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At least 217 records · Page 12

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DOE OSTI2025

A quantitative risk assessment framework for fault reactivation in underground hydrogen storage: Coupled simulation and deep learning approach

Underground hydrogen storage (UHS) is emerging as a critical solution for large-scale energy storage. However, like all subsurface fluid injection activities, UHS poses the risk of injection-induced fault reactivation. Accurate risk assessment is essential to ensuring the safety and efficiency of UHS operations. This study presents the development of deep-learning surrogate models for fault reactivation prediction in UHS, trained on a comprehensive database of fully coupled fluid flow-geomechanics simulations. Our findings reveal that analytical models often yield unreliable estimates, with errors up to 54% in the allowable injection pressure, potentially leading to a 40% reduction in UHS operational capacity. The developed surrogate models were incorporated into a quantitative risk assessment (QRA) framework, enabling probabilistic evaluation of fault reactivation risk while accounting for uncertainties in the input variables. Site-specific features, such as horizontal stress gradients, fault’s dip and strike angles, and operational parameters like bottom-hole injection pressure and well-fault distance, were identified as the primary drivers of fault reactivation across various stress regimes. Whereas other hydraulic, geological, and poroelastic reservoir properties were found to have a secondary impact. Notably, we observed that the risk of fault reactivation for a critically oriented fault with a static friction coefficient greater than 0.55 remains below 10% in a normal faulting stress regime. However, the risk significantly increases as the stress regime transitions from normal to strike-slip and ultimately to reverse faulting conditions. These findings underscore the importance of rigorous site characterization and comprehensive QRA evaluations to optimize UHS performance and minimize geomechanical risks.

25 ENERGY STORAGE↗

DOE OSTI2020

Optimizing Cornell’s future geothermal district heating performance through systems engineering and simulation

Cornell University’s intention to lower its carbon footprint has motivated this engineering evaluation of using low-temperature geothermal energy to supply heat to the campus district energy system. Optimal selection and operation of heat pumps can significantly improve system performance. To support this analysis, we model the quantitative relationships between heat pump configurations and source flow, supply and distribution temperatures, facility heating design options, output heat generation, and carbon abatement outcomes. A systems approach is used for analysis, troubleshooting, and improvement of the model. The result of this effort is a dynamic tool that appropriately responds to hourly thermal demand and communicates energy response for techno-economic analysis. Simulations indicate that a single well-pair in the local Basement Contact Zone reservoir can satisfy almost 68% of annual thermal demand at nearly 20 MWth average capacity while remaining financially competitive compared to conventional heating with levelized cost of heating (LCOH) as low as $4.55/MMBTU ($15.53/MWh th ). A direct-use scheme with heat pump augmentation provided more carbon abatement and 50 to 150% more annual heat supply than one without augmentation. Exploration of three and four well-pair scenarios prove the capability to achieve 50 MW th baseload heating capacity with competitive LCOH and significant carbon reductions.

15 GEOTHERMAL ENERGY↗

DOE OSTI2022

Identifying Hydrometeorological Factors Influencing Reservoir Releases Using Machine Learning Methods

Simulation of reservoir releases plays a critical role in social-economic functioning and our nation's security. How-ever, it is challenging to predict the reservoir release accurately because of many influential factors from natural environments and engineering controls such as the reservoir inflow and storage. Moreover, climate change and hydrological intensification causing the extreme precipitation and temperature make the accurate prediction of reservoir releases even more challenging. Machine learning (ML) methods have shown some successful applications in simulating reservoir releases. However, previous studies mainly used inflow and storage data as inputs and only considered their short-term influences (e.g, previous one or two days). In this work, we use long short-term memory (LSTM) networks for reservoir release prediction based on four input variables including inflow, storage, precipitation, and temperature and consider their long-term influences. We apply the LSTM model to 30 reservoirs in Upper Colorado River Basin, United States. We analyze the prediction performance using six statistical metrics. More importantly, we investigate the influence of the input hydrometeorological factors, as well as their temporal effects on reservoir release decisions. Results indicate that inflow and storage are the most influential factors but the inclusion of precipitation and temperature can further improve the prediction of release especially in low flows. Additionally, the inflow and storage have a relatively long-term effect on the release. These findings can help optimize the water resources management in the reservoirs.

Fan, Ming↗

DOE OSTI2026

A Boundary Element Model for Assessing Large‐Scale Pressurization in Faulted Geological Storage Systems

Assessing large-scale pressurization at the regional scale—a possible outcome of large subsurface storage applications such as wastewater injection and geological carbon sequestration—presents significant computational challenges. These challenges are particularly pronounced when accounting for complex geologic structures with multiple reservoir and caprock layers, fault zones, and wells. This study introduces a computationally efficient model that integrates single-phase semi-analytical solutions with a boundary element (BE) approach. The model simulates pressure propagation in multilayered 3D systems, including vertical faults, caprock, basement, and confining units. We apply this new model to a representative scenario involving CO 2 injection near a partially sealing fault with verification against an independent two-phase flow model. Results demonstrate that our model accurately captures far-field pressure responses and that, outside the CO 2 plume zone, pressure predictions from single-phase and two-phase models are nearly identical. This supports the use of single-phase models like ours for efficient estimation of far-field pressure changes. Additionally, we demonstrate its effectiveness at a large scale, incorporating multiple wells and faults. With its ability to represent multiple wells, fault zones, and geological heterogeneity, our model is well suited for assessments of basin-scale pressurization. Its computational efficiency also makes it a promising tool for integration with optimization frameworks aimed at designing and managing injection strategies in faulted storage systems.

Cihan, A. [Lawrence Berkeley National Laboratory (↗

DOE OSTI2026

Geochemical evolution in Cacapon member: Fluid-rock interaction experiments and model insights for Appalachian Basin geothermal development

Here, this study combines recirculated flow-through experimental results conducted for 17 days at 90C and 200 PSI with reactive transport modeling to estimate fluid-rock interactions occurring in a sandy mudstone using an interbedded sandstone-shale sample from the Cacapon Member of the upper Tuscarora Sandstone/lower Rose Hill Formation for the purpose of geothermal exploration. Results suggest that the fluid and rock are likely to be in or near partial-equilibrium after approximately one year. In addition, after >400 h of continuous injection at 0.05 mL/min (5*10 −8 m 3 /min) the reactive front is restricted to the first ∼13% of the 4 cm*1.6 cm experimental rock length, whereas after >9100 h (∼1 year), the reactive front extends to 30% total length. The rate of changes in dissolution or precipitation are however, very minimal, with all major rock-forming minerals having rates <10 −11 mol/L porous media/s. Reservoir rock in the presence of dilute brine as may be the case during operation of an enhanced geothermal system would experience little alteration during the shut-in phase, and possibly up to one year. These results have utility in geothermal exploration for reservoirs at similar temperatures as well as general fluid-sandy mudstone rock interaction geochemistry.

Appalachian Basin↗

DOE OSTI2025

Three-Dimensional Pore Networks in Miocene Stevens Sandstone of California: Implications for CO 2 Geologic Storage

The Miocene Stevens Sandstone in the San Joaquin Basin of California is increasingly recognized as a promising candidate for CO 2 geological storage due to the enormous storage capacity, proven sealing, and existing infrastructure. In this study, computed microtomography imaging and pore network modeling were employed to investigate the influence of pore geometry and wettability on the CO 2 injectivity and residual trapping. Image analysis revealed that a significant fraction of the cement and matrix consists of microporous regions. The microporosity can substantially increase the overall pore space, yet its contribution to permeability remains modest, particularly in samples with low permeability. The intrinsic heterogeneity of turbidite reservoirs further complicates the reservoir properties among different layers. Two-phase flow simulations under varying wettability conditions (water-wet, weak water-wet, and neutral-wet) demonstrated that the CO 2 injection is predominantly controlled by macropores. CO 2 invades microporous regions only after these larger pores are filled. The presence of microporosity leads to a decrease in both initial and residual CO 2 saturations, with the magnitude of the reduction being influenced by wettability. Neutral-wet scenarios exhibit higher CO 2 mobility and thus lower residual trapping than water-wet scenarios. The results imply that heterogeneity in pore geometry and cement distribution across different layers can result in stratified CO 2 flow pathways, complicating efforts to predict injection performance. Overall, the Stevens Sandstone shows considerable promise for CO 2 geologic storage, but effective implementation will require detailed characterization of the pore structure as well as the integration of reactive fluid flow to account for potential mineral dissolution and fines migration.

fluids↗

DOE OSTI2023

Coupled Investigation of Fracture Permeability Impact on Reservoir Stress and Seismic Slip Behavior

Our goal is to develop, apply and validate a holistic thermal, hydrologic, mechanical, and chemical (THMC) workflow that includes evaluation of induced seismic slip in EGS reservoirs. We will integrate experimental and modeling approaches to reduce parameter uncertainty and better predict/mitigate seismic hazard at EGS sites. Our novel approach couples 3D physics-based earthquake simulations with THMC models (THMc+E). This capability will enable improve engineering decisions at Utah-FORGE and move EGS operations toward repeatable, robust, economically viable, and socially accepted development. For example, our THMC+E models will predict circulation scenarios and related seismic hazard for a suite of flow rates and under uncertainty, thus enabling evaluation of optimal circulation strategy. Laboratory experiments will be performed to constrain key model parameters and Bayesian techniques will provide a probabilistic evaluation of parameters used in models. THMC+E simulations will enable exploration various circumstances that may hinder EGS success and develop mitigation strategies.

58 GEOSCIENCES↗

DOE OSTI2024

5-2428: Fracture Permeability Impact on Seismic Slip Behavior

58 GEOSCIENCES↗

DOE OSTI2025

Online monitoring of lanthanide species with combined spectroscopy in flowing aqueous aerosol systems

Combined spectroscopic analysis through absorption and laser-induced breakdown spectroscopy (LIBS) was used to monitor Nd and Pr concentrations in a flowing aqueous system. A unique, online sampling approach was employed that allowed for an approximately closed analysis loop of liquids from a reservoir. Absorption spectroscopy was performed with an optical flow cell, and LIBS was performed on an aerosol stream. Multivariate calibrations based on combined absorption and LIBS signals were built for Nd and Pr and then used to monitor concentrations in mixed solutions in a series of spiking tests. In these tests, the concentrations of Nd and Pr in solution were intermittently changed while spectroscopic signals were monitored in real-time. The combined spectroscopic signals and multivariate models were successful in monitoring changing concentrations of lanthanide species with high accuracy and minimal latency. Root-mean squared error of predictions were 0.015 mol L −1 and 0.019 mol L −1 for Nd and Pr respectively, and these lanthanides were able to be monitored to an accuracy of < 0.2 wt%. This work demonstrates both the capabilities of a sampling system for online analysis of liquids and the capabilities of multimodal spectroscopic characterization for real-time, continuous tracking of species in potentially hazardous liquid systems.

absorbance spectroscopy↗

DOE OSTI2022

Leakage From Coexisting Geologic Forcing and Injection‐Induced Pressurization: A Semi‐Analytical Solution for Multilayered Aquifers With Multiple Wells

Abstract Abnormal fluid pressures (above or below hydrostatic pressure) can develop and persist in sedimentary basins. The common occurrence of abnormal pressures may cause challenges for project permitting of geological carbon sequestration (GCS), particularly in reservoirs with pre‐injection overpressure. The leaky wells that may exist in some sedimentary basins can provide flow paths between deep brine aquifers and shallower freshwater aquifers. Pre‐injection relative overpressures can cause brine leakage through leaky wells even before any injection occurs. The tendency for flow through leaky wells is coupled with the process of pressure dissipation that occurs through aquitards. Specifically, with non‐zero permeability, aquitards can dissipate pressure over large areal extents and thereby reduce leakage rates through leaky wells. This study presents development of a semi‐analytical solution for hydraulic head and brine leakage in multilayered aquifer–aquitard systems with geologic pressure forcing. The geologic forcing that causes abnormal pressures in the multilayered system can coexist with any number of injection, extraction, and leaky wells that also affect fluid pressure. The semi‐analytical model is applied to explore how leakage through leaky wells varies as functions of pressurization rate, along with aquitard and leaky well properties in an overpressured multilayered system. The results show that although injection‐induced pressures can dissipate rapidly through suitably permeable aquitards, coexisting geologic forcing may create sustained rates of brine leakage into freshwater aquifers through leaky wells. In GCS, a very low‐permeability aquitard with high capillary entry pressure to free‐phase CO 2 is desired to serve as the caprock to prevent leakage of CO 2 from the storage reservoir. Nevertheless, the results from this study show that the brine leakage impact to shallow freshwater aquifers through leaky wells might substantially decrease with increasing aquitard permeability values, as long as small aquitard permeability and high capillary entry pressure serve to prevent CO 2 leakage.

Cihan, A.↗

DOE OSTI2026

Evaluating the Impacts of Coordination for Cascaded Hydropower Plants on the Mid-Columbia

The Mid-Columbia region in the U.S. Pacific Northwest includes seven large, cascaded hydropower plants with a combined nameplate capacity of nearly 14 GW. These plants have several different operators, and there is currently limited coordination among the operators. We develop a detailed model of the Mid-Columbia system that captures both river flow constraints and power system operations. We then place this detailed model within a production cost model of a 2030 system of the Western Interconnection to evaluate how coordination of the Mid-Columbia hydropower plants might impact operations throughout the interconnection. We find that coordination of the Mid-Columbia plants leads to more generation from the Mid-Columbia plants and increased revenues for the Mid-Columbia operators. This benefit is achieved primarily through better utilization of the storage reservoirs and decreased spill through the cascade. We also evaluate the impacts of look-ahead on production costs and find that longer look-ahead values result in lower system-wide production costs.

13 HYDRO ENERGY↗

DOE OSTI2020

River Regulation Alleviates the Impacts of Climate Change on US Thermoelectricity Production

Climate change and anthropogenic activities can both influence stream temperature, which is a critical indicator of thermoelectric power generation potential. Previous studies emphasized the vulnerability of power-generation systems to climate warming (ref 1, 2). Despite the fact that two-thirds of the global rivers are regulated by dams (ref 3), the relative impacts of water management and climate change on stream temperature and thermoelectric production have not yet been evaluated. Using an integrated modeling system applied to the conterminous United States, simulations confirm the dominant role of climate change in shaping the future trends of stream temperature. However, water management, reservoir regulation in particular, can mitigate the stream temperature warming and reduce the frequency and magnitude of extreme high stream temperatures through redistribution of the seasonal streamflow. Moreover, water management can alleviate the loss of power plant usable capacity from climate change to the level of reduced warming from emission mitigation. By regulating the flow and temperature regimes, water management is a promising adaptation measure to alleviate climate change induced effects in the water-energy nexus.

Zhang, Xiao↗

DOE OSTI2024

Stress Testing California's Hydroclimatic Whiplash: Potential Challenges, Trade‐Offs and Adaptations in Water Management and Hydropower Generation

Abstract Inter‐annual precipitation in California is highly variable, and future projections indicate an increase in the intensity and frequency of hydroclimatic “whiplash.” Understanding the implications of these shocks on California's water system and its degree of resiliency is critical from a planning perspective. Therefore, we quantify the resilience of reservoir services provided by water and hydropower systems in four basins in the western Sierra Nevada. Using downscaled runoff from 10 climate model outputs, we generated 200 synthetic hydrologic whiplash sequences of alternating dry and wet years to represent a wide range of extremes and transitional conditions used as inputs to a water system simulation model. Sequences were derived from upper (wet) and lower (dry) quintiles of future streamflow projections (2030–2060). Results show that carryover storage was negatively affected in all basins, particularly in those with lower storage capacity. All basins experienced negative impacts on hydropower generation, with losses ranging from 5% to nearly 90%. Reservoir sizes and inflexible operating rules are a particular challenge for flood control, as in extremely wet years spillage averaged nearly the annual basins' total discharge. The reliability of environmental flows and agricultural deliveries varied depending on the basin, intensity, and duration of whiplash sequences. Overall, wet years temporarily rebound negative drought effects, and greater storage capacity results in higher reliability and resiliency, and lesser volatility in services. We highlight potential policy changes to improve flexibility, increase resilience, and better equip managers to face challenges posed by whiplash while meeting human and environmental needs.

Environmental Sciences & Ecology↗

DOE OSTI2024

Fracture Characterization Via AI‐Assisted Analysis of Temperature Logs

Abstract Fractures control fluid flow, mass transport, and heat transfer in a geothermal reservoir. This makes accurate characterization of fracture networks a prerequisite for optimal design and control of a reservoir's exploitation. We develop a deep‐learning procedure to identify fracture locations via interpretation of temporally and spatially continuous downhole temperature measurements. A long short‐term memory fully convolutional network (LSTM‐FCN) is used both to capture long‐term dependencies in sequential temperature data and to distill local features around fractures. A wellbore and fractured‐reservoir thermal model is established to generate temperature data for network training. The trained LSTM‐FCN exhibits a unique ability to detect multiple fractures intersecting a borehole. We use the LSTM‐FCN algorithm to evaluate the effectiveness of different‐stage wellbore temperature measurements on fracture detection in a complex fractured system. Our experiments reveal that the use of various‐stage temperature information as an input feature set improves the robustness of fracture detection to noise interference. This study indicates the practical feasibility of obtaining accurate fracture‐network reconstructions from temperature signals, at reasonable computational cost.

Yang, Xiaoyu↗

DOE OSTI2021

Experimental Study of Microgel Conformance-Control Treatment for a Polymer-Flooding Reservoir Containing Superpermeable Channels

Summary Polymer flooding has been widely used to improve oil recovery. However, its effectiveness would be diminished when channels (e.g., fractures, fracture-like channels, void-space conduits) are present in a reservoir. In this study, we designed a series of particular sandwich-like channel models and tested the effectiveness and applicable conditions of micrometer-sized preformed particle gels (PPGs, or microgels) in improving the polymer-flooding efficiency. We studied the selective penetration and placement of the microgel particles, and their abilities for fluid diversion and oil-recovery improvement. The results suggest that polymer flooding alone would be inefficient to achieve a satisfactory oil recovery as the heterogeneity of the reservoir becomes more serious (e.g., permeability contrast kc/km > 50). The polymer solution would vainly flow through the channels and leave the majority of oil in the matrices behind. Additional conformance-treatment efforts are required. We tried to inject microgels in an attempt to shut off the channels. After the microgel treatment, impressive improvement of the polymer-flooding performance was observed in some of our experiments. The water cut could be reduced significantly by as high as nearly 40%, and the sweep efficiency and overall oil recovery of the polymer flood were improved. The conditions under which the microgel-treatment strategy was effective were further explored. We observed that the microgels form an external impermeable cake at the very beginning of microgel injection and prevent the gel particles from entering the matrices. Instead, the microgel particles could selectively penetrate and shut off the superpermeable channels under proper conditions. Our results suggest that the 260-µm microgel particles tested in this study are effective to attack the excessive-water-production problem and improve the oil recovery when the channel has a high permeability (>50 darcies). The gels are unlikely to be effective for channels that are less than 30 darcies because of the penetration/transport difficulties. After the gels effectively penetrate and shut off the superpermeable channel, the subsequent polymer solution is diverted to the matrices (i.e., the unswept oil zones) to displace the bypassed oil. Overall, this study provides important insights to help achieve successful polymer-flooding applications in reservoirs with superpermeable channels.

Engineering↗

DOE OSTI2023

Techno-Economic Analysis for a Potential Geothermal District Heating System in Tuttle, Oklahoma: Preprint

Geothermal deep direct use (DDU) has potential across a wide swath of the United States but is underutilized due to challenging project economics associated with developing a deep geothermal resource for a large-scale and variable heat demand. The National Renewable Energy Laboratory (NREL) and University of Oklahoma evaluated the feasibility of a geothermal district heating (GDH) and cooling system in two schools and 250 houses by utilizing existing oil and gas (O&G) wells in Tuttle, Oklahoma. Heating and cooling demand in the two schools and a typical single-family house were modeled using EnergyPlus building energy simulation software. The modeling results indicated that annual heating demand in two schools and 250 houses is approximately 2.61 GWhth, and cooling demand in the two schools is approximately 2.65 GWhth. In this scope, the techno-economic analysis (TEA) was conducted using the GEOPHIRES tool combined with the TOUGH2 reservoir simulator. The reservoir performance, including geothermal heat production capacity, was modeled by the reservoir simulator TOUGH2. Then, levelized cost of heat (LCOH) was calculated using GEOPHIRES version 3.0, which includes new features such as hourly heat load optimization and peak performance evaluation. Geothermal reservoir temperature was estimated as 90.5 degrees C at a total depth of 3.3 km by the regional average temperature gradient of 22.8 degrees C/km and validated by cation geothermometer calculations. Four production scenarios with two different well configurations and two different heat load profiles have been developed for well flow rates ranging between 3.1 kg/s and 9.3 kg/s. The LCOH of the district heating and cooling system was calculated between $95 and $210/MWh ($28/MMBtu to $62/MMBtu) for four different production scenarios. Typical natural gas prices for residential customers in Oklahoma have ranged from 9 to 19 $/MMBtu over the past decade, which indicates a challenge for deployment of such a GDH system.

deep direct-use↗

DOE OSTI2023

Techno-Economic Analysis for a Potential Geothermal District Heating System in Tuttle, Oklahoma

deep direct-use↗

DOE OSTI2026

Xanthos-Lake Model Source Code

This repository contains the source code for Xanthos-Lake, a lake-modeling extension of the Xanthos framework that introduces a coupled lake component comprising the Xanthos-Lake Snow and Ice Model (xLSIM) and the Xanthos-Lake Water Balance Model (xLWBM). xLSIM is a basin-aware machine-learning model for lake snow, ice, and thermal conditions. It predicts monthly lake ice thickness, snow depth, snow-cover fraction, mixing-layer temperature, and lake ice fraction from meteorological forcing and lake surface-area information. It uses sequence-based deep-learning architectures, including Transformer and hybrid Long Short-Term Memory–Transformer (LSTM–Transformer) models, together with seasonal encoding, multi-lake learning, physical masking, and basin-level cryospheric and non-cryospheric classification. The training workflow uses Ray for scalable execution and includes optional Ray Tune hyperparameter optimization. Model predictions, observations, diagnostics, and feature-importance outputs are written in NetCDF. xLWBM is the water-balance component of the new lake framework. It simulates monthly lake storage, surface area, evaporation, inflow, outflow, and lake–groundwater exchange. It combines physical water-balance equations with calibrated bathymetric relationships, weir-based outlet flow, modified Penman open-water evaporation, groundwater head relaxation, Penman–Monteith snow and ice sublimation, and snow, ice, and thermal conditions supplied by xLSIM. The model calibrates lake parameters against satellite-derived surface-area data, using evaporation-based calibration where surface-area data are unavailable, and supports small, medium, and large lake classes. For large lakes, xLWBM is integrated with the managed-routing workflow so that lake storage and outflow interact directly with downstream river routing and reservoir operations. Together, xLSIM and xLWBM provide Xanthos with a coupled lake-modeling capability. xLSIM supplies the snow, ice, and thermal conditions that affect lake evaporation and snow- and ice-related water exchanges, while xLWBM translates those conditions into dynamic lake storage, surface area, evaporation, and discharge. In return, xLWBM supplies evolving lake surface area to xLSIM. This coupling enables Xanthos to represent lakes as active hydrologic components within basin-scale water-availability and routing simulations.

Machine Learning↗