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DOE OSTI2021

Analysis and Integration of the Hydraulic Fracturing Test Site-2 (HFTS-2) Comprehensive Dataset

Hydraulic Fracturing Test Site-2 (HFTS-2) is a field-based research experiment performed in the Permian (Delaware) Basin. The unique aspect of this program was the acquisition of a unique, comprehensive, diagnostic dataset. Additionally, shorter parent wells drilled three years before the child wells offered clear distinction between the stages influenced by parent-child effects and the stages without any effects. The goal of this study was to analyze and integrate this comprehensive diagnostic dataset to understand the areal and vertical extent of hydraulic fractures (HF). The paper also provides insights on the effects of parent wells’ depletion on child well HF geometry based on various monitoring methods and subsurface models. Areal and vertical coverage for all HFTS-2 wells during stimulation and depletion was estimated based on analysis and interpretation of diagnostics and advanced modeling results. HFTS-2 diagnostics included microseismic (MS), pre- and post-stimulation logs and cores, bottomhole gauges, and fiber optic (FO) data. The diagnostics results (MS, FO, image logs) were integrated and used to calibrate subsurface models. Additional field tests were designed and implemented for depletion monitoring. The tailored program for monitoring depletion included vertical and slant well pressures, interference testing, and a vertical strain depletion trial. Areal Coverage: Conventional MS (and FO MS) were used to compute HF dimensions, which were compared with diagnostics (FO strain, gauge, image logs) observations and calibrated subsurface models. A post-production interference test did not show offset well communication. Vertical Coverage: Vertical coverage during stimulation was monitored using a vertical monitoring well. The stronger mechanical strain signals showed good correlation with MS event intensities, geomechanical properties, and gauge inferences. Vertical depletion was estimated based on vertical/slant well gauges and strain depletion tests. Parent-Child Effects: Diagnostics and calibrated subsurface models show asymmetry in child well fracture geometries for stages that overlap parent wells. Child well image logs serve as a good indicator for parent Downloaded from http://onepetro.org/URTECONF/proceedings-pdf/21URTC/2-21URTC/D021S031R004/2477423/urtec-2021-5241-ms.pdf/1 by Carol Worster on 28 February 2022 URTeC 5241 well HF tracking. Child well MS events had an eastward bias, in line with pre-stimulation image logs, and was confirmed by parent well frac hits. Novel/Additive Information: The dataset presents a unique, over-constrained problem space to compare independent techniques to arrive at HF metrics (i.e., stimulation height and/or half-length), unlike a single source dataset, in which calibration is done using available data to guide predictions. Here, the asymmetry in HF geometry seen in the stages influenced by parent-child effects offers unique insights into well spacing and landing, which are key capital decisions the unconventional resources industry is seeking to optimize.

58 GEOSCIENCES↗

DOE OSTI2026

Integration of the Biot–Gassmann Fluid Substitution Method and Machine Learning-Based Velocity–Stress Relationship for Estimating In Situ Stresses

Recent advancements have shown that in situ stresses can be reliably estimated through an integrated machine/deep learning (ML/DL)-based framework, which relies on models trained and validated using true triaxial ultrasonic velocity (TUV) experimental data that involve measurements of ultrasonic velocity in saturated rocks under varying stress configurations. However, when the goal is to interpret lower frequency measurements, it may be more appropriate to run experiments on dry rocks and then obtain Biot–Gassmann-derived equivalent saturated velocities (low-frequency approximation) and employ these quantities for training ML/DL models to predict in situ stress. Whether the dispersion effect of frequency on the velocity–stress relationship substantially impacts in situ stress prediction is an important and unresolved question. This work presents an enhancement of ML/DL-based workflow by training and implementing ML/DL models using equivalent saturated acoustic velocities (low-frequency) obtained by applying Biot–Gassmann fluid substitution on the ultrasonic velocities of dry cores. The models were trained on TUV data sets derived from three subsurface cores extracted from the geothermal well 16B(78)-32 at the Utah FORGE site. Each core was subjected to 75 unique stress configurations for velocity measurement in the dry state. The ML/DL trained on the TUV data set with equivalent saturated velocities demonstrated promising performance to predict in situ stress in subsurface geological rocks using velocity–stress relationships with R 2 of 0.86, 0.971, and 0.975 and root mean squared error (RMSE) of 2.59, 1.92, and 1.80 for validation/testing phases of vertical, minimum horizontal, and maximum horizontal stress models, respectively. Additionally, interpretation and explanation by Shapley additive explanations (SHAP) analysis further improved scientific validation and model reliability for estimating in situ stresses.

colloids↗

DOE OSTI2021

Influence of Agricultural Managed Aquifer Recharge (AgMAR) and Stratigraphic Heterogeneities on Nitrate Reduction in the Deep Subsurface

This data package contains input files for TOUGHREACT for a modeling study examining the effects of managed aquifer recharge on agricultural lands on nitrate cycling and transport in the Central Valley of CA near Modesto. The files contain all the geochemical species, reactions, and hydrological parameters for the model. The files are text files used for the TOUGH family of code created by LBNL. To use the files a license is required. https://tough.lbl.gov/licensing-download/toughreact-licensing-download/Accompanying Paper Abstract: Agricultural managed aquifer recharge (AgMAR) is a proposed management strategy whereby surface water flows are used to intentionally flood croplands with the purpose of recharging underlying aquifers. However, legacy nitrate (NO3-) contamination in agriculturally-intensive regions poses a threat to groundwater resources under AgMAR. To address these concerns, we use a reactive transport modeling framework to better understand the effects of AgMAR management strategies (i.e., by varying the frequency, duration between flooding events, and amount of water) on N leaching to groundwater under different stratigraphic configurations and antecedent moisture conditions. In particular, we examine the potential of denitrification and nitrogen retention in deep vadose zone sediments (~15 m) using variable AgMAR application rates on two-dimensional representations of differently textured soils, soils with discontinuous bands/channels, and soils with preferential flow paths characteristic of typical agricultural field sites. Our results indicate that finer textured sediments, such as silt loams, alone or embedded within high flow regions, are important reducing zones providing conditions needed for denitrification. Simulation results further suggest that applying water all-at-once rather than in increments for a fixed volume of recharge transports higher concentrations of NO3- deeper into the profile, which has the potential to exacerbate groundwater quality. This transport into deeper depths can be aggravated by wetter antecedent soil moisture conditions. However, applying water all-at-once also increases denitrification within the vadose zone by promoting anoxic conditions. We conclude that AgMAR management strategies can be designed to enhance denitrification in the subsurface and reduce N leaching to groundwater, while specifically accounting for lithologic heterogeneity, antecedent soil moisture conditions, and depth to the water table. Our findings are potentially relevant to other systems that experience flooding inundation such as riparian corridors, floodplains, wetlands, and other managed landscapes like dedicated recharge basins.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Continental Scale Hydrostratigraphy: Basin‐Scale Testing of Alternative Data‐Driven Approaches

Abstract Integrated hydrological modeling is an effective method for understanding interactions between parts of the hydrologic cycle, quantifying water resources, and furthering knowledge of hydrologic processes. However, these models are dependent on robust and accurate datasets that physically represent spatial characteristics as model inputs. This study evaluates multiple data‐driven approaches for estimating hydraulic conductivity and subsurface properties at the continental‐scale, constructed from existing subsurface dataset components. Each subsurface configuration represents upper (unconfined) hydrogeology, lower (confined) hydrogeology, and the presence of a vertical flow barrier. Configurations are tested in two large‐scale U.S. watersheds using an integrated model. Model results are compared to observed streamflow and steady state water table depth (WTD). We provide model results for a range of configurations and show that both WTD and surface water partitioning are important indicators of performance. We also show that geology data source, total subsurface depth, anisotropy, and inclusion of a vertical flow barrier are the most important considerations for subsurface configurations. While a range of configurations proved viable, we provide a recommended Selected National Configuration 1 km resolution subsurface dataset for use in distributed large‐and continental‐scale hydrologic modeling.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Selecting Appropriate Model Complexity: An Example of Tracer Inversion for Thermal Prediction in Enhanced Geothermal Systems

Abstract A major challenge in the inversion of subsurface parameters is the ill‐posedness issue caused by the inherent subsurface complexities and the generally spatially sparse data. Appropriate simplifications of inversion models are thus necessary to make the inversion process tractable and meanwhile preserve the predictive ability of the inversion results. In this study, we investigate the effect of model complexity on fracture aperture inversion and thermal performance prediction in a field‐scale EGS model. Principal component analysis was used to map the aperture field to a low‐dimensional latent space. The complexity of the inversion model was quantitatively represented by the percentage of total variance in the original aperture fields preserved by the latent space. Tracer, pressure and flow rate data were used to invert for fracture aperture through an ensemble‐based inversion method, and the inferred aperture field was used to predict thermal performance. With an over‐simplified aperture model, ensemble collapse occurred. The inverted aperture models failed to resolve necessary flow and transport features, leading to a biased thermal performance prediction. A complex aperture model involved excessive features and was prone to overinterpreting the inversion data. Both the tracer/pressure/flow rate data reproduction and thermal prediction showed significant uncertainties, making it difficult to properly estimate long‐term thermal performance. Fortunately, our results indicate that there exists an appropriate model complexity which can simultaneously match inversion data and predict thermal performance with an acceptable uncertainty. The quality of the fit of tracer data appears to be a useful indicator of such an appropriate model complexity.

15 GEOTHERMAL ENERGY↗

DOE OSTI2021

Coupling Surface Flow with High-performance Subsurface Reactive Flow and Transport Code PFLOTRAN

Water exchange between the surface and subsurface is important for both water resource management and environmental protection. In this paper, we develop coupled surface and subsurface flow simulation capability in a parallel subsurface flow and reactive transport code PFLOTRAN. We sequentially couple the diffusion wave-based surface flow with the subsurface flow governedby the Richards equation in PFLOTRAN. These two flow domains are linked with a boundary condition switching method that ensures continuity of pressure and flux at the surface-subsurface interface. We verify the coupled code against other existing hydrologic models and observation data using a number of numerical experiments. The coupled hydrological model exhibits good performance in strong parallel scaling tests. The new coupled surface and subsurface simulator significantly advance community simulation capability towards improving integrated hydrologic and biogeochemical understanding of complex systems such as watersheds and river corridors. Keywords: Surface flow, Integrated hydrological modeling, Boundary condition switching, Parallel computing

Wu, Runjian↗

DOE OSTI2021

Interferometric SAR modelling of near surface data to improve geological model in the Surat Basin, Australia

Here we present a study on geophysical inverse modelling for subsurface structural properties of an unconventional hydrocarbon site that was monitored previously by Interferometric Synthetic Aperture Radar (InSAR) technology for surface deformation. A static three-dimensional geomodel along with extracted property maps replicates the depth of each underlying stratigraphic unit and structural feature with the density of each geological layer. We examine the hypothesis that integration of elastic properties of each formation layer with InSAR observations in a stratified elastic medium will lead to a viscoelastic geophysical inverse problem that can be solved to estimate fractional volume change at the reservoir level. Moreover, we examine synthetic scenarios in which the elastic properties of the formations are perturbed before determining the resulting impact on the rate of surface deformation. The results show that although the slope of underlying formations, their density and depth can define the extent and pattern of a deformation signal, their properties have a marginal impact on volumetric change compared to the dense network of shallow depth Coal Seam Gas (CSG) mining wells. Besides, it is also demonstrated that the inversion of InSAR deformation maps can resolve the uncertainties associated with low-resolution seismic interpretation as well as filling the data gaps within seismic acquisitions. A significant contribution of this investigation to the geological basin modelling involves a) introducing a remote and non-invasive technology such as InSAR to improve geophysical mapping of subsurface structures such as faults in areas with sparse or no reflective seismic information, and b) applying a multi-layer viscoelastic geophysical source model for an unconventional hydrocarbon reservoir such as CSG.

58 GEOSCIENCES↗

DOE OSTI2021

Intelligent Monitoring Systems and Advanced Well Integrity and Mitigation

Long-term seismic monitoring of carbon capture and storage projects is needed to verify that the injected gas is safely stored in the subsurface until permanence can be assured. Conventional surface seismic monitoring techniques are usually expensive, require highly invasive surface operations, and need significant time investments on the part of personnel for both the field effort and processing the acquired data. For these reasons, permanent reservoir monitoring technologies are preferred, as they can offer a cost-effective solution for long-term monitoring. As part of the monitoring program of the Archer Daniels Midland’s large-scale injection of CO 2 in Decatur, Illinois, USA, a continuous seismic monitoring array was installed using a combination of surface orbital vibrator (SOV) sources and fiber-optic cables for distributed acoustic sensing (DAS) acquisition with the objective to build a continuous monitoring array. The aim of the presented project was to build a monitoring array and platform that integrates real-time seismic data with conventional data streams and provides continuous data analysis using dynamic computational models to deliver a comprehensive real-time assessment of subsurface conditions. It is in this context that the Intelligent Monitoring Systems and Advanced Well Integrity and Mitigation project was proposed with the objective to develop an integrated architecture that utilizes a permanent seismic monitoring network, combines the real-time geophysical and process data with reservoir flow and geomechanical models to create a comprehensive monitoring, visualization, and control system that delivers critical information for process surveillance and optimization.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Ambient field seismology in critical zone hydrological sciences

Passive ambient noise monitoring is an emerging tool in environmental seismology, leveraging the ambient seismic field to assess temporal variations in shallow subsurface properties. This review focuses on the potential and challenges of using scattered coda waves from noise correlation functions to monitor critical zone dynamics. The sensitivity of seismic velocities to various environmental factors, including precipitation, snowmelt, atmospheric pressure, and groundwater fluctuations, underscores the method’s versatility. While coda waves excel in detecting subtle changes due to their scattered nature, ballistic waves provide higher spatial resolution, albeit with challenges in source stability. Advances in seismic sensing, including distributed acoustic sensing and low-cost geophone networks, have enabled high-resolution monitoring of hydrological processes, subsurface deformation, and seismic hazards. Integrating seismic data with hydrological models provides insights into water storage, pore pressure changes, and soil moisture dynamics. However, limitations in spatial resolution, calibration with ground truth data, and coupled effects between environmental factors remain key challenges. This review emphasizes the importance of interdisciplinary approaches in refining methodologies, enhancing sensor deployments, and addressing data gaps. Passive seismic monitoring offers opportunities to understand critical zone processes and their broader impacts on seismic hazards and environmental sustainability.

58 GEOSCIENCES↗

DOE OSTI2023

Subsurface Characterization for Evaluating Geothermal Resource Potential from Existing Oil and Gas Wells in Tuttle, Oklahoma: Preprint

Oil and gas (O&G) wells often encounter co-produced hot water, possibly suitable for geothermal direct-use applications. The City of Tuttle is located on the eastern part of the Anadarko sedimentary basin in Oklahoma with high heat-in-place potential and recovery capability at depth. This study aims at demonstrating the potential of geothermal energy production for direct-use applications in two public schools and 250 nearby houses in Tuttle via repurposing existing O&G wells. In this scope, geochemistry, geology, and borehole log data were collected and incorporated into a 3D conceptual subsurface model. A digital elevation model (DEM) was used to represent the study area topography with four O&G wells. In addition, hydrogeochemical characteristics of the geothermal fluid and scaling potential were analyzed using ternary diagrams and chemical ratios to develop mixing models. The subsurface geology model indicated that the study area primarily consists of Permian to Mississippian Sandstone and Limestone formations, implying a porosity ranging between 12% and 22%, and a permeability up to 3.90E-14 m2 in certain reservoir levels. The reservoir temperature is expected to be ranging between 80 degrees C to 95 degrees C around 3 km depth with an average temperature gradient of 22.8 degrees C/km. Chemical geothermometers also estimated the reservoir temperature as 90 degrees C. Findings of the chemical model demonstrated that the geothermal fluid is Sodium-Potassium-Chloride-Sulfate type and possibly mixed with shallow groundwater resulting in higher Ca and Mg concentrations and lower Na/K ratio implying lower calcite scaling. These results comprehensively characterize the potential of geothermal resources in the study area and imply that geothermal energy production by repurposing existing O&G wells is suitable for low-temperature direct-use applications.

GEOTHERMAL ENERGY↗

DOE OSTI2023

Subsurface Characterization for Evaluating Geothermal Resource Potential from Existing Oil and Gas Wells in Tuttle, Oklahoma

gas wells↗

DOE OSTI2024

Deep Learning for Full Waveform Inversion of Elastic Active-Source Seismic Data to Estimate P-Wave Velocity Models

Seismic imaging methods are critical for Global Security and Energy & Homeland Security missions and activities that rely on subsurface characterization, but traditional methods remain computationally expensive and require significant labor hours and expertise to execute. Within the past few years, machine learning (ML), namely deep learning (DL), has been used to develop data-driven end-to-end full waveform inversion (FWI) methods to estimate 2D P-wave velocity (Vp) models in a fraction of the time as conventional FWI. These methods, however, are trained on simplistic acoustic wave seismic data and Vp models that are not realistic nor representative of real-world observations, leaving a large gap between the state-of-the-art and deployable, feasible, and practical DL FWI methods. Here, we generate a synthetic active-source, 3D, elastic wave seismic data set and a variety of Vp models with realistic geologic structure for training DL FWI methods. We evaluate six different methods that have performed well for acoustic DL FWI or medical imaging tasks using our more realistic dataset. We find that these six trained models do not match the performance of published acoustic end-to-end DL FWI methods, indicating more training data may be needed, physics may need to be incorporated to achieve good accuracy at the sacrifice of the end-to-end advantage, and/or novel methods need to be developed to enable end-to-end DL FWI methods to perform well for real-world seismic data.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗

DOE OSTI2022

Data used in manuscript "Quantifying Subsurface Flow and Solute Transport in a Snowmelt-recharged Hillslope with Multiyear Water Balance", submitted to Water Resources Research on 5-26-2022

Data in this package were used to calculate subsurface flow and solute (specifically dissolved nitrogen) transport in the Pumphouse Lower Montane (PLM) hillslope. These data are included in separate CSV files associated with each figure presented in the manuscript. The files describe site locations and elevations, weather data (air temperatures and precipitation), modeled evapotranspiration, time dependent groundwater elevations, calculated annual subsurface flow, hydraulic conductivity values, time trends in subsurface flow within different zones, pore water concentrations of total dissolve nitrogen (TDN), rates of TDN transport, soil water matric potentials following snowmelt, soil water contents during drainage, and evapotranspiration calculated by soil water mass balance versus by CLM4 model.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Permafrost Thaw, Uneven Subsidence and Projected Drying of Ice-wedge Polygon Tundra: Modeling Archive

This dataset is a model archive of the paper Permafrost Thaw, Uneven Subsidence and Projected Drying of Ice-wedge Polygon Tundra (in prep) to support a modeling study investigating how projected increases in Arctic temperature and precipitation will jointly influence hydrologic conditions in ice-rich tundra landscapes. With this dataset, this study is to address the research question: Will Arctic tundra landscapes become wetter or drier with increasing precipitation and temperature in the future when thaw-induced ground subsidence and associated microtopographic evolution are represented? The simulations focus on ice-wedge polygon tundra, a widespread form of ice-rich permafrost terrain that is highly sensitive to thaw-driven landscape change. This dataset contains model input and output data for four study watersheds in Alaska: Anaktuvuk, Utqiagvik (formerly Barrow), Brooks Foothills, and Prudhoe Bay. Simulations were performed using the Advanced Terrestrial Simulator (ATS, v1.5), a physics-rich integrated surface–subsurface hydrologic model. For each watershed, ten modeling cases were performed representing two landscape evolution conditions (with subsidence and without subsidence) combined with five climate forcing scenarios derived from Shared Socioeconomic Pathways (SSP5, SSP5 with precipitation trend, SSP2, SSP2 with precipitation trend, and SSP2 with double precipitation trend). Particularly, for each watershed under the forcing SSP2 with precipitation trend, there are two additional simulations considering spatially heterogeneous subsidence distributions: one assumes randomly distributed scaling and the other includes elevation dependent distribution scaling. These simulations span 1980–2099 and include spin-up runs (1980–2009) followed by transient projections (2010–2099). To facilitate reproducibility of simulations, all datasets are organized by watershed. For each study watershed, the dataset contains: (1) Pre-partitioned mesh files for 32-core modeling (.par.32.XX), located in EACH_WATERSHED/mesh/basin; and also a non-partitioned mesh file (.exo) located in EACH_WATERSHED/mesh; (2) Climate forcings corresponding to the five SSP scenarios (.h5), located in EACH_WATERSHED/data; (3) Final states (.h5) from column spin-up modeling used to initialize historical watershed-scale spin-up runs from 1980 to 2009, located in EACH_WATERSHED/PreSpinupHistorical; (4) Final states (.h5) of historical watershed-scale spin-up runs from 1980 to 2009 used to initialize projection runs, located in EACH_WATERSHED/Spinup_daymetERA5; (5) ATS modeling input files (.xml), located in EACH_WATERSHED/EACH_SIMULATION_SCENARIO/inputfiles; (6) ATS modeling output files (.dat), located in in EACH_WATERSHED/EACH_SIMULATION_SCENARIO/combined_obs; (7) For the Brooks Foothills watershed, additional spatial model outputs are provided (.h5) for selected years (2033 and 2093) used to generate spatial figures in this study, located in Brooksfoothills/EACH_SIMULATION_SCENARIO/results-WITH/WITHOUT_SUBSIDENCE-year2033/2093. All data files with suffix .h5 can be accessible through Python h5py, and all data files with suffix of .dat can be imported by Python pandas. Mesh file with .exo can be visualized through Paraview or read by Python netCDF. The Next-Generation Ecosystem Experiments in the Arctic (NGEE Arctic) project is a research effort to reduce uncertainty in the Department of Energy’s Energy Exascale Earth System Model (E3SM) by developing a predictive understanding of Arctic tundra ecosystems underlain by permafrost and to quantify feedbacks from the Arctic tundra to the Earth system. NGEE Arctic is supported by the Department of Energy's Office of Biological and Environmental Research. Over Phases 1–3, observations made by the NGEE Arctic team across a gradient of permafrost landscapes in Arctic Alaska improved the representation of tundra processes in the land surface component of E3SM (the E3SM Land Model, ELM). Model improvements emphasized unique aspects of permafrost environments and explored reductions in model complexity while retaining predictive power. The Arctic-informed ELM developed by NGEE Arctic has been used to make novel predictions on processes ranging from permafrost thaw to soil biogeochemical cycling to Earth system feedbacks associated with the unique characteristics of tundra plants. In Phase 4, the NGEE Arctic team is evaluating our new predictive understanding under novel conditions across the Arctic domain. In collaboration with partners at long-term pan-Arctic research sites we are examining whether an Arctic-informed ELM can faithfully simulate interactions among surface and subsurface processes at site, regional, and pan-Arctic scales. In turn, we are using variety of tools to dynamically extend and evaluate ELM inference, with an emphasis on data synthesis and pan-Arctic model evaluation, reintegration of code with an evolving E3SM, scaling across heterogeneous Arctic landscapes, and the appropriate representation of the impacts of increasingly frequent Arctic disturbances.

EARTH SCIENCE > ATMOSPHERE > PRECIPITATION↗

DOE OSTI2021

Deep learning inversion of gravity data for detection of CO 2 plumes in overlying aquifers

In this work, we developed an effective U-Net based deep learning (DL) model for inversion of surface gravity data on a rectangular grid to predict 2-D high-resolution subsurface CO 2 distribution along a vertical cross-section due to CO 2 leakage through a wellbore within a deep CO 2 storage reservoir. We used synthetic data to model two types of CO 2 leakage scenarios: one CO 2 plume in a shallow aquifer (single plume case), and two plumes present at different depths (double plume case). The 3-D synthetic plume samples were created by sampling among predetermined CO 2 plume depths, saturations, and volumes. The corresponding surface gravity data on a rectangular grid were generated by a 3-D forward model. The U-Net model detected 72% of single-plume samples, and one or both plumes in 75% of double-plume samples. Most of the undetected single plumes have small gravity field strengths below the typical noise level of 5 μGal. This model generated reproducible, reliable predictions with acceptable errors and demonstrated improved spatial resolution over the conventional least-squares inversion. In contrast to the conventional least-squares inversion, which often overestimates the size of its target and underestimates its density, this U-Net model accurately delineated the boundary of a target. Furthermore, this DL inversion detected deep, small, or low saturation CO 2 plumes that are often more difficult to resolve with conventional gravity inversion methods. We note the limitations of this feasibility study, including the use of synthetic data with regular CO 2 plume shapes, and the prediction of a 2-D plume cross-section rather than the full 3-D plume, as well, we recognize the lower detection fraction for double-plume scenarios. Nevertheless, this study demonstrates that DL gravity inversion is a promising and potentially superior method to conventional least-squares inversion. Our U-Net based deep learning inversion approach may be adapted for inversion of other types of geophysical data. DL inversion can facilitate near real-time monitoring of geologic carbon sequestration to provide site operators with prompt information about subsurface CO 2 distribution for risk management and mitigation.

58 GEOSCIENCES↗

DOE OSTI2025

Foliar element determination from field survey in association with the National Ecological Observatory Network Airborne Observation Platform survey, East River, Colorado 2018

The purpose of this dataset is to support research aimed at understanding the coupling between hydrologic and biogeochemical processes at watershed scale, particularly the relationship between aboveground vegetation characteristics and subsurface soil properties. These data are intended to inform and calibrate models of catchment-scale biogeochemical fluxes, including rock-derived nutrient cycling, and they were procured to address the following questions: (1) What is the distribution of vegetation characteristics across the study catchments? (2) Are foliar concentrations of rock-derived nutrients related to underlying lithology and soil availability, or are these signals masked by biotic nutrient cycling and retention processes?This data package contains foliar elemental data collected during the 2018 National Ecological Observatory Networks (NEON) Airborne Observation Platform (AOP) imaging spectroscopy and lidar surveys in Gunnison County, Colorado. Folair samples were collected across the East River, Washington Gulch, Slate River, and Coal Creek watersheds and contain a mixture of vegetation including meadow, shrub, and tree foliar samples. The samples were processed using aqua regia digestion and analyzed for elemental determination on inductively coupled plasma optical emission spectrometry (ICP-OES).The data package includes: (1) raw foliar elemental data files in CSV and PDF formats, (2) quality control certificates in PDF format, and (3) an aggregated CSV file containing all elemental measurements compiled across samples. No specialized software is required to access or use these files.

2018 National Ecological Observatory Network Campa↗

DOE OSTI2020

Charged Wellbore Casing Controlled Source Electromagnetics (CWC-CSEM) for Reservoir Imaging and Monitoring (Final report)

This project addresses the needs of the U.S. Department of Energy (DOE) to develop advanced monitoring technologies and protocols to track the fate of subsurface carbon dioxide (CO2) plumes for carbon storage. Specifically, the project seeks to develop and test a unique and novel system of technologies consisting of electromagnetic data acquisition, coupled multiphysics imaging, and reservoir model enhancement to understand the migration and long-term distribution of CO2 in the subsurface. The overarching objective is to develop an integrated approach for long term monitoring of carbon storage. The two main components of the project include the methodology development and the test of the method at a field site. The methodology component consists of 1) developing the field procedure and protocol for collecting time-lapse controlled-source electromagnetic (CSEM) data with source electric current injected into the subsurface through wellbore casings; 2) building of background 3D electrical conductivity utilizing multiple sources of data such as supplemental surface transient EM (TEM) surveys, well-logs, and seismic structural information, for enhancing CSEM signal from reservoir depths; and 3) coupled multiphysics simulations and inversion of CSEM data constrained by production data and by structural information from seismic imaging of the reservoir and overlying formations. The testing component used the field site of Bell Creek Oil Field, which served both as a field laboratory for the method development as well as a test site to evaluate the CSEM signal strengths and the methodology developed in this research project. We have accomplished all the proposed tasks and developed the methodology as planned. These include the procedure for time-lapse CSEM data acquisition, data processing techniques, integration with 3D conductivity model building, fast reservoir simulation for history matching using machine learning, and interpreting CSEM data with coupling to the reservoir modeling. Collectively, the outcome of these tasks form a coherent workflow that can be applied to monitor dedicated carbon storage in saline reservoirs. The testing component evaluated the applicability and limitations of the method, and concluded that the method would be ideal for monitoring dedicated carbon storage sites utilizing saline reservoirs.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Data and scripts associated with the manuscript evaluating the hydrologic responses of the Pacific Northwest watersheds to wildfires (v2)

This data package is associated with the publication “Evaluating Post-fire Watershed Response to Varying Burn Severity and Precipitation Regimes Using Fully-distributed and Integrated Hydrologic Models” submitted to Journal of Hydrology (Li et al. 2025). In this study, we employed the Advanced Terrestrial Simulator (ATS), an integrated watershed model that couples surface flow, subsurface flow, and canopy biophysical processes, to investigate post-fire hydrologic responses in a few selected watersheds with varying burn severity.The data package contains the required input data (meteorological forcing, Leaf Area Index, wildfire burn severities, etc.) to run the model, configuration files, the Jupyter notebooks in Python to pre-process and post-process data, the figures in the manuscript, and the modeling output files. The variables include watershed-averaged evapotranspiration, watershed-averaged surface/subsurface/canopy water content, and river discharge at watershed outlet.The data package contains a file-level metadata that lists and describes all the files contained in the data package (ATS_flmd.csv), a data dictionary file that defines columns headers across all csv files contained in the data package (ATS_dd.csv), a data package level readme file (the current file), and four zipped folders.The ‘data’ folder provides data needed to run the model in .h5, .i2s, .xyz, .shp, and .exo formats. The sub-folders are for each data types. The ‘model’ folder provides input files (.xml format) and essential model outputs. Each sub-folder provides the files from each simulated watershed. The ‘notebooks’ folder provides the Jupyter notebooks (.ipynb format) for pre- and post- processing model files, and for producing the figures in the manuscript. The ‘figures’ folder provides the figures associated with manuscript in .pdf and .png formats.The ‘model’ folder and the ‘data’ folder have been split into 5GB-large pieces using the Linux command ‘split -b 5120m model.zip model.zip.’ and ‘split -b 5120m data.zip data.zip.’, respectively. They can be merged back using the Linux command ‘cat model.zip.* > model.zip’ and ‘cat data.zip.* > data.zip’, respectively.

54 ENVIRONMENTAL SCIENCES↗