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DOE OSTI2023

Extending Magnetic Core Shell Nanoparticle Extraction Technology to Cesium and Antimony Removal from Geothermal Brines in New Zealand

Our industrial client (Geo40) has developed and deployed a process to remove silica from geothermal fluids and produce a high-margin specialty colloidal silica product comparable to those of market leaders. Geo40 now wishes to explore opportunities to extend their mineral extraction operations to other elements that are present in these brines. Geo40 has identified cesium (Cs) that is present in Ohaaki brines (pH ~8–8.5) at parts per million levels and could be sold to customers if it could be produced at an attractive price. With support from the Department of Energy’s (DOE’s) Geothermal Technologies Office, a simple and highly cost-effective magnetic nanofluid method for extraction of rare earth elements (REEs) from geothermal brine solutions has been developed and demonstrated at the laboratory bench scale at Pacific Northwest National Laboratory (PNNL). Core shell sorbent particles are produced using an iron oxide core particle, which is used to anchor and grow a surrounding adsorbent shell functionalized with a chelating ligand that selectively binds REEs. We extended PNNL’s work by exploring new sorbent shells that are highly selective for Cs. Uptake of Cs was measured as a function of exposure time by analyzing solution samples extracted from batch sorption tests.

15 GEOTHERMAL ENERGY↗

DOE OSTI2021

Extending Magnetic Core Shell Nanoparticle Extraction Technology to Cesium and Antimony Removal from Geothermal Brines in New Zealand - CRADA 440

Geo40 Limited has developed and deployed a process to remove silica from geothermal fluids and produce a high-margin specialty colloidal silica product comparable to market leaders. Geo40 now wishes to explore opportunities to extend their mineral extraction operations to other elements that are present in these brines. Geo40 has identified Cs present in Ohaaki brines at ppm level amounts that could be sold to known customers if it could be produced at an attractive price. With support from DOE’s Geothermal Technologies Office, a simple and highly cost-effective magnetic nanofluid method for extraction of rare earth elements (REEs) from geothermal brine solutions has been developed and demonstrated at the laboratory bench scale at Pacific Northwest National Laboratory (PNNL). Moselle Technologies has acquired a license to the background IP associated with this technology and wishes to foster commercial deployment by supporting applications of the technology beyond REEs, including Cs.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Utah FORGE 1-2409: Zonal Isolation Solution for Geothermal Wells - Workshop Presentation

This is a presentation on the Zonal Isolation Solution for Geothermal Wells project by PetroQuip Energy Services, presented by VP of operations Robert Coon. The project's objective was to design and develop a multi-stage system for zonally isolating fluids inside and outside of geothermal well casings. This presentation was featured in the Utah FORGE R&D Annual Workshop on September 7, 2023. The workshop provided a valuable opportunity to explore the progress made in each of the 17 Research and Development projects funded under Solicitation 2020-1 which aim to enhance our understanding of the crucial factors influencing the development of Enhanced Geothermal Systems (EGS) reservoirs and resources.

15 GEOTHERMAL ENERGY↗

DOE OSTI2020

Polymer-cement composites with adhesion and re-adhesion (healing) to casing capability for geothermal wellbore applications

Deterioration of cement/casing adhesion in wellbore scenarios can result in unwanted and potentially harmful leakage with the potential of serious repair costs. In this work, we explore the use of self-healing polymers added to conventional wellbore cements as a way to bring about self-healing and readhering (to steel casing) properties to the composite material. The polymers are pH resistant and seem to improve the cement integrity after exposure to typical chemical and thermal stresses encountered under geothermal wellbore conditions. We find that addition of about 10-15 wt% of polymer to the cement visually increases its resistance to fracturing from exposure to geothermal conditions, while the adhesive strength of cement/stainless steel increases with curing time for a period of about 10 days. Self-healing capability was demonstrated by permeability analysis showing that polymer-cement composites reduce flow by 50-70% at cement bulk and at the cement/steel interface. Use of atomistic simulations imply that these polymers have good wetting properties on the steel surfaces. Analysis of the interactions between steel/polymer and cement/polymer show that they are complementary, resulting in a wider range of bonding patterns. Cracks are likely to expose under-coordinated sites that result in more bonding interactions, which agrees well with the permeability measurements showing high degree of healed cracks and healed (cement-steel) interfacial gaps together with an overall increased in structural integrity of these advanced polymer-cement composite materials.

Rod, Kenton A.↗

DOE OSTI2020

Characterization of flow and transport in a fracture network at the EGS Collab field experiment through stochastic modeling of tracer recovery

Energy extraction from subsurface reservoirs is important for addressing the increasing energy demand and environmental concerns such as global warming. However, the characterization of subsurface reservoirs, particularly reservoirs dominated by fracture networks remains a challenge due to the lack of means to directly observe subsurface processes. This study explores the feasibility and efficacy of characterizing fracture flow and transport processes in an enhanced geothermal system (EGS) testbed through stochastic tracer modeling. There are two enabling factors that allow application of stochastic modeling to characterize a subsurface reservoir. First, an abundance of geological and geophysical measurements enables the development of a high-fidelity and well-constrained fracture network model. Second, high-performance computing (HPC) allows running massive realizations efficiently. Six conservative tracer tests were stochastically modeled and produced satisfactory realizations that successfully reproduce field tracer recovery data from each tracer test. The evolution of flow and transport processes in the fracture network was then analyzed from these satisfactory realizations. This work demonstrates that stochastic tracer modeling on a high-fidelity fracture network model is feasible and can provide important insights regarding flow and transport characteristics in subsurface fractured reservoirs.

58 GEOSCIENCES↗

DOE OSTI2021

Autonomous extraction of millimeter-scale deformation in InSAR time series using deep learning

Systematically characterizing slip behaviours on active faults is key to unraveling the physics of tectonic faulting and the interplay between slow and fast earthquakes. Interferometric Synthetic Aperture Radar (InSAR), by enabling measurement of ground deformation at a global scale every few days, may hold the key to those interactions. However, atmospheric propagation delays often exceed ground deformation of interest despite state-of-the art processing, and thus InSAR analysis requires expert interpretation and a priori knowledge of fault systems, precluding global investigations of deformation dynamics. Here, we show that a deep auto-encoder architecture tailored to untangle ground deformation from noise in InSAR time series autonomously extracts deformation signals, without prior knowledge of a fault’s location or slip behaviour. Applied to InSAR data over the North Anatolian Fault, our method reaches 2 mm detection, revealing a slow earthquake twice as extensive as previously recognized. We further explore the generalization of our approach to inflation/deflation-induced deformation, applying the same methodology to the geothermal field of Coso, California.

58 GEOSCIENCES↗

DOE OSTI2020

Artificial Intelligence Applications in Renewable Energy

Addressing new methodologies in deep learning (DL), machine learning (ML) and artificial intelligence (AI), the webinar speakers will provide an overview of the literature spanning these three overlapping fields as applied to energy systems research. The audience will learn how developments in these areas have added new capabilities for pattern recognition and predictive modeling that are complementary to more traditional modeling approaches used in energy systems research. The speakers will illustrate several use cases of these new approaches in the energy space, such as physics-guided neural networks to improve ML regressions for solar data and how AI agents can be used to explore power plant operations.

40 EE - Geothermal Technologies Office (EE-4G)↗

DOE OSTI2021

GEOPHIRES files for DDU techno-economic simulations

During 2017-2019, the U.S. Department of Energy funded six geothermal deep direct-use (DDU) projects to investigate feasibility of DDU for heating, cooling and thermal storage in the United States. In a follow-on study conducted at the National Renewable Energy Laboratory (NREL), findings of these six projects were reviewed and analyzed, and additional simulations were conducted using the simulator GEOPHIRES to explore technical performance and cost-competitiveness of DDU. The results of the NREL study were published in the paper "Evaluating the Feasibility of Geothermal Deep Direct-Use in the United States." The GEOPHIRES files developed in that study are included in this submission. The reference for the paper under review is below: Beckers KF, Kolker A, Pauling H, McTigue JD, and Kesseli D (2021) ?Evaluating the Feasibility of Geothermal Deep Direct-Use in the United States?, Submitted to Energy Conversion and Management, Under Review.

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.

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DOE OSTI2021

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

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NASA NTRS2008

Solid-State High-Temperature Power Cells

All-solid-state electrochemical power cells have been fabricated and tested in a continuing effort to develop batteries for instruments for use in environments as hot as 500 C. Batteries of this type are needed for exploration of Venus, and could be used on Earth for such applications as measuring physical and chemical conditions in geothermal and oil wells, processing furnaces, and combustion engines. In the state-of-the-art predecessors of the present solid-state power cells, fully packaged molten eutectic salts are used as electrolytes. The molten-salt-based cells can be susceptible to significant amounts of self-discharge and corrosion when used for extended times at elevated temperatures. In contrast, all-solid-state cells such as the present ones are expected to be capable of operating for many days at temperatures up to 500 C, without significant self-discharge. The solid-state cell described here includes a cathode made of FeS2, an electrolyte consisting of a crystalline solid solution of equimolar amounts of Li3PO4 and Li4SiO4, and an anode made of an alloy of Li and Si (see figure). The starting material for making the solid electrolyte is a stoichiometric mixture of Li3PO4, SiO2, and Li3CO2. This mixture is ball-milled, then calcined for two hours at a temperature of 1,100 C, then placed in a die atop the cathode material. Next, the layers in the die are squeezed together at a pressure between 60 and 120 MPa for one hour at a temperature of 600 C to form a unitary structure comprising the solid electrolyte and cathode bonded together. Finally, the lithium-alloy anode is pressure-bonded to the solid electrolyte layer, using an intermediate layer of pure lithium. In one test of a cell of this type, a discharge rate of about 1 mA per gram of cathode material was sustained for 72 hours at a temperature of about 460 C. This is about three times the discharge rate required to support some of the longer duration Venus-exploration mission scenarios.

Whitacre, Jay↗

DOE OSTI2023

Utah FORGE 1-2410: Development of a Smart Completion and Stimulation Solution - Workshop Presentation

This is a presentation on the Development of a Smart Completion & Stimulation Solution project by Welltec in collaboration with the University of Oklahoma, presented by Yosafat Esquitin, a Senior Business Development Manager at Welltec. The project's objective was to develop an annular isolation system, a stimulation isolation system, and a multi-open-close flow system for geothermal environments. These systems were developed to enable effective zonal isolation and stimulation, implement downhole Enhanced Geothermal Systems (EGS) in any location, and extend the productive life of the geothermal well. This presentation was featured in the Utah FORGE R&D Annual Workshop on September 7, 2023. The workshop provided a valuable opportunity to explore the progress made in each of the 17 Research and Development projects funded under Solicitation 2020-1 which aim to enhance our understanding of the crucial factors influencing the development EGS reservoirs and resources.

15 GEOTHERMAL ENERGY↗

NASA NTRS1974

A reconnaissance space sensing investigation of crustal structure for a strip from the eastern Sierra Nevada to the Colorado Plateau

The author has identified the following significant results. Studies were conducted in key field areas in the Sierra Nevada, the Basin Range Province and the Colorado Plateau to evaluate the origins and significance of geologic and structural anomalies expressed in the ERTS-1 data. The investigation included development of image enhancement and analysis techniques and comparison of remote sensing data available over the test site. The ERTS-1 MSS imagery has proven to be an effective tool for studying the interrelationsships between Cenozoic tectonic patterns and the distributions of Cenozoic plutonism and volcanism, seismic activity, geologic hazards, and known mineral, geothermal and ground water resources. Recommendations are made for applications of ERTS-1 data to natural and resource exploration and management.

Liggett, M. A.↗

DOE OSTI2023

Connecting People to Data: Enabling Data Connected Communities through Enhancements to the Geothermal Data Repository: Preprint

The Department of Energy's (DOE) Geothermal Data Repository (GDR) has implemented a series of new features designed to connect people to data. These features, which are based on feedback from the GDR user community and surveys of the greater geothermal research community, are designed to improve data quality and empower members of all communities to better engage with geothermal data resources by providing universal access to data and by improving the connections between data providers, subject matter experts, and the communities of people using GDR data. This paper will explore some of the recent enhancements made to the GDR to improve data discoverability, reduce submission time, and result in better quality data submissions. These improvements include the ability for users to save a list of their favorite datasets, search for insight into geothermal datasets or data availability, or sign up to receive notifications of future updates to specific datasets. These improvements aim to enhance the overall user experience of the GDR while further connecting communities to the data they need to inform decisions, advance geothermal research, and develop innovative solutions to local energy problems.

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DOE OSTI2023

Connecting People to Data: Enabling Data Connected Communities through Enhancements to the Geothermal Data Repository

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DOE OSTI2023

Utah FORGE 5-2565: Hydrothermal Evolution of Fracture Properties - Workshop Presentation

This is a presentation on the Evolution of Permeability and Strength Recovery of Shear Fractures Under Hydrothermal Conditions project by the U.S. Geological Survey, presented by Dr. David Lockner. The project's objective was to determine how thermal, hydraulic, mechanical, and chemical processes affect the sustainability of fracture networks in geothermal reservoirs and provide strategies for improved EGS techniques that maximize thermal coupling and increase reservoir longevity. This presentation was featured in the Utah FORGE R&D Annual Workshop on September 8, 2023. The workshop provided a valuable opportunity to explore the progress made in each of the 17 Research and Development projects funded under Solicitation 2020-1 which aim to enhance our understanding of the crucial factors influencing the development of Enhanced Geothermal Systems (EGS) reservoirs and resources.

15 GEOTHERMAL ENERGY↗

DOE OSTI2024

Derisking Superhot Geothermal Plays with Value of Information: Utilizing Play Fairway Analysis, Geophysics and Technoeconomics

This paper describes a methodology for evaluating how the play fairways analysis (e.g., favorability) can improve our chances of making geothermal development decisions. We make statistical resource assessments and couple them with technoeconomic analysis utilizing previous favorability work performed for the Newberry Volcano. We demonstrate how the favorability can be used in a decision analysis framework because the Newberry favorability also estimated an associated uncertainty. The specific decision considered is how large of a power plant to build, which is difficult given the uncertainty about the resource size. Our results focus on two resource types, hydrothermal and enhanced geothermal systems, and they demonstrate how estimates of the minimum, most likely, and maximum estimates of the geothermal resource (denoted as the P10, P50, and P90, respectively) can be used in a decision analysis framework. Lastly, the value of information results explore using favorability with and without the magnetotelluric and gravity data from the Newberry Volcano. As expected, the favorability is more reliable, according to our methodology, at indicating the resource size when it includes the two geophysical models.

enhanced geothermal system↗

DOE OSTI2024

Empowering Geothermal Research: The Geothermal Data Repository's New AI Research Assistant: Preprint

The Department of Energy's (DOE) Geothermal Data Repository (GDR) team has integrated a Large Language Model (LLM) with the metadata and supporting documents associated with GDR datasets to create an Artificially Intelligent (AI) research assistant. By leveraging work done to make GDR metadata machine-readable and an open-source LLM integration model called the Energy Language Model, developed by the National Renewable Energy Laboratory, AskGDR serves as a virtual research assistant to GDR users. It provides answers to a variety of user-provided questions using natural language processing and generative machine learning. Users can get answers to questions about specific datasets, including inquiries about the equipment, assumptions and methodologies used in the origination of the data; or more abstract questions, such as the applicability of data to specific research fields. AskGDR improves the discoverability of geothermal data by helping guide users to datasets beyond simple keyword searches. It enables users to find data based on properties of the data, discover information contained within supporting documents, and explore data from projects related to their research objectives.

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