Search NASA⌕ Search

SEARCH · Search NASA

Results for “subsurface fracture evolution”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

36 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Sub-Diurnal Methane Variations on Mars Driven by Barometric Pumping and Planetary Boundary Layer Evolution

In recent years, the Tunable Laser Spectrometer within the Sample Analysis at Mars (TLS-SAM) instrument on board the Mars Science Laboratory (MSL) Curiosity rover has detected methane variations in the atmosphere at Gale crater. Methane concentrations appear to fluctuate seasonally as well as sub-diurnally, which is difficult to reconcile with an as-yet-unknown transport mechanism delivering the gas from underground to the atmosphere. To potentially explain the fluctuations, we consider barometrically induced transport of methane from an underground source to the surface, modulated by temperature-dependent adsorption. The subsurface fractured-rock seepage model is coupled to a simplified 1-D atmospheric mixing model to provide insights on the pattern of atmospheric methane concentrations in response to transient surface methane emissions, as well as to predict sub-diurnal variation in methane abundance for the northern summer period, which is a candidate time frame for a MSL Curiosity sampling campaign. Our analysis suggests that there is a lower limit to the subsurface fracture density that can produce the observed methane patterns, below which the atmospheric methane variations would be out of phase with the observations. The best-performing model scenarios indicate a significant, short-lived methane pulse just prior to sunrise, the detection of which by TLS-SAM would be a potential indicator of the contribution of barometric pumping to Mars' atmospheric methane variations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

The emergence of small-scale self-affine surface roughness from deformation

Most natural and man-made surfaces appear to be rough on many length scales. There is presently no unifying theory of the origin of roughness or the self-affine nature of surface topography. One likely contributor to the formation of roughness is deformation, which underlies many processes that shape surfaces such as machining, fracture, and wear. Using molecular dynamics, we simulate the biaxial compression of single-crystal Au, the high-entropy alloy Ni 36.67 Co 30 Fe 16.67 Ti 16.67 , and amorphous Cu 50 Zr 50 and show that even surfaces of homogeneous materials develop a self-affine structure. By characterizing subsurface deformation, we connect the self-affinity of the surface to the spatial correlation of deformation events occurring within the bulk and present scaling relations for the evolution of roughness with strain. These results open routes toward interpreting and engineering roughness profiles.

42 ENGINEERING↗
DOE OSTI2021

Seismic Diffusivity and the Influence of Heterogeneity on Injection‐Induced Seismicity

Abstract The spatiotemporal patterns of injection‐induced seismicity (IIS) are commonly interpreted with the concept of a triggering front, which propagates in a diffusion‐like manner with an associated diffusivity parameter. Here, we refer to this diffusivity as the “seismic diffusivity.” Several previous studies implicitly assume that seismic diffusivity is equivalent to the effective hydraulic diffusivity of the subsurface, which describes the behavior of the mean pressure field in heterogeneous porous media. Seismicity‐based approaches for hydraulic characterization or simulations of IIS using domains of homogeneous equivalent porous media are implicitly based on this assumed equivalence. However, seismicity is expected to propagate with the threshold triggering pressure, and thus not be controlled by the evolution of the mean pressure field. We present numerical simulations of fluid injection to compare the seismic and effective hydraulic diffusivities in heterogeneous formations (including fractured rock). The numerical model combines uncoupled, linear pressure diffusion with the Mohr‐Coulomb failure criterion to simulate IIS. We demonstrate that connected pathways of relatively high hydraulic diffusivity in heterogeneous media (particularly in fractured rock domains) allow the threshold triggering pressure to propagate more rapidly than predicted by the effective hydraulic diffusivity. As a result, the seismic diffusivity is greater than the effective hydraulic diffusivity in heterogeneous porous media, possibly by an order of magnitude or more. Additionally, we present a case study of IIS near Soultz‐sous‐Forêts where seismic diffusivity is found to be at least one order of magnitude larger than the effective hydraulic diffusivity.

Haagenson, Ryan↗
DOE OSTI2024

Photoluminescence probing of light absorption centers at silica laser damage

We use photoluminescence (PL) imaging to study damage growth precursors within laser damage sites on the surface of silica. Damage site evolution is induced by multiple shots of UV nanosecond pulsed laser at various energy densities and monitored throughout the early stages of growth. Wide-field PL imaging rapidly locates microscopic light absorption centers within the silica damage site. Our quantitative analysis shows that damage sites with strong local PL intensity show a higher probability of growth upon subsequent laser pulses. Scanning electron microscopy (SEM) paired with a study of PL spectrum shows that the strong PL intensity appears from the subsurface fractures with high defect density, which provides a local light absorption center leading to significant damage growth. We believe that this result offers an efficient optical damage mitigation strategy by providing a rapid and non-destructive optical inspection approach.

36 MATERIALS SCIENCE↗
DOE OSTI2024

SMART – A Comprehensive Research and Development Program to Demonstrate Application of Machine Learning for Supporting CCS Deployment

Presentation material for a paper presented at the GHGT-17 conference, Calgary, Canada, October 20-24, 2024. The objective of the US Department of Energy’s SMART Initiative, i.e., Science-informed Machine Learning (ML) for Accelerating Real-Time Decisions in Subsurface Applications, is to showcase how the utilization of ML can significantly improve efficiency and effectiveness of field-scale commercial carbon storage operations. This paper will present the results from the current phase of SMART (field deployment) for demonstrating the applicability of ML-based tools and workflows for: (a) virtual learning during the pre-injection permitting phase, (b) advanced storage reservoir imaging to better characterize fractures and faults, and (c) dynamic storage reservoir modelling and optimization to inform operational decision making and visualization of system evolution.

CO2 geologic storage↗
DOE OSTI2024

SMART – A Comprehensive Research and Development Program to Demonstrate Application of Machine Learning for Supporting CCS Deployment

The objective of the US Department of Energy’s SMART Initiative, i.e., Science-informed Machine Learning (ML) for Accelerating Real-Time Decisions in Subsurface Applications, is to showcase how the utilization of ML can significantly improve efficiency and effectiveness of field-scale commercial carbon storage operations. This paper will present the results from the current phase of SMART (field deployment) for demonstrating the applicability of ML-based tools and workflows for: (a) virtual learning during the pre-injection permitting phase, (b) advanced storage reservoir imaging to better characterize fractures and faults, and (c) dynamic storage reservoir modelling and optimization to inform operational decision making and visualization of system evolution.

Siriwardane, Hema↗
DOE OSTI2021

Advanced Detection of Wellbore Failure for Safe and Secure Utilization of Subsurface Infrastructure

The main goal of this project was to create a state-of-the-art predictive capability that screens and identifies wellbores that are at the highest risk of catastrophic failure. This capability is critical to a host of subsurface applications, including gas storage, hydrocarbon extraction and storage, geothermal energy development, and waste disposal, which depend on seal integrity to meet U.S. energy demands in a safe and secure manner. In addition to the screening tool, this project also developed several other supporting capabilities to help understand fundamental processes involved in wellbore failure. This included novel experimental methods to characterize permeability and porosity evolution during compressive failure of cement, as well as methods and capabilities for understanding two-phase flow in damaged wellbore systems, and novel fracture-resistant cements made from recycled fibers.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Inferring fracture dilation and shear slip from surface deformation utilising trained surrogate models

An important task in energy and CO 2 storage (sequestration) in the subsurface is to verify that the surrounding fractures and faults are not activated, acting as leakage pathways. This is achievable through effective and efficient Measurement, Monitoring and Verification (MMV) plans. In this work, two surrogate models are trained to captures dilation (opening) and shear deformation of fractures, and the associated surface deformation. The trained surrogate model, based on conditional Generative-Adversarial Networks (cGAN) receives fracture apertures from dilational fractures together with fracture slips from shear fractures and predicts the combined surface deformation. An inversion algorithm based on Bayesian framework is proposed to identify the geometry of both types of fractures, as well as volume of dilational fractures and deformation moment induced by shear fractures, all from the measured surface deformation data. The inversion algorithm utilises the Differential Evolution (DE) optimisation technique that has the superior performance in finding the global minimum of cost function. The proposed surrogate-assisted inversion successfully inferred the unknown dip, dip direction and the volume of the dilational fractures as well as the induced deformation moment in shear fractures. The model was further tested for the inversion of a field hydraulic fracturing tilt dataset applying different scenarios with varying unknowns to show the model's performance, as well as incorporating shear deformation for better match with the observed data.

Dilation and shear↗
DOE OSTI2025

Deep learning forecasts the spatiotemporal evolution of fluid-induced microearthquakes

Microearthquakes generated by subsurface fluid injection record the evolving stress state and permeability of reservoirs. Forecasting their spatiotemporal evolution is therefore critical for applications such as enhanced geothermal systems, carbon dioxide sequestration and other geoengineering applications. Here we propose a transformer neural network model that ingests hydraulic stimulation history and prior microearthquake observations to forecast four key quantities: cumulative microearthquake count, cumulative logarithmic seismic moment, and the 50th- and 95th-percentile extents of the microearthquake cloud. Applied to the EGS Collab Experiment 1 dataset, the model achieves R2 > 0.98 for the 1-s forecast horizon and R2 > 0.88 for the 15-s forecast horizon across all targets, and supplies uncertainty estimates through a learned standard deviation term. These accurate, uncertainty-quantified forecasts enable real-time inference of fracture propagation and permeability evolution, demonstrating the strong potential of deep-learning approaches to improve seismic-risk assessment and guide mitigation strategies in future fluid-injection operations.

Chung, Jaehong↗
DOE OSTI2024

Microbial sensor variation across biogeochemical conditions in the terrestrial deep subsurface

ABSTRACT Microbes can be found in abundance many kilometers underground. While microbial metabolic capabilities have been examined across different geochemical settings, it remains unclear how changes in subsurface niches affect microbial needs to sense and respond to their environment. To address this question, we examined how microbial extracellular sensor systems vary with environmental conditions across metagenomes at different Deep Mine Microbial Observatory (DeMMO) subsurface sites. Because two-component systems (TCSs) directly sense extracellular conditions and convert this information into intracellular biochemical responses, we expected that this sensor family would vary across isolated oligotrophic subterranean environments that differ in abiotic and biotic conditions. TCSs were found at all six subsurface sites, the service water control, and the surface site, with an average of 0.88 sensor histidine kinases (HKs) per 100 genes across all sites. Abundance was greater in subsurface fracture fluids compared with surface-derived fluids, and candidate phyla radiation (CPR) bacteria presented the lowest HK frequencies. Measures of microbial diversity, such as the Shannon diversity index, revealed that HK abundance is inversely correlated with microbial diversity ( r 2 = 0.81). Among the geochemical parameters measured, HK frequency correlated most strongly with variance in dissolved organic carbon ( r 2 = 0.82). Taken together, these results implicate the abiotic and biotic properties of an ecological niche as drivers of sensor needs, and they suggest that microbes in environments with large fluctuations in organic nutrients (e.g., lacustrine, terrestrial, and coastal ecosystems) may require greater TCS diversity than ecosystems with low nutrients (e.g., open ocean). IMPORTANCE The ability to detect extracellular environmental conditions is a fundamental property of all life forms. Because microbial two-component sensor systems convert information about extracellular conditions into biochemical information that controls their behaviors, we evaluated how two-component sensor systems evolved within the deep Earth across multiple sites where abiotic and biotic properties vary. We show that these sensor systems remain abundant in microbial consortia at all subterranean sampling sites and observe correlations between sensor system abundances and abiotic (dissolved organic carbon variation) and biotic (consortia diversity) properties. These results suggest that multiple environmental properties may drive sensor protein evolution and highlight the need for further studies of metagenomic and geochemical data in parallel to understand the drivers of microbial sensor evolution.

response regulator↗
DOE OSTI2026

Effects of temperature and dose rate on ion-irradiated γ-LiAlO 2 pellets

Defect accumulation and microstructural evolution during ion irradiation at elevated temperatures are governed by competing processes of defect production, driven by the dose rate, and defect recovery, controlled by diffusion, interaction, and annihilation. Here, this study investigates the effects of irradiation temperature and the dose rate on microstructural evolution, deuterium retention, and lithium volatilization in γ-LiAlO 2 pellets subjected to sequential He + and D + ion irradiation. Experiments were performed to a total fluence of 3 × 10 17 (He + + D + )/cm 2 at 623, 673, 723, and 773 K with an average He + dose rate of 7.7 × 10 −4 dpa/s, and to 2 × 10 17 (He + + D + )/cm 2 at 773 K with dose rates of 6.8 × 10 −5 , 2.9 × 10 −4 , and 7.3 × 10 −4 dpa/s. At 623 K, the microstructure was dominated by cavities and fractures with no observable precipitate formation, while small precipitates emerged at 673 K. Increasing the irradiation temperature to 723–773 K promoted the formation of larger, faceted LiAl 5 O 8 precipitates, and surface amorphization, accompanied by pronounced lithium depletion and H–D isotopic exchange. At 773 K, medium and high dose rates produced an amorphized surface layer over a crystalline subsurface containing LiAl 5 O 8 precipitates and blisters at the crystalline–amorphous interface, whereas low-dose-rate irradiation preserved surface crystallinity with cavities distributed in the matrix, around precipitates, and along grain boundaries. Precipitate morphology was anisotropic with limited size dependence on the dose rate. These results elucidate the coupled effects of temperature and the dose rate and demonstrate that sequential He + and D 2 + irradiation at 773 K reproduces key microstructural features and H isotope behavior observed in neutron-irradiated γ-LiAlO 2 at 573 K.

dose rate effects↗
DOE OSTI2024

Revealing complex subsurface dynamics with continuous seismic monitoring: Observations using distributed acoustic sensing and surface orbital vibrators during hydraulic fracturing

Understanding hydraulic fracturing is crucial to improving the stimulation of unconventional reservoirs and increasing fluid production. This study develops a novel seismic monitoring technology using distributed acoustic sensing (DAS) and surface orbital vibrators (SOV) to capture fracture seismic response and mechanical properties at high temporal intervals. We analyze continuous time-lapse vertical seismic profiling (VSP) data acquired every hour during the first nine days of treatment of an unconventional reservoir in the Austin Chalk/Eagle Field Laboratory. The VSP data contain clear seismic signals scattered from the activated fractures. The spatiotemporal changes of the fracture reflectivity revealed by the SOV/DAS data correlate well with the observations of fracture locations inferred from low-frequency DAS data. These results capture the fracture opening and closure processes, as well as highlight potential prestage activations of the fractures due to hydraulic connectivity with preexisting fracture systems. Therefore, analysis of the presented data set provides a unique opportunity to understand fracture initiation and subsequent evolution, not only in the context of unconventional resources but also in enhanced geothermal systems.

Correa, Julia↗
DOE OSTI2020

Feasibility of using reactive silicate particles with temperature-responsive coatings to enhance the security of geologic carbon storage

The large-scale deployment of geologic carbon storage will require minimizing the risk of CO 2 leakage. Here we report on a novel strategy to seal leakage pathways by injecting functionalized reactive calcium silicate (CaSiO 3 ) particles. The particles consist of a mineral silicate core within a temperature-sensitive Poly(N,N-dimethylaminoethyl methacrylate) coating that limits contact between the core and the surrounding CO 2 (aq) above a switching temperature making them relatively unreactive deep in the subsurface. If the CO 2 leaks to shallower depths and cooler temperatures, the coatings would extend and enable the silicate core to react with CO 2(aq) to form solid carbonate precipitates that reduce the permeability of the fracture or flow path. A combined laboratory- and simulation-based investigation was conducted to explore key early-stage research questions related to the feasibility of this leakage management strategy. Sand columns were injected with either coated or uncoated CaSiO 3 particles and the permeability was measured before and after exposure to CO 2 . Experiments were conducted at 35 °C and 70 °C, a range that spans the switching temperature for the coatings. Results show that at the cooler temperature the coating effectively enabled reaction and permeability reduction. At higher temperatures, the coated particles limited mineral carbonation and the permeability decrease was minimal compared to columns with uncoated particles. Here, the morphology and location of the precipitates provides insight about permeability evolution. Model simulations of 2D radial particle transport from an injection point provided insight into the effects of particle size, coating properties, and concentrations of particles in several representative rock formations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Isotopic fractionation as in-situ sensor of subsurface reactive flow and precursor for rock failure

Greater utilization of subsurface reservoirs perturbs in-situ chemical-mechanical conditions with wide ranging consequences from decreased performance to project failure. Understanding the chemical precursors to rock deformation is critical to reducing the risks of these activities. To address this need, we investigated the coupled flow-dissolution- precipitation-adsorption reactions involving calcite and environmentally-relevant solid phases. Experimentally, we quantified (1) stable isotope fractionation processes for strontium during calcite nucleation and growth, and during reactive fluid flow; (2) consolidation behavior of calcite assemblages in the common brines. Numerically, we quantified water weakening of calcite using molecular dynamics simulations; and quantified the impact of calcite dissolution rate on macroscopic fracturing using finite element models. With microfluidic experiments and modeling, we show the effect of local flow fields on the dissolution kinetics of calcite. Taken together across a wide range of scales and methods, our studies allow us to separate the effects of reaction, flow, and transport, on calcite fracturing and the evolution of strontium isotopic signatures in the reactive fluids.

42 ENGINEERING↗
DOE OSTI2025

Machine Learning Approaches to Predicting Induced Seismicity and Imaging Geothermal Reservoir Properties

This project developed machine learning (ML) methods, lab data sets, and field data to advance geothermal exploration and geothermal energy production. The work had three focus areas. One involved the development of ML methods to use microearthquakes (MEQs) for imaging geothermal reservoir properties and improving subsurface characterization – most importantly the evolution of permeability within the evolving reservoir. This part of the work included development of ML approaches for automated MEQ location, focal mechanism determination and identification of earthquake precursors. The second area focused on using MEQ signals generated by geothermal exploration and production to predict the relationship between fluid injection and seismicity. Here, we extended to reservoir scale our success in using ML to predict laboratory earthquakes and fault zone stress state. The third focus area was on lab experiments. Here, we developed new ML models for lab earthquake prediction and identification of precursors to failure to improve earthquake forecasting and early warning in geothermal settings. Major outcomes of our work include ML models that learn from MEQ signals during geothermal exploration and production to predict induced seismicity. MEQs occur naturally in connection with drilling and energy production. We developed ML methods to use the seismic waves from these events to characterize the elastic, hydraulic and poromechanical properties of reservoirs. Our work illuminated fracture geometry and the evolution of fracture permeability by incorporating seismic coda wave analysis and ML methods to relate fluid injection and seismicity. We significantly expanded laboratory earthquake prediction to include methods that use both passive measurements of microearthquakes within the lab fault zones and also active source acoustic measurements of fault zone elastic properties. These methods can now predict fault zone stress state, time to failure and the magnitude of lab earthquakes. Our work showed that repetitive stick- slip failure events during frictional sliding (the lab equivalent of earthquakes) are preceded by a cascade of micro-failure events that radiate energy in a manner that foretells unstable failure – manifest as laboratory MEQs. We documented a mapping between fracture properties and statistical attributes of elastic radiation. We extended existing works to geothermal reservoir scale and developed ML methods to determine reservoir permeability, fracture properties, and their evolution during geothermal energy production. An attractive feature of ML algorithms is their ability to handle big datasets and reveal patterns and correlations that may remain invisible to conventional analyses. Our work connected data from field, laboratory and intermediate scales to study permeability, stress, strength, fracture stiffness and geometry. At the field scale we used data from the Newberry Volcano field site, UtahFORGE, EGS Collab, and also the Bedretto underground research lab in Switzerland. These data sets are bridging the gap between the lab scale, theory, and reservoir scale. Our work produced plain language summaries to improve public understanding of DOE research. We also developed openly distributed ML and seismicity datasets for use by all researchers and we published connections between induced seismicity in geothermal areas and reservoir properties including permeability, fracture properties, and stress state. Our models are designed for the large data sets of induced seismicity typically associated with geothermal sites. We produced labeled event catalogs and used them on geothermal data to assess how ML can facilitate geothermal production and exploration. All datasets are available on the GDR Productivity: The project produced 32 publications in peer reviewed journals (two are in review). It supported the work of 6 PhD students, 40 conference presentations, 6 keynote talks at national meetings, and mentoring and professional development for 4 postdoctoral fellows.

15 GEOTHERMAL ENERGY↗
DOE OSTI2022

Chemical and isotopic evolution of flowback fluids from the Utica Gas Shale Play, Eastern Ohio USA

For this work, hydraulic fracturing flowback fluids were collected from two Utica/Point Pleasant well pads in eastern Ohio. One site was in the wet gas zone (UPPW), while the site ~50 km south consisted of four wells on the same pad in the dry gas zone (UPPS). Samples of input fluids also were collected before and during hydraulic fracturing. Flowback fluids are Na-Ca-Cl brines with total dissolved salt (TDS) concentrations that increase over several months from ~100 to 200 g/L. The slightly higher TDS of the dry gas fluids are in part due to recycled flowback used as input fluids, and in part due to lower volume of water used in hydraulic fracturing. Concentrations of most major ions (Ca 2+ , Mg 2+ , Na + , Sr2 + , Fe, Mn, Cl – , Br – ) are similar for the five wells sampled, although small but systematic changes occur in the major element ratios over time. Most notably, an increase in the Sr/Cl ratio corresponds to an increase in the 87 Sr/ 86 Sr ratio in the fluids, suggesting interactions with a more radiogenic Sr source in the subsurface. Dissolved Ba concentrations and Ra activities were different between the two sites, reflecting a high SO 4 2– fluid used at the UPPW site, and water-rock reactions occurring during hydraulic fracturing at the UPPS site. Water oxygen (δ 18 O) and hydrogen (δD) isotopes for input fluids used in the UPPW4 well and fresh water used for the UPPS wells fall on the Global Meteoric waterline (GMWL). Flowback fluids from both sites are relatively enriched in δ 18 O and δD compared to the input, but do not appear to follow a simple mixing trend, suggesting reaction and isotopic exchange with carbonates and fractionation due to imbibition in the rock. Cl isotopes, δ 37 Cl in the FP fluids varied from ~ –0.43 to +0.13‰, the largest variation was observed in the earlier stages of flowback, while in the later stages δ 37 Cl exhibited a small but systematic increase over time, suggesting diffusion control of isotopic composition. Some of the trace species measured (dissolved Fe, Mn, thiosulfate and organic acids) do not follow the same trends as the major ions, suggesting contributions from input fluids, or microbially mediated reactions are exerting control on their concentrations. Input water chemistry exerts an important control on the Sr and Ba concentrations in flowback water. High SO 4 2– in the input fluids used for hydraulically fracturing the wet gas well leads to precipitation of barite-celestite in flowback fluids.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

The weathered bedrock vadose zone: A hidden control on water availability in the western United States (Final Technical Report)

This report summarizes the research completed during the project entitled "The weathered bedrock vadose zone: A hidden control on water availability in the western United States" (Award: DE-SC0018039). The report covers work completed between 9/15/17 and 9/14/20. The objective of the exploratory project work was to evaluate the overarching hypothesis that the subsurface underlying actively eroding landscapes is systematically structured as a result of a balance between weathering and erosion and that this structure leads to dynamic moisture storage within weathered rock. To accomplish project objectives, we characterized hillslope weathering profile structure and conducted subsurface moisture monitoring across four study sites with comparable bedrock types but differing erosion rate and hydroclimate: The Angelo Coast Range Reserve (ACRR), Sagehorn-Russell Ranch (SRR), Antelope Valley Ranch (AVR), and the LBNL Watershed Function Scientific Focus Area (SFA). New characterization and monitoring infrastructure at the AVR and SFA sites was established through this project and enables long-term monitoring of hydrologic and carbon fluxes at the sites for comparison with long-term monitoring in other sites. Deep drilling, downhole logging, and characterization of chemistry and porosity of rock recovered from drilling reveals two weathering fronts across the sites. A shallower front is marked by the extent of pervasive fracturing and oxidation of pyrite and organic carbon. A deeper weathering front is marked by the appearance of open fractures and discoloration. At the AVR site, multiple ridge tops of different hillslope length were investigated to establish the relationship between the weathering front and topography. Drilling reveals that the fraction of the hillslope relief that is weathered scales with hillslope length, consistent with the theoretical framework that guided the project relating groundwater drainage with landscape evolution. With the establishment of new boreholes, we have documented significant water storage in weathered bedrock using low-field borehole nuclear magnetic resonance (NMR) across all four sites. In the upper 4 m of bedrock, the average water contents at ACRR, AVR, SRR, and SFA are approximately 25%, 16%, 25%, 23% respectively. At the ACRR site, where long-term monitoring via neutron probe surveys and time-domain transmission sensors are available, we have demonstrated that NMR reliably records moisture content and importantly, moisture content dynamics in weathered bedrock. Across all sites, our NMR monitoring has also revealed that a considerable fraction of the water storage occurs in fractures or pores that are significantly larger than the fine-grained bedrock matrix of the parent rock. To identify the proportion of water storage occurring in fractures and large pores, we analyzed the NMR signal by evaluating both the sum of echoes and an inversion of the NMR signal for a distribution of T2 relaxation times. Both methods reveal that at least 20% of the water storage occurs in fractures and larger pores across all sites. To constrain the interpretation of the in-situ field-scale NMR measurements, we compare the NMR response in the field to independent pore-structure information obtained via helium pycnometry. Results support the inference that dynamic seasonal water storage is dominantly restricted to the fracture network, which can reach 7% of the total volume. This dynamic range in water content is comparable to that of some soils, underscoring the need to understand how this region of the Earth System is structured, and functions as a hydrologic reservoir.

58 GEOSCIENCES↗
DOE OSTI2025

Crystallographic Texture, Structure, and Stress Transmission in Nugget Sandstone Examined With X‐Ray Tomography and Diffraction Microscopy

Subsurface processes in sandstones are controlled by porosity, permeability, and deformation mechanisms, all of which are controlled by a complex interplay of crystallographic rock texture, structure, and micromechanics. Texture, structure, and micromechanics have historically been studied using optical and electron microscopy of thin-sections. Here, we employed a new combination of in situ X-ray tomography and ray diffraction microscopy to study crystallographic texture, structure, and grain stresses in 3D. We examined these features in a sample of Nugget sandstone, a sandstone constituting hydrocarbon reservoirs across the American West. Our aims are threefold. First, we demonstrate the utility of X-ray diffraction microscopy probes for revealing texture, structure, and stress transmission in 3D. Second, we apply these techniques to Nugget sandstone and discuss findings in the context of prior work. Third, we study grain stress tensor evolution during mechanical compression to examine whether their heterogeneity and orientation evolution reflect that of inter-particle forces in granular materials. Our results show: (a) larger grains featured higher intra-granular misorientations, possibly from an increased prevalence of cements; (b) pores closed parallel to the loading direction and opened normal to loading; (c) grain stresses featured heterogeneity and orientations similar to inter-particle forces in non-cohesive granular materials; (d) grains featured compressive stresses in the loading direction and tensile stresses orthogonal to the loading direction, the latter resisting sample dilation and grain separation. Our work demonstrates the first known application of multi-modal X-ray tomography and diffraction microscopy to sandstone, providing new 3D insight into the nature of quartz cement and stress evolution.

high energy diffraction microscopy↗