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DOE OSTI2018

Assessment of CO 2 Storage Resources in Depleted Oil and Gas Fields in the Ship Shoal Area, Gulf of Mexico

The Gulf of Mexico is one of the most important regions in the United States for energy resources and infrastructure. Gulf of Mexico federal offshore oil and gas production accounts for 17% of total U.S. crude oil production and 5% of total U.S. gas production (EIA Gulf of Mexico Fact Sheet). This region presents an excellent combination of high need and significant opportunity for large scale geologic storage of CO 2 . The Ship Shoal Area is located about 20 miles offshore Louisiana within the Gulf Coast federal waters. Miocene and Pliocene sediments in the Ship Shoal Area are proven to provide excellent and secure traps for oil and gas. The Ship Shoal Area contains a large number of depleted oil and gas fields either currently abandoned, or planned for abandonment by 2025, which may provide very significant potential CO 2 storage capacity. GeoMechanics Technologies conducted a comprehensive research project to better characterize Neogene sediments in the Ship Shoal Area for high volume CO 2 storage. The data generated from literature, well data, well log, and formation evaluation were input into Rockwork 16 geologic software to create a detailed geologic model for the Ship Shoal study area. The geologic grids were then fed into TOUGH2, the gas migration model software and FLAC 3D, the geomechanical model software. The fluid flow modeling for both the Miocene and Pliocene simulation from both Block 107 and Block 84 indicated that Pliocene and Miocene are a good reservoir for the CO 2 sequestration. Thirty years of CO 2 injection (at a rate of 1 million metric tons CO 2 per year) and 30 years of observation simulations were run with the fluid flow modeling. The 3D geomechanical modeling for Ship Shoal Block 107 and Block 84 were developed to evaluate induced surface deformation and potential fault reactivation after 30 years CO 2 injection at base of Pliocene and at upper Miocene injection locations. Evaluation results indicate low to no risks for fault slips or fault reactivation. Twelve well bores and 76 well bores for Ship Shoal Blocks 84 and 107 respectively were reviewed for their cement history. Most wells (57 out of 76 and 7 out of 12 wells in Ship Shoal Block 107 and 84 respectively) have good integrity. Nineteen and 5 wells (in Ship Shoal Block 107 and 84 respectively) with no top plug, incomplete cement or Plug and Abandonment information are given yellow cautionary indicators. These cautionary wells may provide leakage paths of CO 2 through the well bores to the USDWs. Using our Quantitative Risk & Decision Analysis Tool (QRDAT) for caprock integrity evaluation, we compared Ship Shoal’s risk to that of In Salah, Sleipner, Kevin Dome, Loudon, Illinois Industrial CCS and Wilmington Graben. We found the risk at the Ship Shoal Blocks 84 and 107 fields are similar to the known CO 2 active sequestration sites, but lower than the Wilmington Graben turbidities offshore California studied site. The risk of natural seismicity in the Gulf of Mexico is relatively low. Geomechanics Technologies has documented the top 25 CO 2 emission sources within the close proximity of the Ship Shoal Block 84 and 107 fields. All the offshore and onshore pipelines have been digitized and can be viewed in an interactive website (http://www.geomechanicstech.com/shipshoal.html). We also performed a feasibility study on the potential for converting existing oil or gas pipelines for CO 2 transport. There are abandoned, idled and retired onshore and offshore pipelines. However, there is no standard specification for maximum pipeline pressures needed for CO 2 transport. It is a function of design, materials, and testing provided; thus it will be the responsibility of the pipeline operator to correctly determine, maintain and operate within the limits of the pipelines. There are a few transit corridors extending from onshore Louisiana (Cailou Bay) to offshore trunk-lines. The cost for constructing a pipeline has increased about 46% from 2015 to an average cost of $5,064,046 per mile in 2016. We use the NETL CO 2 storage resource mass estimate to calculate the potential resources for Ship Shoal Block 84 and 107 fields using the depleted oil and gas reservoir volume and sand volume generated from our geologic modeling. The estimated storage resource results are greater when using the sand volumes derived through geologic modeling than the oil and gas reservoir data. The difference is due to the depleted oil and gas reservoir information not accounting for the water-flooded sand located below the oil/gas-water contact or unproductive sand units. Resource calculation using the sand volume obtained through geologic modeling overestimate the storage capacity as the model accounts for all sand within the formation, not just the interconnected sand.

02 PETROLEUM↗
DOE OSTI2024

Efficient Dimension Reduction of Complex Three-dimensional CO2 Saturation using Deep Learning Models

In the domain of deep learning (DL), dimension reduction is crucial for enhancing training efficiency and mitigating overfitting, particularly when managing complex data such as three-dimensional (3D) saturation data. The 3D saturation data in the context of geological carbon storage (GCS) presents unique challenges due to its inherent sparsity and the abrupt transitions at plume boundaries, known as shock fronts. To address the challenges, we proposed a novel DL framework that integrates dimension reduction with advanced 3D reconstruction techniques. Our model leveraged latent variables derived from 2D average saturation data, offering a robust and efficient solution tailored to the intricate dynamics of 3D saturation fields. The proposed framework can extract the critical features of the high-dimensional data while reducing the variable numbers, which is more tractable for DL models and enhances the model robustness and accuracy. Therefore, it provides a novel approach for modeling and analyses in complex geological scenarios, which finds great potential applications in environmental monitoring and energy storage.

Wang, Hongsheng↗
DOE OSTI2026

Regional Earthquake Ground Motion Simulations for Southern California With EQSIM: Insights From the 2008 Chino Hills, 2024 Highland Park, and 2021 Carson Earthquakes

This study presents physics-based, 3D simulations using the EQSIM framework for several earthquakes in the Los Angeles region. The primary objective was to assess the ability of deterministic physics-based ground motion simulations to reproduce the observed motions from historical events. The selected events included the mathematical equation M w 5.4 2008 Chino Hills, the mathematical equation M w 4.4 2024 Highland Park, and the mathematical equation M w 4.3 2021 Carson events. The simulated motions were evaluated by comparing the recorded and simulated seismograms, as well as the Fourier amplitude spectra, across multiple seismic stations. The SCEC 3D velocity model, CVM-S4.26.M01, was used to represent the regional geology, and ground motion simulations were carried out with a resolution of up to 5 Hz. The results indicate that the simulated motions captured the recorded motions up to approximately 4 Hz. While careful iterations regarding source parameters and corner frequencies were required, and, for the case of the Highland Park event, some of the near-source stations had relatively low accuracy, the present study established a positive step toward the utilization of physics-based simulations in practical applications. The computational efficiencies exhibited by EQSIM, especially on GPU clusters, further supported this assertion, as wall-clock times of simulations involving more than 10 billion grid points were as low as mathematical equation minutes. This permits ensemble simulations for a considered scenario event so that modeling uncertainties (e.g., source and geology) can be bracketed.

EQSIM↗
DOE OSTI2018

Modeling dynamic fracture in granite under in situ conditions at high temperatures and pressures

Reproducing the in situ mechanical response of granite at a depth of a few kilometer under dynamic loading presents significant challenges. We have developed and calibrated a new thermo-mechanical model for granite using extensive experimental data from both quasi-static and dynamic test data. The model includes parameters which can be calibrated for a specific geologic setting. Using the model developed we have performed a series of 2D and 3D simulations to evaluate effects of in situ stress and temperature on the extent of damage surrounding underground explosions. We compare our results with available data for cavity size, plastic zone and peak velocity attenuation with range in granite.

58 GEOSCIENCES↗
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 OSTI2024

Deep Learning-based Parameterization of Complex 3D CO2 Saturation Data in Large-scale Geological Carbon Storage

In deep learning (DL), dimension reduction plays a pivotal role in improving training efficiency and minimizing overfitting, especially when working with complex datasets like three-dimensional (3D) saturation data. In the context of geological carbon storage (GCS), 3D saturation data introduces unique challenges due to its sparse nature and sharp transitions at plume boundaries, known as shock fronts. To tackle these challenges, we developed a novel DL framework that combines dimension reduction with advanced 3D reconstruction techniques. Our approach utilizes latent variables derived from 2D average saturation fields to efficiently capture the essential features of high-dimensional data while reducing the number of variables. This enhances both the robustness and accuracy of DL models, making the framework more practical for real-world applications. By offering a tailored solution for modeling complex 3D saturation dynamics, this framework holds significant potential for environmental monitoring, energy storage, and other geological applications.

Wang, Hongsheng [University of Texas at Austin]↗
DOE OSTI2019

Coupled Geomechanical Modeling to Assess Cap Rock Integrity and Mechanical Fault Stability: Application to Farnsworth Field Unit Project

Abstract The SWP project is located in a mature waterflood undergoing conversion to CO2-WAG operations in the Farnsworth, Texas, USA. Anthropogenic CO2 is sourced from a fertilizer and an ethanol plant. This work utilizes Farnsworth’s full-field, history-matched, compositional hydrodynamic coupled geomechanical model for assessing the impact of stress changes observed through the history matched field life. Production and injection induced stress changes, fault stability and caprock integrity investigations are performed to project the potential for fault reactivation and the loss of caprock integrity under shear failure. A static mechanical earth model (MEM) was constructed for use in transient coupled geomechanical model based on the existing Southwest Regional Partnership (SWP) geological model. The static MEM inherits the stratigraphic and structural features of the geologic model and incorporates additional overburden, underburden, and sideburden formations required to impose mechanical boundary conditions. Mechanical properties were distributed in the 3D MEM through integration of geophysical logs and 3-dimensional seismic elastic inversion properties using a combination of Bayesian and stochastic interpolation methods. These data are further enhanced by lab derived strength and failure criteria for the caprock interval. Additionally, interpreted faults and other geological features were included as part of the static structural framework to fully represent subsurface hydraulic and mechanical systems and appropriately integrate heterogeneity. Two-way coupling of hydrodynamic flow and geomechanical simulations incorporates the Kozeny-Carman relationship for updating permeability and is history matched through primary, secondary (waterflooding) and tertiary (CO2 WAG) recovery periods before performing a twenty year forecast. Two-way simulations are performed to understand the effective stress perturbations imposed by field operations: water injection and the more recent implementation of WAG. Evaluation of Mohr circles with liquid production and field pressure charts as well as slip tendency and distance to failure metrics indicate that neither our faults or cap rock are critically stressed. This paper presents the results of the Farnsworth initial attempts to integrate the seismic driven 3D MEM into coupled hydrodynamic geomechanical history match simulation workflow.

02 PETROLEUM↗
DOE OSTI2017

Progress Report on the Nevada Play Fairway Project: Integrated Geological, Geochemical, and Geophysical Analyses of Possible New Geothermal Systems in the Great Basin Region

The Great Basin region of the western USA is capable of producing much greater amounts of geothermal energy than the current ~670 MW from ~25 power plants. Most geothermal resources in this region are blind, and thus the favorable characteristics for geothermal activity must be synthesized and methodologies need to be developed to discover new commercial-grade systems. In the Nevada play fairway project, nine geologic, geochemical, and geophysical parameters were synthesized to produce a new detailed geothermal potential map of 96,000 km2 from west-central to eastern Nevada. These parameters were grouped into subsets and individually weighted to delineate rankings for local permeability, regional permeability, and heat, which collectively defined geothermal play fairways (i.e., most likely locations for significant geothermal fluid flow). This work is aimed at reducing the risks in geothermal exploration and therefore facilitating discovery of new commercial-grade systems in blind settings, as well as in areas with surface expressions of geothermal activity. Twenty-four highly prospective areas, including both known undeveloped systems and previously undiscovered potential blind systems, were identified across the region for further analysis. Five of the most promising sites have been selected for detailed studies: Granite Springs Valley, Sou Hills, southern Gabbs Valley, Crescent Valley, and Steptoe Valley. Multiple techniques, including detailed geologic mapping, shallow temperature surveys, detailed gravity surveys, LiDAR, geochemical studies, seismic reflection analysis, and 3D modeling, are now being employed in these areas. The ultimate goal of these detailed studies is to determine the most likely areas for high permeability thermal fluids such that potential drill sites can be targeted. Potential new blind systems discovered in this project include southern Gabbs Valley and Granite Springs Valley currently undergoing detailed study, as well as step-overs in the Dun Glen area and along the west flank of the Humboldt Range.

15 GEOTHERMAL ENERGY↗
DOE OSTI2016

The Nevada Play Fairway Project — Phase II: Initial Search for New Viable Geothermal Systems in the Great Basin Region, Western USA

Estimates suggest that the Great Basin region is capable of producing much greater amounts of geothermal energy than the current ~600 MW from ~24 power plants. Because most of the geothermal resources in this region are blind, it is imperative that the favorable characteristics for geothermal activity be synthesized and methodologies developed to discover new robust systems. Nine geologic, geochemical, and geophysical parameters were synthesized to produce a new detailed geothermal play fairway map of a large transect across the Great Basin region (96,000 km2) from west-central to eastern Nevada. These parameters were grouped into subsets to delineate rankings for local permeability, regional permeability, and heat, which collectively defined geothermal play fairways (i.e., most likely locations for significant geothermal fluid flow). The primary objective of this work was to reduce the risks in geothermal exploration and thus facilitate discovery of commercial-grade, blind geothermal fields. Two dozen highly prospective areas, including both known systems and previously undiscovered potential blind systems, have been identified across the region for further analysis, of which several will be selected for detailed studies. Multiple techniques, including detailed geologic mapping, shallow temperature surveys, detailed gravity surveys, LiDAR, geochemical studies, seismic reflection analysis, and 3D modeling, are being employed in the detailed studies. Initial work on the detailed studies has begun and will proceed over the next year.

15 GEOTHERMAL ENERGY↗
DOE OSTI2021

A Physics-Constrained Deep Learning Model for Simulating Multiphase Flow in 3D Heterogeneous Porous Media

Physics-based simulators for multiphase flow in porous media emulate nonlinear processes with coupled physics, and usually require extensive computational resources for software development, maintenance and simulation execution. As a result, a huge demand exists for fast modeling of coupled processes in a wide range of subsurface applications including geological sequestration, hydrocarbon recovery and geothermal energy extraction. In this work, an efficient physics-constrained deep learning model is developed for solving multiphase flow in 3-Dimensional (3D) heterogeneous porous media. The model fully leverages the spatial topology predictive capability of convolutional neural networks, specifically U-Net with successive contracting and expansive steps, and is coupled with an efficient continuity-based smoother to predict flow responses that need spatial continuity. Furthermore, the transient regions are penalized to steer the training process such that the model can accurately capture flow in these regions. The model takes inputs including properties of porous media, fluid properties and well controls, and predicts the temporal-spatial evolution of the state variables (pressure and saturation). While maintaining the continuity of fluid flow, the 3D spatial domain is decomposed into 2D images for reducing training cost, and the decomposition results in an increased number of training data samples and better training efficiency. Additionally, a surrogate model is separately constructed as a postprocessor to calculate well flow rate based on the predictions of state variables from the deep learning model. We use the example of CO 2 injection into saline aquifers, and apply the physics-constrained deep learning model that is trained from physics-based simulation data and emulates the physics process. The model performs prediction with a speedup of ~ 1400 times compared to physics-based simulations, and the average temporal errors of predicted pressure and saturation plumes are 0.27% and 0.099% respectively. Furthermore, water production rate is efficiently predicted by a surrogate model for well flow rate, with a mean error less than 5%. Therefore, with its unique scheme to cope with the fidelity in fluid flow in porous media, the physics-constrained deep learning model can become an efficient predictive model for computationally demanding inverse problems or other coupled processes.

58 GEOSCIENCES↗
DOE OSTI2026

Improving Building Footprint Extraction Using NAIP and 3DEP Lidar Derived Features with Deep Learning

Accurate building footprint extraction is critical for applications ranging from population estimation to disaster management. Although optical imagery provides detailed spectral information, it often struggles with shadows, occlusions, and background clutter in dense urban environments. Lidar data, by contrast, offer precise elevation and structural attributes but face challenges such as variable point density and noise. This study integrates multispectral imagery from the U.S. Department of Agriculture (USDA) National Agriculture Imagery Program (NAIP) with lidar-derived feature height and intensity from the U.S. Geological Survey (USGS) 3D Elevation Program (3DEP) to improve footprint extraction using a U-Net–based deep learning model. A six-band input stack (RGB, near-infrared, height, intensity) was developed, normalized, and tiled for training and evaluation against Microsoft Global Building Footprints (GBF). Results from the Houston, TX test site show that the six-band model achieved a precision of 0.86, recall of 0.88, F1 score of 0.87, and Intersection-over-Union (IoU) of 0.76, consistently outperforming four-band baselines by reducing false positives while maintaining sensitivity. Predictions on withheld Houston tiles confirmed strong within-region generalization, yielded a precision of 0.78, recall of 0.81, F1 score of 0.79, and IoU of 0.66. Qualitative analysis further revealed limitations stemming from both training label quality and vegetation–building confusion. These findings demonstrate the complementary value of integrating spectral and structural information for robust building footprint extraction and how domain adaptation strategies can be used to enhance cross-regional transferability.

Liu, Jung Kuan [United States Geological Survey (U↗
DOE OSTI2022

Deep learning-accelerated 3D carbon storage reservoir pressure forecasting based on data assimilation using surface displacement from InSAR

Fast forecasting of the reservoir pressure distribution during geologic carbon storage (GCS) by assimilating monitoring data is a challenging problem. Due to high drilling cost, GCS projects usually have spatially sparse measurements from few wells, leading to high uncertainties in reservoir pressure prediction. To address this challenge, we use low-cost Interferometric Synthetic-Aperture Radar (InSAR) data as monitoring data to infer reservoir pressure build up. We develop a deep learning-accelerated workflow to assimilate surface displacement maps interpreted from InSAR and to forecast dynamic reservoir pressure. Employing an Ensemble Smoother Multiple Data Assimilation (ES-MDA) framework, the workflow updates three-dimensional (3D) geologic properties and predicts reservoir pressure with quantified uncertainties. We use a synthetic commercial-scale GCS model with bimodally distributed permeability and porosity to demonstrate the efficacy of the workflow. A two-step CNN-PCA approach is employed to parameterize the bimodal fields. The computational efficiency of the workflow is boosted by two residual U-Net based surrogate models for surface displacement and reservoir pressure predictions, respectively. The workflow can complete data assimilation and reservoir pressure forecasting in half an hour on a personal computer.

25 ENERGY STORAGE↗
DOE OSTI2017

Discovering New Geothermal Systems In The Great Basin Region, Western USA: An Integrated Approach For Establishing Geothermal Play Fairways

The Great Basin of the western USA is capable of producing much greater amounts of geothermal energy than the current ~670 MW from ~25 power plants. Most geothermal resources in this region are blind, and thus the favorable characteristics for geothermal activity must be synthesized and methodologies developed to discover new commercial-grade systems (>130oC). The geothermal play fairway concept involves integration of multiple parameters indicative of geothermal activity as a means of identifying promising areas for new development. In the Nevada play fairway project, nine geologic, geochemical, and geophysical parameters were initially synthesized to produce a new geothermal potential map of 96,000 km2. These parameters were grouped into subsets and individually weighted to delineate rankings for heat and local, intermediate, and regional permeability, which collectively defined the play fairways. From the regional map, 24 highly prospective areas, including known undeveloped systems and unknown potential blind systems, were identified for further analysis. Five particularly promising sites were then selected for detailed studies. Multiple techniques, including geologic mapping, Quaternary fault analysis, 2-m temperature surveys, gravity surveys, LiDAR, geochemical studies, seismic reflection analysis, and 3D modeling, were employed in these areas to define likely sites for high permeability and define highly prospective drilling targets. Local and intermediate permeability models were revised to reflect results of detailed analyses and generate new detailed play fairway maps. Lessons learned include: 1) initially identified sites commonly include multiple favorable settings at a finer scale; 2) promising sites in Cenozoic basins cannot be recognized without detailed geophysical surveys; and 3) play fairway analysis is critical at multiple scales, providing a means to select regional prospects as well as vectoring into drilling targets at individual sites. Based on our detailed studies, several potential new, high temperature systems were discovered. However, TG drilling is needed to confirm commercial grade resources.

15 GEOTHERMAL ENERGY↗
DOE OSTI2021

3D orthorhombic earth model effects on seismic source characterization

Most earth materials are anisotropic with regard to seismic wave-speeds, especially materials such as shales, or where oriented fractures are present. However, the base assumption for many numerical simulations is to treat earth materials as isotropic media. This is done for simplicity, the apparent weakness of anisotropy in the far field, and the lack of well-characterized anisotropic material properties for input into numerical simulations. One approach for addressing the higher complexity of actual geologic regions is to model the material as an orthorhombic medium. We have developed an explicit time-domain, finite-difference (FD) algorithm for simulating three-dimensional (3D) elastic wave propagation in a heterogeneous orthorhombic medium. The objective of this research is to investigate the errors and biases that result from modeling a non-isotropic medium as an isotropic medium. This is done by computing “observed data” by using synthetic, anisotropic simulations with the assumption of an orthorhombic, anisotropic earth model. Green’s functions for an assumed isotropic earth model are computed and then used an inversion designed to estimate moment tensors with the “observed” data. One specific area of interest is how shear waves, which are introduced in an anisotropic model even for an isotropic explosion, affect the characterization of seismic sources when isotropic earth assumptions are made. This work is done in support of the modeling component of the Source Physics Experiment (SPE), a series of underground chemical explosions at the Nevada National Security Site (NNSS).

58 GEOSCIENCES↗
DOE OSTI2025

Southwest Regional Partnership on Carbon Sequestration: Phase III (Final Scientific/Technical Report)

The Southwest Regional Partnership on Carbon Sequestration (SWP) is one of 7 regional partnerships formed in 2003 under the U.S. Department of Energy’s (DOE) Regional Carbon Sequestration Partnerships (RCSPs) initiative. The overall purpose of the initiative was to help determine and implement the technology, infrastructure, and regulations most appropriate to promote carbon storage in different regions of the country. Covering Arizona, Colorado, New Mexico, Oklahoma, Utah, and parts of Texas, Wyoming, and Kansas, the SWP evaluated regional carbon storage and utilization potential and focused on technologies and sites that could complement the region’s strong position in energy production. The project progressed through three phases: • Phase I (2003–2005): Characterized regional geologic formations and CO 2 sources, assessed sequestration potential, and identified pilot test sites. • Phase II (2005–2013): Conducted small-scale field tests to validate sequestration methods, including geologic and terrestrial projects. • Phase III (2008–2022): Demonstrated large-scale CO 2 injection at a commercial oil field to test monitoring, verification, and long-term storage strategies. This report covers Phase III. The final project site, the Farnsworth Unit (FWU) in Texas, provided real-world testing of reservoir characterization, monitoring, and risk evaluation tools and processes that could be used in any commercial scale carbon capture, utilization, and storage (CCUS) project. Extensive data collection and analysis helped refine best practices for reservoir characterization, injection monitoring, and storage verification. The SWP contributed to national databases, DOE best practice manuals, and regional geological assessments to support future sequestration efforts. Key lessons learned include the importance of robust data management, strategic site selection, regulatory navigation, and effective industry collaboration. The project’s findings will inform ongoing and future carbon storage initiatives. Task 1 (Regional Characterization) • The SWP continued to participate in national outreach efforts and NATCARB. • The SWP evaluated multiple potential sites before selecting the FWU as the primary field test location. Task 2 (Public Outreach and Education) • The SWP contributed to national databases, DOE best practice manuals, and regional geological assessments to support future sequestration efforts. Task 3 (Permitting and Regulatory Compliance) • The SWP ensured compliance with federal and state regulations, including National Environmental Policy Act (NEPA) requirements. • The SWP obtained all necessary permits for drilling, injection, and monitoring activities. Task 4 (Site Characterization and Planning) • The SWP developed work plans for four key activities: characterization, simulation, monitoring and verification, and risk evaluation. • The SWP collected and synthesized legacy data from multiple sources to build initial static geological models and dynamic reservoir models demonstrating project feasibility. • The SWP conducted an initial risk evaluation and developed mitigation plans. Task 5 (Field Operations and Data Collection) • The SWP drilled, logged, and cored three characterization wells to gather critical subsurface data. • The SWP conducted multiple geophysical surveys, including 3D seismic, crosswell seismic, and vertical seismic profiling, to improve reservoir characterization. Task 6 (Monitoring and Verification) • The SWP performed extensive geological characterization using data from characterization wells and seismic surveys. • The SWP established a surface monitoring network to track CO 2 flux in soil gas, groundwater chemistry, and near-surface atmospheric CO 2 levels. • The SWP built and refined reservoir models to study the effects of relative permeability on simulation behavior and improve calibration with experimental data. Task 7 (Risk Assessment and Model Refinement) • The SWP conducted multiple studies to evaluate reservoir integrity, predict CO 2 plume behavior and improve predictive modeling capabilities. • The SWP refined geological models and used them to enhance the accuracy of simulation models. • The SWP continued quantitative risk assessment of top-ranked risks and strengthened the link between qualitative and quantitative risk methodologies.

02 PETROLEUM↗
DOE OSTI2023

DEEPEN Leapfrog Geodata Model Cleaned and Reformatted Exploration Datasets from Newberry Volcano

DEEPEN stands for DE-risking Exploration of geothermal Plays in magmatic ENvironments. As part of the DEEPEN 3D play fairway analysis (PFA) conducted at Newberry Volcano for multiple play types (conventional hydrothermal, superhot EGS, and supercritical), existing geoscientific exploration datasets needed to be acquired, cleaned, reformatted, and assembled in Leapfrog Geothermal. This GDR submission includes all of the cleaned and reformatted (X (m), Y (m), elevation (m), processed data values) datasets used to build the Leapfrog Geodata model. Existing datasets were acquired from the GDR, from AltaRock, and from other sources. This yielded the following datasets: - Digital elevation model produced from LiDAR data by Ramsey and Bard, 2016 - MT surveys from 2006, 2011, 2014, and 2017 (including single inversions) - Gravity surveys from 2006, 2007, and 2011 (including single) - Earthquake catalogs from PNSN, LLNL, and the Newberry EGS Demonstration project - Seismic velocity model from Templeton et al., 2014 - The Frone, 2015 temperature model and a new one produced through extrapolating downhole temperature measurements and the SMU temperature at depth maps. Two versions of the new model are provided: 250 m spacing and 500 m spacing - EarthVision geologic model with alteration from Moser et al., 2016 - Well data from EGS well 55-29, deep geothermal wells, coreholes (GEO N-2 through 5) and several thermal gradient holes - "Newberry Well Data:" Location, simple lithology, directional survey data, and temperature data for the 34 wells and coreholes used in the Newberry PFA Although there are additional 2D datasets available in the area, such as aeromagnetic surveys, these were not included in the analysis. While it may be possible to project these datasets into three dimensions by assuming the surface measurements do not vary with depth, this method is associated with high uncertainty. Preexisting inversions of these data were unavailable, and inverting additional geophysical datasets is outside the scope of this project.

15 GEOTHERMAL ENERGY↗
DOE OSTI2025

Building confidence in models for complex barrier systems for radionuclides

The modeling and simulation of the Cement-clay Interaction-Diffusion field (CI-D) experiment at the Mont Terri site in Switzerland presented here demonstrates that it is possible to capture the multiscale physical and chemical features of natural and engineered barrier systems for radionuclides. The simulations are successfully carried out with the newly developed CrunchODiTi high-performance computing software that accounts for multiple continua, including a continuum representing the electrical double layer (EDL) developed along negatively charged clay particles in clay rock. The simulation also accounts for both the complex three-dimensional (3D) geometry, expected as the norm in a geological waste repository, and the anisotropy of the geological formation. In addition, the high resolution of the model makes it possible to include "skin effects" developed at the interface between highly reactive materials, in this case between the high pH cement and the circumneutral but electrostatic Opalinus Clay. The successful history matching with the field experiment demonstrates that the distinct geochemical and physical properties of the cement and the Opalinus Clay in the CI-D experiment can be accounted for. Such analyses are essential for developing a defensible safety case for the underground storage of radioactive waste.

Sarsenbayev, Dauren↗