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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.

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At least 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Stimulated geologic hydrogen: from mechanistic control to engineered rock transformation

Geologic hydrogen (GeoH 2 ) generated from subsurface iron-rich rock–water reaction (i.e., serpentinization) is emerging as a promising candidate for the next primary energy source. Yet, accelerating GeoH 2 production from geological to human timescales via enhanced serpentinization remains a formidable scientific and technical challenge. Here, in this Review, we decipher the mechanistic control and explore strategies for accelerating in situ, engineered iron-rich rock transformation into carbon-free GeoH 2 by orders of magnitude. Serpentinization rate is hindered by low porosity and permeability of source rocks, suboptimal temperatures, unfavorable water chemistry, inefficient Fe 2+ -to-Fe 3+ conversion, thermodynamic constraints, and low reactive surface area. While closed-system experiments provide valuable mechanistic insights, open-system conditions with fluid circulation are more crucial for economically viable GeoH 2 production. We assess stimulation techniques from enhanced hydrocarbon and geothermal recovery as tools to be adopted or adapted for increasing reactive surface areas for stimulated GeoH2 production. We estimate that 7.40 × 10 5 to 1.73 × 10 6 million metric tons (Mt) of hydrogen could be engineered over 20 to 50 years from about 10% iron-rich rocks within 10 km depth of continental crust. Enabling GeoH 2 as a viable energy source requires not only advancing scientific frontiers but also forming a global GeoH 2 research network and innovation ecosystem to address the critical scientific, technical, societal, economic, and policy challenges.

08 HYDROGEN↗
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 OSTI2023

Utah FORGE: Well 16A(78)-32/Well16B(78)-32 Circulation Test Data

This dataset encompasses the collected data and associated reports from the low-rate injection circulation test conducted on wells 16A(78)-32 and 16B(78)-32 in July 2023 at Utah FORGE. It includes comprehensive raw circulation data, captured by Pason and Schlumberger (SLB). The dataset details various parameters recorded during circulation testing such as produced fluid, injection pressure, injection rate, produced rate, temperature, downhole pressure difference, bottom hole pressure in the well, and surface treating pressure. Note that the Pason data is presented in Mountain Time, whereas the SLB data is in Pacific Time.

15 GEOTHERMAL ENERGY↗
DOE OSTI2023

Influence of extreme fracture flow channels on the thermal performance of open-loop geothermal systems at commercial scale

Abstract Adequate stewardship of geothermal resources requires accurate forecasting of long-term thermal performance. In enhanced geothermal systems and other fracture-dominated reservoirs, predictive models commonly assume constant-aperture fractures, although spatial variations in aperture can greatly affect reservoir permeability, fluid flow distribution, and heat transport. Whereas previous authors have investigated the effects of theoretical random aperture distributions on thermal performance, here we further explore the influence of permeability heterogeneity considering field-constrained aperture distributions from a meso-scale field site in northern New York, USA. Using numerical models of coupled fluid flow and heat transport, we conduct thermal–hydraulic simulations for a hypothetical reservoir consisting of a relatively impervious porous matrix and a single, horizontal fracture. Our results indicate that in highly channelized fields, most well design configurations and operating conditions result in extreme rates of thermal drawdown (e.g., 50% drop in production well temperatures in under 2 years). However, some other scenarios that account for the risks of short-circuiting can potentially enhance heat extraction when mass flow rate is not excessively high, and the direction of geothermal extraction is not aligned with the most permeable features in the reservoir. Through a parametric approach, we illustrate that well separation distance and relative positioning play a major role in the long-term performance of highly channelized fields, and both can be used to help mitigate premature thermal breakthrough.

Rangel-Jurado, Nicolás↗
DOE OSTI2020

Seismic identification of geothermal prospecting in Harrat Rahat, Northern Arabian Shield

Harrat Rahat volcanic field lies along the Makkah-Madinah-Nafud active volcanic line. It has extensive linear weak zones of wrench faults that could provide ideal conduits for upwelling magma and hydrothermal fluids as well. Here, passive seismic data recorded by seismic stations installed within and around Harrat Rahat has been processed for the detection of microseismic events. Moreover, ambient noise was also gathered and analyzed to construct a subsurface velocity model. This model indicates low velocity zone in Harrat Rahat, and two areas with coincident basement faults and possibly with enhanced subsurface temperatures were identified. It is concluded that Harrat Rahat has high geothermal potential and promising for geothermal renewable power.

58 GEOSCIENCES↗
DOE OSTI2022

From Fluid Flow to Coupled Processes in Fractured Rock: Recent Advances and New Frontiers

Abstract Quantitative predictions of natural and induced phenomena in fractured rock is one of the great challenges in the Earth and Energy Sciences with far‐reaching economic and environmental impacts. Fractures occupy a very small volume of a subsurface formation but often dominate fluid flow, solute transport and mechanical deformation behavior. They play a central role in CO 2 sequestration, nuclear waste disposal, hydrogen storage, geothermal energy production, nuclear nonproliferation, and hydrocarbon extraction. These applications require predictions of fracture‐dependent quantities of interest such as CO 2 leakage rate, hydrocarbon production, radionuclide plume migration, and seismicity; to be useful, these predictions must account for uncertainty inherent in subsurface systems. Here, we review recent advances in fractured rock research covering field‐ and laboratory‐scale experimentation, numerical simulations, and uncertainty quantification. We discuss how these have greatly improved the fundamental understanding of fractures and one's ability to predict flow and transport in fractured systems. Dedicated field sites provide quantitative measurements of fracture flow that can be used to identify dominant coupled processes and to validate models. Laboratory‐scale experiments fill critical knowledge gaps by providing direct observations and measurements of fracture geometry and flow under controlled conditions that cannot be obtained in the field. Physics‐based simulation of flow and transport provide a bridge in understanding between controlled simple laboratory experiments and the massively complex field‐scale fracture systems. Finally, we review the use of machine learning‐based emulators to rapidly investigate different fracture property scenarios and accelerate physics‐based models by orders of magnitude to enable uncertainty quantification and near real‐time analysis.

58 GEOSCIENCES↗
DOE OSTI2026

Self-Healing Cements with Improved Toughness at Casing and Formation Interfaces for Subsurface Applications (CRADA 530)

Over the three-year CRADA, Pacific Northwest National Laboratory (PNNL) advanced a new class of Molecular Velcro™ self-healing wellbore cements designed to improve the long-term integrity of wells used for geothermal energy production and carbon storage. Conventional wellbore cement can crack due to repeated mechanical loading, temperature swings, and exposure to aggressive subsurface fluids. Once cracks form, they can create leakage pathways, drive expensive remediation (“workovers”), and in extreme cases shorten well life. This project addressed that problem by developing polymer-modified cement systems that can autonomously re-seal cracks and better tolerate the extreme conditions experienced in subsurface wells, while remaining compatible with practical cementing operations.

15 GEOTHERMAL ENERGY↗
DOE OSTI2020

Grid-Scale, Long-Term Energy Storage: Repurposing Hydrocarbon Reservoirs, Resources, and Infrastructure to Store CO 2 and Heat (Mid-Project Progress Report)

The purpose of this project is to create a viable business case for CO 2 capture, use, and storage (CCUS) by enabling grid-scale, long-term energy storage in mature hydrocarbon reservoirs and saline aquifers. Our CCUS approach, called the Earth Battery, is based on patents issued to Lawrence Livermore National Laboratory on Multi-Fluid Geo-Energy and Earth Battery Systems and to the University of Minnesota and TerraCOH, Inc. on CO 2 Plume Geothermal (CPG). Excess energy can be used to compress and store CO 2 and hot brine underground in permeable formations, including: (1) saline aquifers suitable for geologic CO 2 storage (GCS) and (2) hydrocarbon reservoirs, the focus of this project featuring a pilot test in a mature oil and natural gas (NG) field. Excess energy can come in the form of excess electricity from the grid or excess NG that does not have a market. It can also come in the form of excess heat from other sources, such as from the heat of air or CO 2 compression or from a nearby base-load power plant or solar thermal energy (STE) facility, when heat is not needed to generate electricity from those sources.

15 GEOTHERMAL ENERGY↗
DOE OSTI2026

Big Data Meets Geothermal Exploration (CRADA Final Report)

As part of the Cyclotron Road program, Zanskar Geothermal & Minerals, Inc. investigated the application of micro-earthquake and ambient noise seismology methods to imaging and characterizing the structural characteristics and hydrothermal flux of subsurface faults. Significant advances in what could be resolved were enabled by two major developments in seismology: 1) the availability of large-n arrays of low-cost seismometers, and 2) the availability of increased computational power and semi-automated data reduction algorithms. In tandem, these advances may improve the signal-to-noise ratio and spatial precision of the data collected and enable higher-resolution characterization of subsurface fracture systems and their spatio-temporal evolution. These tools supported efforts to reduce dry-hole risk and to improve wellfield productivity for geothermal resource development. In particular, two applications of these advances were evaluated: 1) fracture-seismic imaging, which was used to detect ambient emissions from fluid-filled fractures, and 2) reservoir tomography, which used information about travel paths, source locations, and source parameters of micro-earthquakes to identify areas of enhanced permeability. Integration of these methods provided guidance for siting wells and served as prior constraints for reservoir models, informing forecasts of power potential and production and injection strategies aimed at minimizing temperature decline and improving overall resource productivity.

15 GEOTHERMAL ENERGY↗
DOE OSTI2022

Modeling Subsurface Performance of a Geothermal Reservoir Using Machine Learning

Geothermal power plants typically show decreasing heat and power production rates over time. Mitigation strategies include optimizing the management of existing wells—increasing or decreasing the fluid flow rates across the wells—and drilling new wells at appropriate locations. The latter is expensive, time-consuming, and subject to many engineering constraints, but the former is a viable mechanism for periodic adjustment of the available fluid allocations. In this study, we describe a new approach combining reservoir modeling and machine learning to produce models that enable such a strategy. Our computational approach allows us, first, to translate sets of potential flow rates for the active wells into reservoir-wide estimates of produced energy, and second, to find optimal flow allocations among the studied sets. In our computational experiments, we utilize collections of simulations for a specific reservoir (which capture subsurface characterization and realize history matching) along with machine learning models that predict temperature and pressure timeseries for production wells. We evaluate this approach using an “open-source” reservoir we have constructed that captures many of the characteristics of Brady Hot Springs, a commercially operational geothermal field in Nevada, USA. Selected results from a reservoir model of Brady Hot Springs itself are presented to show successful application to an existing system. In both cases, energy predictions prove to be highly accurate: all observed prediction errors do not exceed 3.68% for temperatures and 4.75% for pressures. In a cumulative energy estimation, we observe prediction errors that are less than 4.04%. A typical reservoir simulation for Brady Hot Springs completes in approximately 4 h, whereas our machine learning models yield accurate 20-year predictions for temperatures, pressures, and produced energy in 0.9 s. This paper aims to demonstrate how the models and techniques from our study can be applied to achieve rapid exploration of controlled parameters and optimization of other geothermal reservoirs.

15 GEOTHERMAL ENERGY↗
DOE OSTI2020

toughio v1.2.2

TOUGH is a widely used general purpose numerical simulator designed for fluid and heat flows of multiphase, multicomponent fluid mixtures in porous and fractured media, which has been applied to many real-world problems such as underground geological storage, geothermal reservoir engineering or nuclear waste disposal, to name a few. `toughio` is a lightweight, object-oriented and vectorized Python library that aims to provide user-friendly routines to facilitate pre- and post-processing of a TOUGH simulation. Although its primary target is the latest version TOUGH3, `toughio` can also be used to read/write TOUGH2 input/output files. Currently, to the best of our knowledge, only PyTOUGH offers an exhaustive number of features to carry out a complete TOUGH simulation using a scripting language. `toughio` and PyTOUGH share the same objectives, yet with different approaches and philosophies. On the one hand, a PyTOUGH mesh is represented as a MULGRAPH geometry where elements can be unstructured horizontally but only layered vertically. It also checks that the model parameters are valid to ensure that a TOUGH simulation runs smoothly. On the other hand, although it provides basic meshing features, `toughio` mostly relies on common third-party softwares (e.g. Abaqus, FLAC3D, Gmsh, LaGriT) to generate the mesh by importing and converting it to a TOUGH mesh, which also conveniently facilitates the coupling of TOUGH with any other simulator that also supports the same mesh formats. Besides, `toughio` is designed to be just a TOUGH input and output file reader/writer. It assumes that the user knows what he is doing and does not verify the validity of the model parameters (`toughio` does verify the functional arguments).

Luu, Keurfon↗
DOE OSTI2021

Experimental Images and Videos of Foam Stability (Half-life)

The experimental data obtained in this project is the thermal stability data of various foams measured using the setup established at Temple University during this study. The setup is installed with a portable digital camera which can take images and videos of foam evolution at a given pressure and temperature condition. Consequently, the half-life data was recorded from the images/videos, which are used as a measure of the thermal stability for foams. Over the 3 years of this project, four different surfactants and five different stabilizing agents were studied. The surfactants are, Alfa Olefin Sulfonate (AOS), Sodium Dodecyl Sulphate (SDS), Tergitol (NP-40), and Cetyltrimethylammonium chloride (CTAC). The stabilizing agents are, guar gum, bentonite clay, crosslinking agents, silicon dioxide nanoparticles (60 to 70nm), and graphene oxide dispersions. Foam stability was evaluated at different temperatures between 100C and 200cC, while the foam generation pressure varied between atmospheric pressure (14.7 psi) and 1000 psi. The images are saved as .jpg file and videos are saved as .avi files.

15 GEOTHERMAL ENERGY↗
DOE OSTI2026

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

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

colloids↗
DOE OSTI2020

Predictive Inverse Model for Advective Heat Transfer in a Short–Circuited Fracture: Dimensional Analysis, Machine Learning, and Field Demonstration

Identifying fluid flow maldistribution in planar geometries is a well–established problem in subsurface science/engineering. Of particular importance to the thermal performance of enhanced (or “engineered”) geothermal systems is identifying the existence of nonuniform (i.e., heterogeneous) permeability and subsequently predicting advective heat transfer. Here, machine learning via a genetic algorithm (GA) identifies the spatial distribution of an unknown permeability field in a two–dimensional Hele–Shaw geometry (i.e., parallel plates). The inverse problem is solved by minimizing the L2 norm between simulated residence time distribution (RTD) and measurements of an inert tracer breakthrough curve (BTC) (C–Dot nanoparticle). Principal component analysis (PCA) of spatially correlated permeability fields enabled reduction of the parameter space by more than a factor of 10 and restricted the inverse search to reservoir–scale permeability variations. Thermal experiments and tracer tests conducted at the mesoscale Altona Field Laboratory (AFL) demonstrate that the method accurately predicts the effects of extreme flow channeling on heat transfer in a single bedding–plane rock fracture. However, this is only true when the permeability distributions provide adequate matches to both tracer RTD and frictional pressure loss. Without good agreement to frictional pressure loss, it is still possible to match a simulated RTD to measurements, but subsequent predictions of heat transfer are grossly inaccurate. Here, the results of this study suggest that it is possible to anticipate the thermal effects of flow maldistribution, but only if both simulated RTDs and frictional pressure loss between fluid inlets and outlets are in good agreement with measurements.

42 ENGINEERING↗
DOE OSTI2024

Efficient data-driven models for prediction and optimization of geothermal power plant operations

Increasing the capacity of geothermal energy as a renewable resource calls for development and deployment of efficient control and optimization technologies for geothermal power plants. A data-driven prediction and optimization model is presented as a cost-effective and efficient alternative to physics-based approach. The model predicts power output and operational cost by propagating the influence of control and disturbance variables within an artificial neural network (ANN). Numerical experiments with simulated and field data from a real geothermal power plant are first used to demonstrate the prediction performance of the ANN model. The model is then adopted to maximize the net predicted power production by automatically adjusting the working fluid circulation rate. The optimization performance of the model in evaluated using a thermodynamic flowsheet simulation model. The workflow is applied to model and control the effect of ambient temperature on an air-cooled binary cycle power plant, which is complex and costly to perform using a physics-based predictive model. As a result, the performance of the method is demonstrated by applying it to both simulated and field datasets from a binary cycle geothermal power plant.

15 GEOTHERMAL ENERGY↗
DOE OSTI2023

Utah FORGE: Documentation on Discrete Fracture Network and Fracture Propagation Modelling

This dataset includes reports and a slide presentation on discrete fracture network (DFN) generation and hydraulic fracture modeling at the Utah FORGE site. It details the characterization of natural fractures using well log and core data, as well as stochastic modeling techniques. The reports describe simulations of hydraulic fracture propagation, fluid-mechanical interactions, and induced microseismicity. The dataset also includes history-matching of net pressure and analyses of fracture growth in naturally fractured geothermal reservoirs. The slides summarize key findings and future research directions.

15 GEOTHERMAL ENERGY↗
DOE OSTI2024

Closing the Loop Between In Situ Stress Complexity and EGS Fracture Complexity (Quarterly Research Performance Progress Report: Q3Y3)

As part of Task 1, we have started by testing our modeling capabilities by reproducing isothermal DFIT simulations presented in the literature. To start, we have been working towards adjusting numerical parameters to reproduce DFIT tests with different fracture geometries. Then, we have focused modeling the DFIT conducted at well 58-32 (cycle 4 in zone 2). Specifically, we have carefully analyzed the influence of the aperture-contact stress relationship and investigated how two different laws affect model results. We have also introduced thermal effects in the model and rerun the simulations to estimate the effect of cooling on the DFIT response. Due to the small amount of fluid injected during a DFIT, thermal effects seem to be moderate.

15 GEOTHERMAL ENERGY↗
NASA NTRS2015

Clays and Carbonates in a Groundwater-Fed 3.8 Ga Martian Lake: Insights to Subsurface Habitability on Mars

On Earth, the deep biosphere remains a largely unexplored, but clearly important carbon reservoir. Results from some uplifted central peaks in craters on Mars indicate that substantial carbon was also present at depth and might have helped sustain a deep biosphere. In fact, many factors relevant to deep biosphere habitability are more favorable on Mars than on Earth (e.g. porosity of the crust, geothermal gradient). Future exploration of Mars should include landing sites where materials have been exhumed from depth by meteor impact or basins where subsurface fluids have emerged, carrying clues to subsurface habitability. One of the most astrobiologically interesting sites on Mars McLaughlin Crater, a 93 km-diameter impact crater that formed approximately 4 b.y. ago. On the floor of the crater is a stratigraphic section of subhorizontal, layered sedimentary rocks with strong spectroscopic evidence for Fe-rich clay minerals and Mg-rich carbonates, which we interpret as ancient lacustrine deposits. The fluids that formed these materials likely originated in the subsurface, based on the paucity of channels leading into the crater basin and the fact that this is one of the deepest basins on Mars - a good candidate to have experienced upwelling of subsurface fluids. Therefore, the deposits within McLaughlin crater provide insight into subsurface processes on Mars. In this presentation, we will discuss the habitability of the martian subsurface as well as the geology of McLaughlin Crater and the possibility to detect biomarkers at that site with a future landed mission.

Michalski, Joseph↗