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DOE OSTI2023

From Well Log to Formation Model: A Novel Laboratory Calibrated Methodology with Demonstration

This work demonstrates how the characterization and modeling of both elastic and creep properties are essential to describe zones in layered rock formations as either low-stress targets for stimulation or high-stress barriers to fracture growth. Prediction of fracture height is critical for designing stimulation operations in oil and gas wells. Ideally, fractures are placed in target zones which will produce hydrocarbons and should not propagate into zones expected to be unproductive or to produce unwanted fluids such as water which in turn must be treated and/or disposed. The essential task in designing stimulation plans is predicting which zones have low horizontal stresses and which will be high-stress barriers to fracture growth. Despite this importance, there are gaps in current knowledge and a complete workflow from laboratory characterization to a finite element model which includes time dependent rock deformation is required. While the research and methodology presented here also have application to CO 2or hydrogen storage, wastewater injection, and geothermal applications, the focus will be on hydrocarbon extraction. This thesis presents the results of a characterization-to-prediction workflow for the Caney shale, which is an emerging hydrocarbon resource in Oklahoma, USA. It begins with an investigation to enable critical evaluation of the Caney zonation into nominally “brittle” and “ductile” zones based on properties observed from well logs. It shows none of the zones are consistently “brittle” or “ductile” mechanical behavior based on the variety of definitions of these terms. However, the nominally ductile zones are weaker and more prone to creep. A laboratory investigation of samples including strength, elastic, and creep properties, is then used in a finite element model of stress evolution. The model includes both elastic deformation and viscoplastic creep. Results predict the least creep-prone layers to have the lowest horizontal stresses, therefore comprising hydraulic fracturing targets. The most creep-prone layers attain a horizontal stress similar to the vertical stress and therefore are predicted to be high stress barriers to hydraulic fracture stimulation. In addition to defining stimulation target intervals, the model shows how as tectonic strain rate increases, there is a transition from creep-dominated stresses to stresses dominated by elasticity.

Benge, Margaret↗

DOE OSTI2021

An HPC-Based Hydrothermal Finite Element Simulator for Modeling Underground Response to Community-Scale Geothermal Energy Production

Geothermal heat, as renewable energy, shows great advantage with respect to its environmental impact due to its significantly lower CO2 emissions than conventional fossil fuel. Open and closed-loop geothermal heat pumps, which utilize shallow geothermal systems, are an efficient technology for cooling and heating buildings, especially in urban areas. Integrated use of geothermal energy technologies for district heating, cooling, and thermal energy storage can be applied to optimize the subsurface for communities to provide them with multiple sustainable energy and community resilience benefits. The utilization of the subsurface resources may lead to a variation in the underground environment, which might further impact local environmental conditions. However, very few simulators can handle such a highly complex set of coupled computations on a regional or city scale. We have developed high-performance computing (HPC) based hydrothermal finite element (FE) simulator that can simulate the subsurface and its hydrothermal conditions at a scale of tens of km. The HPC simulator enables us to investigate the subsurface thermal and hydrologic response to the built underground environment (such as basements and subways) at the community scale. In this study, a coupled hydrothermal simulator is developed based on the open-source finite element library deal.II. The HPC simulator was validated by comparing the results of a benchmark case study against COMSOL Multiphysics, in which Aquifer Thermal Energy Storage (ATES) is modeled and a process of heat injection into ATES is simulated. The use of an energy pile system at the Treasure Island redevelopment site (San Francisco, CA, USA) was selected as a case study to demonstrate the HPC capability of the developed simulator. The simulator is capable of modeling multiple city-scale geothermal scenarios in a reasonable amount of time.

Sun, Xiang↗

DOE OSTI2021

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

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

58 GEOSCIENCES↗

DOE OSTI2024

Utah FORGE 3-2535: Joint Imaging of Fracture Growth and Estimation of Fracture Properties During EGS Development - 2024 Annual Workshop Presentation

This is a presentation on the Joint Electromagnetic/Seismic/InSAR Imaging of Spatial-Temporal Fracture Growth and Estimation of Physical Fracture Properties During EGS Resource Development by Lawrence Berkeley National Laboratory, presented by David Alumbaugh. This is a video presentation on developing a set of technologies and workflow to image induced fracture generation and growth for an Enhanced Geothermal System (EGS). Using a combination of passive seismic and active source borehole EM and InSAR technology it is anticipated imaging and fracture generation and growth monitoring will be actualized. The workflow will provide a template for imaging induced fracture systems for future EGS systems. This presentation was featured in the Utah FORGE R&D Annual Workshop on August 13, 2024.

15 GEOTHERMAL ENERGY↗

NASA NTRS1974

Energy: An annotated bibliography

This bibliography is a compilation of approximately 4,300 selected references on energy and energy related topics. The references are arranged by date, with the latest works first, in the following subject categories: (1) energy and power - general; resources, supply/demand, and forecasting; policy, legislation, and regulation; research and development; environment; consumption and economics; and conservation, (2) energy and power sources - general, fossil fuels, hydrogen and methanol, organic wastes and waste heat, nuclear, geothermal, solar, wind, ocean/water, magnetohydrodynamics and electrohydrodynamics, and gas and steam turbines, and (3) energy and power storage and transmission. Literature from bibliographic sources dated January 1972 through July 1974 is covered, with some pertinent literature prior to 1972 included.

Blow, S. J.↗

DOE OSTI2021

Wellbore integrity assessment with casing-based advanced sensing

Wellbore integrity is of paramount importance to subsurface resource extraction, energy storage 1 and waste disposal. After installation, well casing and cement are subject to mechanical stress due to near-well pressure changes and fluid induced corrosion. This is exacerbated for geothermal wells where produced fluid is at high temperature and corrosive. The current state-of-the-art technologies for wellbore integrity assessments are an array of cased hole logging tools. Wireline deployed acoustic, electromagnetic and mechanical tools are all available to inspect steel casing corrosion and casing-cement bond and these tools can provide high-resolution assessment of borehole conditions. They are intrusive, however, in terms of borehole preparation and interruption to the normal operation of the wells, and not suitable for high temperature or highly deviated well deployments. In addition, these measurements are performed infrequently due to high cost, and are therefore incapable of providing frequent data to better predict borehole degradation trajectory, which can help provide early warning of potential borehole failures. For this project we are developing a suite of novel, non-invasive, casing based tools for wellbore integrity assessment, combining fast/low cost screening with higher-precision investigation. Our approach is based on monitoring the response of the casing when energized at the wellhead, thereby interrogating the casing without well intervention. Lab, field and numerical approaches are used in our study. During the early stage of the research, we focus on numerical simulations, which have shown the sensitivity of the low frequency electromagnetic (EM) signals to changes in borehole depths and have successfully tested the concept at a field site with different length well casings. Initial seismic modeling efforts have also demonstrated our capability to simulate seismic tube wave and seismic field alterations due to borehole breakage and associated fluid leakage. Further numerical, laboratory and field experiments are underway for additional technology sensitivity analysis, particularly the transient EM/Seismic reflectometry methods, data acquisition optimization, and numerical simulation improvements.

Wilt, Michael↗

DOE OSTI2023

GOOML (Geothermal Operational Optimization with Machine Learning) [SWR-23-01]

The Geothermal Operational Optimization with Machine Learning (GOOML) is a partnership between NREL and Upflow, NZ, awarded in response to the U.S. Department of Energy's Geothermal Technologies Office's Funding Opportunity Announcement (FOA) to expand the role of advanced analytics and automation in geothermal operations through machine learning. Partnering with industry (Contact Energy Limited ("Contact"), Ngati Tuwharetoa Geothermal Assets Limited ("NTGA"), Ormat Technologies Inc. ("Ormat") and Flow State Solutions Limited ("FSS"), GOOML was created to improve the operational efficiency of geothermal power plant steam fields through the analysis of historical operational data and the application of custom machine learning algorithms. NREL's contributions include machine learning, coding, and data management expertise as well as access to high-performance compute solutions. GOOML can increase geothermal operational efficiency through development of a digital system twin that can be utilized to provide optimal geothermal operating conditions for real-world geothermal fields. GOOML allows users to analyze field production histories in detail, develop models, and train machine learning algorithms to identify opportunities for increased geothermal efficiency, detect potential trouble, and allow predictive scenario modeling. Preliminary experiments have demonstrated a potential to increase total generation by as much as 12% through ML optimization of the utilization of existing steam field resources.

Buster, Grant↗

DOE OSTI2020

Utah FORGE: Data for 3-D Model Development - Lithology, Temperature, Pressure, and Stress

This submission comprises two downloadable .zip archives. Each archive contains spreadsheets with 3-D data and a .txt document describing the data. The Mesh Files .zip download contains data regarding the lithologic contacts of the granitoid and overlying sedimentary basin fill. The Initial Conditions .zip download contains data regarding temperature, pressure, and stress. These data were generated during Utah FORGE Phase 2 and exported from Seequent Leapfrog Geothermal. They are intended for use in 3-D earth modeling.

15 GEOTHERMAL ENERGY↗

DOE OSTI2025

Reflections on the role of MOOSE in Unleashing a Golden Era of American Energy Innovation

As the use of artificial intelligence spreads across most professions, projections for the expected energy consumption of these virtual assistants/workers dictate a large increase in energy production. Recent published estimates refer to up to a necessary tripling of nuclear energy production~\cite{iaea}. % TODO add citations This is supported by the new administration, calling on February 5th to "unleash Commercial Nuclear Power in the United States" and to launch the "long-awaited American nuclear renaissance"~\cite{doe-order}. The Department of Energy (DOE) is tasked with "enabl[ing] the rapid deployment and export of next generation nuclear technology". In terms of research of development, we must seek to "unleash American Energy Innovation", by "focus[ing] our time and resources on technologies that will advance basic science, grow America’s scientific leadership, reduce costs for American families [...]". To that end, the Department will "prioritize affordable, reliable, and secure energy technologies, including fossil fuels, advanced nuclear, geothermal, and hydropower." Following this call to action, we elect to examine the role of the open-source Multiphysics Object Oriented Simulation Environment (MOOSE)~\cite{moose3} in meeting the stated goals.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗

NASA NTRS1973

Satellite geological and geophysical remote sensing of Iceland: Preliminary results of geologic, hydrologic, oceanographic, and agricultural studies with ERTS-1 imagery

The author has identified the following significant results. The wide variety of geological and geophysical phenomena which can be observed in Iceland, and particularly their very direct relation to the management of the country's natural resources, has provided great impetus to the use of ERTS-1 imagery to measure and map the dynamic natural phenomena in Iceland. MSS imagery is being used to study a large variety of geological and geophysical eruptive products, geologic structure, volcanic geomorphology, hydrologic, oceanographic, and agricultural phenomena of Iceland. Some of the preliminary results from this research projects are: (1) a large number of geological and volcanic features can be studied from ERTS-1 imagery, particularly imagery acquired at low sun angle, which had not previously been recognized; (2) under optimum conditions the ERTS-1 satellite can discern geothermal areas by their snow melt pattern or warm spring discharge into frozen lakes; (3) various maps at scales of 1:1 million and 1:500,000 can be updated and made more accurate with ERTS-1 imagery; (4) the correlation of water reserves with snowcover can improve the basis for planning electrical production in the management of water resources; (5) false-color composites (MSS) permitted the mapping of four types of vegetation: forested; grasslands, reclaimed, and cultivated areas, and the seasonal change of the vegetation, all of high value to rangeland management.

Williams, R. S., Jr.↗

NASA NTRS1975

Energy: An annotated bibliography

This bibliography is the first update of a previous energy bibliography dated August 1974. It contains approximately 3,300 selected references on energy and energy related topics from bibliographic sources dated August 1974 through December 1974. The references are arranged by date, with the latest works first, in subject categories. (1) Energy and power - general; resources, supply/demand, and forecasting; policy, legislation, and regulation; research and development, environment; consumption and economics; conservation; and systems analysis. (2) Energy and power sources - general; fossil fuels; hydrogen and other fuels; organic wastes and waste heat; nuclear; geothermal; solar; wind; ocean/water; magnetohydrodynamics and electrohydrodynamics; and gas and steam turbines. (3) Energy and power storage and transmission.

Blow, S. J.↗

DOE OSTI2023

Utah FORGE: Fault Reactivation Through Fluid Injection Induced Seismicity Laboratory Experiments

Included are results from shear reactivation experiments on laboratory faults pre-loaded close to failure and reactivated by the injection of fluid into the fault. The sample comprises a single-inclined-fracture (SIF) transecting a cylindrical sample of Westerly granite. All experiments are conducted at ambient temperature and follow a similar protocol: (i) application of confining stresses (3MPa) on the fault fully saturated with DI water, (ii) shear-mobilization through the increase of axial loading at a constant displacement rate until a post-peak steady-state condition is reached, (iii) reduction of axial loading and related shear stress to a prescribed fraction of the peak steady-state frictional strength (typically 60% to 90%, representing intermediate to high magnitudes) and (iv), fault reactivation triggered by a stepwise increase of pore pressure on the fault in 0.1 MPa increments held constant for 1-5 minutes. Mechanical data from three ISCO pumps connected to a Temco pressure vessel measure axial, confining, and fault-related parameters, including fluid pressure (kPa), fluid flow rate (mL/min), and axial displacement (mm). See included code for initial data analysis and visualization for select experiments. Resource names represent experiment numbers found in the "Read Me" file, which describes each experimental setup and parameters.

15 GEOTHERMAL ENERGY↗

DOE OSTI2022

Innovative Thermal Strategies: Electrification's Best Friend

Because transportation and heat are the most significant contributors to greenhouse gas (GHG) emissions in the northern U.S., electrification of transport and heat are prominent elements of many decarbonization plans, such as that of New York State (NYS). A key challenge is then to decarbonize the electric supply and distribution system ("the grid") that supplies electricity, a process colloquially known as "greening the grid", so that the saved direct carbon emissions are not offset by additional indirect emissions from electrical generation on the grid. Applying this "simple" decarbonization plan to locations with high winter heating loads and modest renewable resources is challenging. Understanding the impact of thermal system design to grid demands in these locations and the related marginal power source for electrification options is critical to allocating proper resources to solve decarbonization challenges and quantifying the resultant carbon emissions impact.

battery storage↗

DOE OSTI2022

Evaluation of accuracy and convergence of numerical coupling approaches for poroelasticity benchmark problems

Accurate modeling of subsurface flow and transport processes is vital as the prevalence of subsurface activities such as carbon sequestration, geothermal recovery, and nuclear waste disposal increases. Computational modeling of these problems leverages poroelasticity theory, which describes coupled fluid flow and mechanical deformation. Although fully coupled monolithic schemes are accurate for coupled problems, they can demand significant computational resources for large problems. In this work, a fixed stress scheme is implemented into the Sandia Sierra Multiphysics toolkit. Two implementation methods, along with the fully coupled method, are verified with one-dimensional (1D) Terzaghi, 2D Mandel, and 3D Cryer sphere benchmark problems. The impact of a range of material parameters and convergence tolerances on numerical accuracy and efficiency was evaluated. Overall the fixed stress schemes achieved acceptable numerical accuracy and efficiency compared to the fully coupled scheme. However, the accuracy of the fixed stress scheme tends to decrease with low permeable cases, requiring the finer tolerance to achieve a desired numerical accuracy. For the fully coupled scheme, high numerical accuracy was observed in most of cases except a low permeability case where an order of magnitude finer tolerance was required for accurate results. Finally, a two-layer Terzaghi problem and an injection–production well system were used to demonstrate the applicability of findings from the benchmark problems for more realistic conditions over a range of permeability. Simulation results suggest that the fixed stress scheme provides accurate solutions for all cases considered with the proper adjustment of the tolerance. Furthermore, this work clearly demonstrates the robustness of the fixed stress scheme for coupled poroelastic problems, while a cautious selection of numerical tolerance may be required under certain conditions with low permeable materials.

58 GEOSCIENCES↗

DOE OSTI2026

A Demonstration System for Geological Thermal Energy Storage of Concentrating Solar Thermal

Energy storage is increasingly necessary as variable energy technologies are deployed. Seasonal energy storage can shift energy generation from the summer to the winter, but these technologies must have extremely large energy capacities and low costs. Geological Thermal Energy Storage (GeoTES) is proposed as a solution for long-term energy storage [1]. Excess thermal energy can be stored in permeable reservoirs such as aquifers and depleted hydrocarbon reservoirs for several months. The energy capacity cost of GeoTES is very low which makes it suitable for both daily- and seasonal storage of Concentrating Solar Thermal (CST) energy, thus enabling CST to provide value to electricity markets and thermal energy off-takers. A CST-GeoTES demonstration system has been funded by the U.S. Department of Energy, Solar Energy Technology Office. In this article, we will describe this demonstration system and progress that has been made in its development. The demonstration system will comprise a 2 MWth parabolic trough with an 8m aperture developed by Gossamer Space Frames, seven wells, and a 100 kWe power cycle. The demonstration system will be deployed in Kern County, California by Premier Resource Management. A techno-economic model for CST-GeoTES systems has also been developed [3] and is applied to the demonstration system and its planned future expansion. The model integrates the output of specialist models of each subsystem, which enables the performance and cost of both the subsurface and surface systems to be captured. Off-design models enable the performance to be evaluated at each hour of the year, before being aggregated to evaluate the economic potential of CST-GeoTES. Initial analysis indicates that CST-GeoTES can provide long duration energy storage capabilities with low marginal costs of energy capacity - leading to a low value of Levelized Cost of Storage (LCOS) compared to alternative technologies, see Figure 2. In this article, we investigate the cost and performance of the specific demonstration site being developed by PRM and explore a range of operational profiles that can deliver different value streams, such as daily and seasonal storage, capacity, and resiliancy.

14 SOLAR ENERGY↗

DOE OSTI2022

Dynamic Earth Energy Storage: Terawatt-year, Grid-scale Energy Storage Using Planet Earth as a Thermal Battery (GeoTES): Phase I Project (Final Report)

Grid-scale energy storage has been identified by the U.S. Department of Energy’s (DOE) Energy Storage Grand Challenge as a necessary technology to support the continued build-out of intermittent renewable energy resources required to attain a carbon-free energy future. To meet this goal, the 2018 Department of Energy Research and Innovation Act mandated the creation of a comprehensive program to accelerate the development and commercialization of next-generation energy storage technologies. One of numerous energy storage technology options is the storage of excess energy as heated geothermal brine in suitable geologic formations. This concept, known as reservoir thermal energy storage (RTES), geologic thermal energy storage (GeoTES), aquifer thermal energy storage (ATES), etc., relies on the storage of thermal energy in geologic formations for recovery and use in large-scale direct use geothermal (e.g., district heating, industrial processes, etc.) and electrical power generation applications. This thermal energy is derived from excess or waste heat from any high-temperature heat source, such as concentrated solar or from conventional thermal/nuclear generation. As such, RTES can potentially play a significant role in meeting the energy storage shortfall in the coming decades. RTES can provide energy arbitrage through both the storage and production of thermal energy stored in geologic formations for direct use applications and can serve as a source of hot fluids that can be used to generate electricity to support peak demand ramping, thus easing stress on transmission and distribution. This energy storage option has geographic benefits in that energy can be stored locally or regionally depending on the various needs/loads. RTES can also be located across an enormous geographic area, without the need for a traditional hydrothermal resource but where thermal gradients and hydrogeology allow economic exploitation of subsurface heat. The work conducted for this project includes (1) a review of lessons learned from past high-temperature RTES international projects; (2) geochemical experimental investigation and numerical simulations of potential domestic sedimentary reservoirs and (3) development of a thermo-hydrological-mechanical (THM) numerical simulation tool for optimizing formation properties and design parameters to maximize thermal energy storage performance.

15 GEOTHERMAL ENERGY↗

DOE OSTI2021

Improving the Accessibility and Usability of Geothermal Information with Data Lakes and Data Pipelines on the Geothermal Data Repository: Preprint

The Geothermal Data Repository (GDR) provides universal access to data and information resulting from research and development activities funded by the Department of Energy (DOE). The GDR has extended this universal access to big data through integration with data lakes developed by the Open Energy Data Initiative (OEDI). Previously, large datasets such as seismic waveform or distributed acoustic sensing (DAS) data could only be accessed by institutions with high performance data storage and compute capabilities, effectively limiting the accessibility of big data to national labs, larger universities, and major corporations. Moreover, the time and resources needed to transport big data and configure them can produce additional barriers to use. Many of the standard formats used for structured data models (also known as content models) are incapable of handling big data and can introduce additional usability problems, often requiring data to be reformatted prior to use. This paper will explore how recent integrations between the GDR and the OEDI data lake have improved the accessibility and usability of geothermal data in a big way, making the data available to a broader audience, and enabling collaborative analysis and innovation across the greater geothermal industry.

access↗

DOE OSTI2021

Improving the Accessibility and Usability of Geothermal Information with Data Lakes and Data Pipelines on the Geothermal Data Repository

access↗