, for monitoring fracture creation and evolution. CASSM acquisition was conducted using 24 hydrophones, 18 accelerometers, and 17 piezoelectric sources within four fracture-parallel wells and two orthogonal wells. 3D anisotropic traveltime tomography and anisotropic elastic-waveform inversion of the campaign cross-borehole seismic data show that the rock within the stimulation region is a heterogeneous horizontal transverse isotropic medium. Here we use these inversion results as the initial models and apply 3D anisotropic first-arrival traveltime tomography and 3D anisotropic elastic-waveform inversion to the CASSM data acquired after each stimulation in May, 2018 and December, 2018. We observe the spatiotemporal evolution of seismic velocities and anisotropic parameters caused by hydraulic fracture stimulations, showing the regions of rock alternation caused by hydraulic fracture stimulation.

15 GEOTHERMAL ENERGY↗