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At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Energy to Communities Program Short Introduction: Evaluation of Enhanced Geothermal System for Cornell District Heating [Slides]

Through the U.S. Department of Energy's Energy to Communities (E2C) program, NLR, other national laboratory experts, and select organizations provide Expert Match - free, short-term technical assistance to address near-term energy challenges and questions. Expert Match is for community stakeholders who have decision-making power or influence in their community but need access to additional energy expertise to inform key upcoming decisions. This Expert Match request supported Cornell University (Itaca, NY) community efforts in developing Enhanced Geothermal System (EGS) reservoir performance, by simulating fracture flow to estimate pressure drop, flow distribution, and heat extraction efficiency in fractured rock. Thermal drawdown modeling was used to assess long-term resource sustainability, while system optimization studies refined key parameters such as well configuration and flow rate to improve overall reservoir efficiency.

15 GEOTHERMAL ENERGY↗
DOE OSTI2024

EGS Reservoir Modeling for Developing Geothermal District Heating at Cornell University: Preprint

Cornell University is pursuing development of an enhanced geothermal system (EGS) for providing heating to its main campus in Upstate New York. A ~10,000 ft (~3 km) deep vertical observation well ("CUBO") was drilled in 2022 to characterize the subsurface using wellbore logging, borehole imaging, fluid sampling, mini-frac tests, coring and drill cutting analysis. Down-hole temperatures measured at 3 km depth are about 80 degrees C, sufficiently high for direct-use heating. The well drilled through generally low porosity and low permeability Paleozoic sedimentary formations and into metamorphic basement rock, encountered at about 9,400 ft depth. Leveraging subsurface data obtained through CUBO, we investigated technical feasibility and design requirements of a doublet well system with horizontal laterals connected to a fracture network created through hydraulic fracturing. The EGS reservoir is sized to provide a nominal heat output in the range 5 to 10 MWth of continuous heating over a 15 year-lifetime with limited thermal drawdown. We applied the Gringarten Multiple parallel fracture model, the Cornell Discrete Fracture Simulator FOXFEM and the commercial simulator ResFrac to estimate required heat transfer area and design a potential hydraulic stimulation treatment. Reservoir simulations indicate that, depending on fluid flow rate and injection temperature, 2 to 3 km2 of effective fracture heat transfer area is required to supply the target heat output of 5 to 10 MWth over 15 years.

district heating↗
DOE OSTI2025

EGS Reservoir Modeling for Developing Geothermal District Heating at Cornell University

Cornell University is pursuing development of an enhanced geothermal system (EGS) for providing heating to its main campus in Upstate New York. A ~10,000 ft (~3 km) deep vertical observation well ('CUBO') was drilled in 2022 to characterize the subsurface using wellbore logging, borehole imaging, fluid sampling, mini-frac tests, coring and drill cutting analysis. Down-hole temperatures measured at 3 km depth are about 80 degrees C, sufficiently high for direct-use heating. The well drilled through generally low porosity and low permeability Paleozoic sedimentary formations and into metamorphic basement rock, encountered at about 9,400 ft depth. Leveraging subsurface data obtained through CUBO, we investigated technical feasibility and design requirements of a doublet well system with horizontal laterals connected to a fracture network created through hydraulic fracturing. The EGS reservoir is sized to provide a nominal heat output in the range 5 to 10 MWth of continuous heating over a 15 year-lifetime with limited thermal drawdown. We applied the Gringarten Multiple parallel fracture model, the Cornell Discrete Fracture Simulator FOXFEM and the commercial simulator ResFrac to estimate required heat transfer area and design a potential hydraulic stimulation treatment. Reservoir simulations indicate that, depending on fluid flow rate and injection temperature, 2 to 3 km2 of effective fracture heat transfer area is required to supply the target heat output of 5 to 10 MWth over 15 years.

district heating↗
DOE OSTI2024

SQMS Quantum R&D in Machine Learning, Optimization and Sensing beyond Fundamental Physics Applications

This newly formed team at SQMS under the Ecosystem Thrust is looking to develop capabilities impacting societal advances outside the core domain of HEP and condensed matter physics. We explicitly leverage the experimental and algorithmic innovations developed across all groups as well as connect to broad-scope external projects of the diverse team of PIs. As the inaugural set of projects, we are studying numerically quantum machine learning models inspired by efficiently trainable echo-state and orthogonal neural networks and developing designs for related experiments to be performed on quantum processors based on SQMS SRF cQED technology and Rigetti s transmon arrays. Investigated models exploit ideas and lessons learned from multiple prior work by SQMS team members in a variety of internal and external activities [R1]. Target initial applications include noisy signal processing, potentially captured by quantum sensors or noisy QPUs, as well as simulation and classification of healthcare data. For instance, image reconstruction of the brain s electrical properties by solving the inverse Maxwell equation problem with uncertainty [R2] through a hybrid quantum-classical physics-informed architecture for time-dependent processes [R3]. The group is also investigating the application and development of novel quantum sensors based on magnetic levitation of a superconducting sphere coupled to a superconducting qubit. This coupling enables high-precision measurements of the position of the sphere, which can be used for sensitive detection of forces, enabling practical applications such as gravimetry for geophysics analysis, or accelerometry for GPS-denied navigation [R4] [R1] Rieffel, Eleanor G., Ata Akbari Asanjan, M. Sohaib Alam, Namit Anand, David E. Bernal Neira, Sophie Block, Lucas T. Brady et al. "Assessing and advancing the potential of quantum computing: A NASA case study." Future Generation Computer Systems (2024). [R2] Yu, X., Serrall s, J.E., Giannakopoulos, I.I., Liu, Z., Daniel, L., Lattanzi, R. and Zhang, Z., 2023. Pifon-ept: Mr-based electrical property tomography using physics-informed fourier networks. IEEE Journal on Multiscale and Multiphysics Computational Techniques. [R3] Wudarski, Filip, Daniel OConnor, Shaun Geaney, Ata Akbari Asanjan, Max Wilson, Elena Strbac, P. Aaron Lott, and Davide Venturelli. "Hybrid quantum-classical reservoir computing for simulating chaotic systems." arXiv preprint arXiv:2311.14105 (2023). [R4] Higgins, Gerard, Saarik Kalia, and Zhen Liu. "Maglev for dark matter: Dark-photon and axion dark matter sensing with levitated superconductors." Physical Review D 109.5 (2024): 055024.

Venturelli, Davide↗
DOE OSTI2025

Foundational Dataset for Developing Large-Sample Stream Temperature Models in the Conterminous United States

This dataset provides inputs, evaluation results, and trained weights from a large-sample Long Short-Term Memory (LSTM) model designed to predict daily stream temperatures across unregulated river reaches in the conterminous United States (CONUS). It includes dynamic meteorological and hydrologic forcings, static physiographic attributes, and model outputs from cross-validation experiments spanning 300 basins. It supports reproducible modeling, direct application for new basins, and provides data suitable for integration with reservoir and river simulations under current and future climates. It contains two .zip files described below · RQ-AI_runs.zip: Model outputs from 10-fold cross-validation experiments, including observed and predicted daily stream temperatures, along with test performance metrics for water years 2017–2019. Two versions are included: 1. Model trained and validated using subbasin-area weighted dynamic features. 2. Model trained and validated using whole-basin area weighted dynamic features. · RQ-AI_inputs.zip: Collection of all formatted dynamic and static predictor datasets (meteorological, hydrologic, and physiographic features) used in model training and analysis. Detailed instructions and data structure is held at the following GitLab repository: https://code.ornl.gov/tempwise/training.

Gomez-Velez, Jesus [Oak Ridge National Laboratory ↗
NASA NTRS1984

Vectorization of a penalty function algorithm for well scheduling

In petroleum engineering, the oil production profiles of a reservoir can be simulated by using a finite gridded model. This profile is affected by the number and choice of wells which in turn is a result of various production limits and constraints including, for example, the economic minimum well spacing, the number of drilling rigs available and the time required to drill and complete a well. After a well is available it may be shut in because of excessive water or gas productions. In order to optimize the field performance a penalty function algorithm was developed for scheduling wells. For an example with some 343 wells and 15 different constraints, the scheduling routine vectorized for the CYBER 205 averaged 560 times faster performance than the scalar version.

Absar, I.↗
NASA NTRS2016

Validation of a Cephalad Fluid Shift Countermeasure

INTRODUCTION: This project will provide critical data required to objectively determine how an optimized thigh cuff could be incorporated into the NASA integrated physiological countermeasure suite. This project will determine if thigh cuffs used during simulated spaceflight impact intracranial pressure (ICP), ocular structure and function, and intraocular pressure (IOP) using state of-the-art techniques. Additionally, some of the same methods, hardware, and protocols will be employed in the present investigation to enable direct comparisons to the International Space Station (ISS) "Fluid Shifts" experiment with Chibis-Lower Body Negative Pressure (LBNP). This study will determine the temporal physiological responses of thigh cuff application and removal on ocular and cerebral variables (including invasive ICP) in a microgravity analog. Furthermore, this proposed study will determine tissue pressure distribution applied by thigh cuffs in order to improve comfort, mobility, and efficacy of the countermeasure. Our specific aim is to determine the efficacy of a novel thigh cuff device to mitigate cephalad fluid shifts. We hypothesize that a thigh cuff countermeasure employed in a microgravity analog will temporarily reverse or attenuate ocular and cerebral-volume-pressure variables, approaching normal Earth-based seated posture, the most frequent posture assumed in daily life. In addition, we hypothesize that the magnitude of fluid and pressure redistribution using a thigh cuff countermeasure may require a longer exposure time than that of Chibis-LBNP (using ground-based data from our "Fluid Shifts" project). This project directly addresses Critical Path Roadmap Risks and Questions regarding "Risk of Spaceflight-Induced Intracranial Hypertension/Vision Alterations," and IRP Gap VIIP13: We need to identify preventative and treatment countermeasures to mitigate changes in ocular structure and function and intracranial pressure during spaceflight. METHODS: Noninvasive measures and tissue pressure distributions beneath thigh cuffs The objectives of this study are to: 1) determine the distribution of skin surface pressures beneath the advanced thigh cuff in ten subjects, 2) calibrate the built-in pressure measurement system of the advanced thigh cuff using an industry standard device, and 3) collect subjective feedback and data on the new cuff design to allow for further adjustments prior to invasive studies. A Tekscan Industrial Sensing (I-Scan) system will measure the pressure distribution of the advanced thigh cuff against the skin. In addition, we will measure blood pooling in the thigh and record the circumference of the thigh using Hokanson strain gauge plethysmography. The advanced thigh cuff will be adjusted to obtain a skin contact pressure of 30-50 mmHg as visualized on the Tekscan system. The built-in advanced thigh cuff pressure monitor will be recorded simultaneously to allow direct comparison to the Tekscan measurements. The volunteer will then remove the thigh cuff and remain at rest for five minutes with no legging applied. The thigh cuff will be donned again and pressure measurements will be taken in the same manner for up to 10 repetitions to show reproducibility of pressure after donning. At the conclusion of the study, subjects will be asked to flex their knee, stand, walk, and sit with the thigh cuff activated. During each of these maneuvers the subject will rate their pain/comfort using a modified Borg scale. Effect of thigh cuffs on ICP during simulated microgravity Ommaya reservoir patients will be recruited from the John Wayne Cancer Institute. Ommaya reservoirs provide safe and direct access for the measurement of ICP. Subjects will be instrumented for continuous blood pressure, ECG, and invasive ICP measures. The subjects will be positioned in the upright sitting posture for a 10-minute stabilization period. After the 10-minute stabilization period, imaging measures [ICP, Optical Coherence Tomography, IOP, ocular and vascular ultrasound] will be performed. Following baseline seated measures, the subject will be positioned randomly in the supine, 15deg head-down-tilt, and 15deg head-down-tilt with thigh cuffs and measures repeated. DISCUSSION: Tests to down-select thigh cuff designs will occur in early 2016. Invasive ICP and noninvasive eye imaging tests will begin in spring 2016. Supported by NSBRI through NCC 9-58.

Macias, B.↗
NASA NTRS2019

Measuring the D/H Ratios of Exoplanets and Brown Dwarfs

The relative abundance of deuterium and hydrogen is a potent tracer of planet formation and evolution. Jupiter and Saturn have protosolar atmospheric D/H ratios, a relic of substantial gas accretion from the nebula, while the atmospheres of Neptune and Uranus are enhanced in D by accretion of ices into their envelopes. For terrestrial planets, D/H ratios are used to determine the mechanisms of volatile delivery and subsequent atmosphere loss over the lifetime of the planet. Planets and brown dwarfs more massive than ~13 M(J) quickly fuse their initial D reservoir. Here, we simulate spectra for giant exoplanets and brown dwarfs (2 M(Neptune) to ~10 M(Jupiter)) from T(eff) = 200–1800 K including both CH3D and HDO to determine the observability of these dominant deuterium isotopologues in mid-infrared thermal emission spectra. Colder objects have stronger molecular features in their spectra, due to the temperature dependence of molecular cross sections. CH3D is easier to observe than HDO at all temperatures considered, due to the strength of its absorption bands and locations of features at wavelengths with few other strong absorption features. We predict that for nearby cool brown dwarfs, the CH3D abundance can be measured at high signal to noise with the James Webb Space Telescope (JWST); for objects from 200 to 800 K closer than 10 pc, a protosolar D/H ratio would be readily observable in 2.5 hr. Moderately young Jupiter-mass planets (100–300 Myr) and young Neptunes (10 Myr) may be discovered with JWST and provide the best targets for detecting deuterium on an exoplanet in the coming decade. Future telescope designs should consider the importance of isotopes for understanding the formation and evolution of planetary atmospheres.

Caroline V. Morley↗
NASA NTRS2012

The Connection between Model Performance on the CCMVal Transport Diagnostics and Simulated Sensitivity of Ozone to Chlorine Change

The chemistry climate models that contributed simulations for past and future ozone evolution to the 2010 Scientific Assessment of Ozone Depletion were subject to extensive evaluation by the SPARC (Stratospheric Processes and their Role in Climate) CCMVal (Chemistry-Climate Model Validation) activity. The sensitivity of ozone to changes in composition and climate varies among the models, but the relationship between these variations and the model evaluations of CCMVal is not obvious. We have learned that the transport evaluation can be used to interpret the comparisons between observed and simulated columns of chlorine reservoirs, hydrochloric acid (HCl) and chlorine nitrate (ClONO2); these comparisons were part of the CCMVal evaluation of chemistry. The simulations with best performance on the transport diagnostics most faithfully reproduce the evolution and seasonal variation of the chlorine reservoirs as observed at NDACC (Network for Detection of Atmospheric Composition Change) stations (NyAlesund 78.9N, Kiruna 67.8N, Harestua 60.2N, Jungfraujoch 46.6N, Toronto 43.6N, Kitt Peak 31.9N, Izana 28.3N, Mauna Loa 19.5N, Lauder 45S and Arrival Heights 77.8S). In the simulations, the HCl in the lower stratosphere depends on total inorganic chlorine (Cly) and partitioning between HCl and ClON02. Total inorganic chlorine depends on the fractional release of chlorine from source gases, and ratio of ClON02 to HCl is inversely dependent on methane and varies quadratically with ozone. Simulated HCl from various models may agree with observations even though Cly is in error, partitioning is in error, or both. Simulated ozone sensitivity to chlorine is shown to be greater for models that produce larger values of chlorine nitrate for background chlorine levels, and vice versa. Comparisons with the NDACC data show why the models with 'best' transport have similar sensitivity to chlorine change. The realistic evolution of the simulated HCl and ClONO2 columns suggests realistic levels of Cly in the lower atmosphere. In addition, the wide range values for the sensitivity of ozone to chlorine obtained from the CCMVal simulations is explained by the wide range in lower atmospheric columns of ClONO2 and the concomitant wide range of levels for chlorine monoxide.

Douglass, Anne↗
DOE OSTI2025

Surrogate models for development of unconventional shale reservoirs by an integrated numerical approach of hydraulic fracturing, flow and geomechanics, and machine learning

We develop well-completion surrogate models by taking an integrated workflow of hydraulic fracturing, flow, geomechanics, and machine learning simulation. There are three steps in the proposed workflow. First, history-matching processes are conducted with the field data including pumping and production data for characterization. Second, full-physics simulation is performed with various parameters of the field development (e.g., cluster spacing, clusters per stage, pumping rates and times, amount of proppant, and well spacing) to generate multiple simulation results by changing the parameters of the completion design with well-known hydraulic fracturing, reservoir, geomechanics simulators to calculate fracture geometry, reservoir depressurization, induced stress changes. The workflow is demonstrated over a field in the Southern Midland Basin. Here, we take two completion scenarios: a single well case followed by a multi-well case. Finally, a Long Short-Term Memory (LSTM) machine learning algorithm is employed to create surrogate models that can replicate the full-physics simulation results. Furthermore, results show that the trained models applied in the single well and multi-well cases for a particular geological system can provide good accuracy close to those provided by full-physics simulations. Specifically, the site-specific surrogate models can predict fracture parameters (length, height, and surface area) and cumulative production accurately with computational efficiency, suggesting our proposed workflow can be used as a pragmatic tool for expediting the well completion optimization process.

Geomechanics↗
DOE OSTI2025

Risk assessment of wellbore leakage during underground hydrogen storage

The expansion of renewable energy sources would require large-scale energy storage options to overcome the intermittent nature of these sources. Underground hydrogen storage (UHS) in depleted hydrocarbon reservoirs offers a scalable and practical energy storage solution. These reservoirs are chosen for their availability and large capacity, but the unique properties of hydrogen raise concerns about potential leakage pathways, particularly through wellbores. In this study, we develop and apply, for the first time, reduced-order models (ROMs) specifically designed for efficient leakage risk prediction in UHS systems operating in depleted hydrocarbon reservoirs. Using 3,000 high-fidelity simulation scenarios, we examine the influence of 11 key parameters, including reservoir and aquifer depths, wellbore permeability and porosity, initial saturations of water, oil and gas fractions (hydrogen, light, intermediate, and heavy hydrocarbons), reservoir pressure multiplier, and the aquifer-to-reservoir volume ratio, to simulate leakage behavior over a 1,000-year timescale. We train ROMs using a two-step classification-regression approach, achieving R 2 values exceeding 99 % across all targets. These ROMs effectively capture the leakage evolution and identify critical controls of leakage, guiding the design of mitigation strategies. Results indicate that gas leakage occurs in about 27 % of scenarios as early as five years post-operation, reaching volumes of up to 106 ft3. Oil leakage is less frequent (~17 %) and typically begins decades later. Our findings also show that hydrogen often migrates first, owing to its smaller molecular size and higher buoyancy, followed by heavier hydrocarbons. Over time, these heavier components contribute significantly to the total leaked volume, reinforcing the need for targeted monitoring and remediation strategies. Our analysis highlights that deeper storage reservoirs, shallower aquifers, and low-permeability wellbores significantly reduce leakage risks. In conclusion, this work offers a robust framework for risk-informed UHS deployment, supporting energy security through reliable large-scale hydrogen storage while safeguarding environmental integrity.

08 HYDROGEN↗
DOE OSTI2024

Upscaling Methods Applied to a Fine-Scale Reservoir Model

This study was conducted as part of the Southwest Regional Partnership on Carbon Sequestration (SWP) project to evaluate how upscaling fine-scale simulation models to coarse-scale simulation models impacted the results. The focus was on the Farnsworth Unit (FWU) and its Morrow' B' Sandstone reservoir, specifically the west half of the field. Due to data limitations and the geologic characteristics of the surrounding area, the upscaling was limited to the west half of the FWU rather than a broader basinscale model. The primary aim was to explore how upscaling impacts numerical simulation models, particularly regarding CO 2 -enhanced oil recovery (EOR) and storage capacity predictions. Upscaling was necessary to reduce computational demands when transitioning from high-resolution geological models to coarser grids, as large-scale simulations with finer grids can be computationally prohibitive. This study expands on previous work by the SWP to understand how additional upscaling, applied to already fine-scale numerical simulation models, affects reservoir performance simulations (Ampomah, Balch, & Grigg, 2015). This is key to understanding how loss of resolution can affect coarsescale model results that may be used for large sensitivity analyses, uncertainty quantifications, and training data for machine learning applications.

02 PETROLEUM↗
DOE OSTI2025

Oakridge Geothermal Resources Assessment

Through the Communities Local Energy Action Program (LEAP), NREL provided technical assistance to a coalition of stakeholders from Oakridge, Oregon. The coalition included community-based non-profit organizations, the city government, the local utility, and others. Technical assistance provides analysis and information to support Oakridge stakeholders with their goals to increase energy reliability and resilience in the community, while promoting economic development. The Geothermal Resource Assessment provides technical analysis related to the geothermal resource in the Oakridge area, possible technology applications, and information related to further geothermal exploration. Technical analysis includes a review of a previous geothermal study conducted for Oakridge, thermal modeling to estimate reservoir temperatures, and technoeconomic simulation of a combined heat and power application in the Oakridge area.

15 GEOTHERMAL ENERGY↗
NASA NTRS2013

An Elevated Reservoir of Air Pollutants over the Mid-Atlantic States During the 2011 DISCOVER-AQ Campaign: Airborne Measurements and Numerical Simulations

During a classic heat wave with record high temperatures and poor air quality from July 18 to 23, 2011, an elevated reservoir of air pollutants was observed over and downwind of Baltimore, MD, with relatively clean conditions near the surface. Aircraft and ozonesonde measurements detected approximately 120 parts per billion by volume ozone at 800 meters altitude, but approximately 80 parts per billion by volume ozone near the surface. High concentrations of other pollutants were also observed around the ozone peak: approximately 300 parts per billion by volume CO at 1200 meters, approximately 2 parts per billion by volume NO2 at 800 meters, approximately 5 parts per billion by volume SO2 at 600 meters, and strong aerosol optical scattering (2 x 10 (sup 4) per meter) at 600 meters. These results suggest that the elevated reservoir is a mixture of automobile exhaust (high concentrations of O3, CO, and NO2) and power plant emissions (high SO2 and aerosols). Back trajectory calculations show a local stagnation event before the formation of this elevated reservoir. Forward trajectories suggest an influence on downwind air quality, supported by surface ozone observations on the next day over the downwind PA, NJ and NY area. Meteorological observations from aircraft and ozonesondes show a dramatic veering of wind direction from south to north within the lowest 5000 meters, implying that the development of the elevated reservoir was caused in part by the Chesapeake Bay breeze. Based on in situ observations, Community Air Quality Multi-scale Model (CMAQ) forecast simulations with 12 kilometers resolution overestimated surface ozone concentrations and failed to predict this elevated reservoir; however, CMAQ research simulations with 4 kilometers and 1.33 kilometers resolution more successfully reproduced this event. These results show that high resolution is essential for resolving coastal effects and predicting air quality for cities near major bodies of water such as Baltimore on the Chesapeake Bay and downwind areas in the Northeast.

ozone↗
NASA NTRS1987

A modular BLSS simulation model

The coordination of material flows in earth's biosphere is largely made possible by the buffering effect of huge material reservoirs. Without similarly-sized buffers, a bioregenerative life support system (BLSS) for extraterrestrial use will be faced with coordination problems more acute than those in any ecosystem found on earth. A related problem in BLSS design is providing an interface between the various life-support processors, one that will allow for their coordination while still allowing for system expansion. A modular model is presented of a BLSS that interfaces system processors only with the material storage reservoirs, allowing those reservoirs to act as the principal buffers in the system and thus minimizing difficulties with processor coordination. The modular nature of the model allows independent development of the detailed submodels that exist within the model framework. Using this model, BLSS dynamics were investigated under normal conditions and under various failure modes. Partial and complete failures of various components, such as the waste processor or the plants themselves, drive transient responses in the model system, allowing examination of the effectiveness of the system reservoirs as buffers. The results from simulations of this sort will help to determine control strategies and BLSS design requirements. An evolved version of this model could be used as an interactive control aid in a future BLSS.

Rummel, John D.↗
DOE OSTI2025

Monthly hydropower generation data for Western Canada to support Western-US interconnect power system studies

Hydroelectric power generation in Western Canada significantly contributes to power grid operations of the North American Western Interconnection through substantial generation, some of which is exported to the United States (U.S.). However, the lack of publicly available hydropower generation datasets poses challenges for future market projections and resource adequacy evaluations. We present a simulation-based monthly power system model-ready hydropower generation dataset for 110 facilities in British Columbia and Alberta from 1981 to 2019. These monthly hydropower generation estimates are developed from integrated hydrologic model simulations of runoff and reservoir-operated streamflow, followed by scaling that considers diversion inflow constraints based on hydropower water license information. To address the lack of comparable hydropower generation records, we conduct step-by-step evaluations for simulated runoff, regulated streamflow, and hydropower generation using available observations or estimates. The presented hydropower dataset aims to enhance the representation of hydropower resources in Western Canada, supporting power grid system studies for the Western Interconnection of the U.S. and Canada.

13 HYDRO ENERGY↗
DOE OSTI2026

Consolidation and Permeability of the B1 and D1 Gas Hydrate Bearing Sands and Associated Seal Sediments of the Extended-Duration Gas Production Test Site on the Alaska North Slope

Gas hydrate, a solid combination of gas (mostly methane in nature) and water molecules stable at low temperatures and elevated pressures, occurs naturally in marine and permafrost-associated environments. Gas hydrate reservoirs, such as those in the Alaska North Slope, have been considered potential energy resources for gas production. To understand the petrophysical and geo-mechanical characteristics of the reservoir, core samples retrieved from the site of the JOGMEC-DOE-USGS collaborative gas hydrate R&D project have been analyzed in the laboratory for their hydraulic and mechanical properties. This paper focuses on both seal and reservoir samples associated with the B1 and D1 sands, which are evaluated for index properties (including porosity, grain size distribution, liquid and plastic limits, specific surface area, and specific gravity), consolidation, permeability, and water retention. Furthermore, the reservoir core samples were tested with pore-filling, laboratory-grown tetrahydrofuran hydrate, in order to assess reservoir behavior during gas production from hydrates. Under simulated in situ stress conditions, the seal and hydrate-free reservoir cores had a permeability anisotropy ratio of k h /k v = 3.0−5.0, and k h /k v = 2.4−3.0 for the reservoir tetrahydrofuran hydrate-bearing cores. The data suggest that depressurizing the reservoir to induce hydrate dissociation alters the reservoir effective permeability in three ways: permeabilities decrease due to porosity lost (e.g., the initial reservoir thickness can decrease by up to 5% upon 7 MPa depressurization), permeability increases due to the loss of solid hydrate in the pore space, and permeability anisotropy k h /k v decreases in response to the evolving pore-space geometry. We show that given the simulated in situ gas hydrate saturations (i.e., S h = 32% in core 7P-2E and S h = 21% in core 20P-4), gas production from the dissociation of tetrahydrofuran hydrate in the two tested cores results in a net increase in effective permeability and a decrease in k h /k v . This study highlights the importance of investigating seal and reservoir sediments and the impacts of depressurization on the porosity and permeability responses during production.

Geological materials↗
DOE OSTI2026

Small Reservoirs Offer a New Perspective on Flood Reduction in Large Basins

The flood reduction potential of individual reservoirs within a large river network continuum remains poorly understood due to the complex interplay between reservoir characteristics and network properties. Here we investigate whether a collection of relatively small reservoirs can play a significant role in mediating downstream floods and assess how that role may be influenced by reservoir network properties compared to traditionally known reservoir characteristics. Our unique contribution was the simulation of downstream flood inundation maps alongside peak flows for each of the 81 major reservoirs (6 × 104 to 8 × 109 m3) across 15,000 river reach segments, integrated into a process-based hydrologic model covering a 415,000 km2 region in the Texas Gulf Coast, United States. The three key takeaways from our study are as follows. (a) Smaller reservoirs can substantially reduce downstream flooding, suggesting that inclusion of large reservoirs—the traditional approach in flood risk management studies—may present only a partial picture. (b) Flood reduction by smaller reservoirs is more effective upstream, although this phenomenon may be linked with aridity and overall water availability. (c) While reservoir size matters, it is not the primary factor determining its downstream flood reduction potential; the influence of network properties, such as catchment area, the count of upstream reservoirs, the cumulative maximum storage capacity of upstream reservoirs, and Stream Order (i.e., location), is equally and often more important. The broader impact of our findings goes beyond just floods, providing foundational insights for addressing emerging challenges such as aging dams and river connectivity.

Patel, Krutikkumar [University of Texas at Arlingt↗