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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Seismic Characterization of the Blue Mountain Geothermal Field

Subsurface characterization is crucial for geothermal energy exploration and production. Yet hydrothermal reservoirs usually reside in highly fractured and faulted zones where accurate characterization is very challenging because of low signal-to-noise ratios of land seismic data and lack of coherent reflection signals. We perform an active-source seismic characterization for the Blue Mountain geothermal field in Nevada using active seismic data to reveal the elastic medium property complexity and fault distribution at this field. We first employ an unsupervised machine learning method to attenuate groundroll and near-surface guided-wave noise and enhance coherent reflection and scattering signals from noisy seismic data. We then build a smooth initial P-wave velocity model based on an existing magnetotellurics survey result, and use 3D first-arrival traveltime tomography to refine the initial velocity model. We then derive a set of elastic wave velocities and anisotropic parameters using elastic full-waveform inversion, and obtain PP and PS images using elastic reverse-time migration. We identify major faults by analyzing the variations of seismic velocities and anisotropy parameters, and reveal mid- to small-scale faults by applying a supervised machine learning method to the seismic migration images. Our characterization reveals complex velocity heterogeneities and anisotropies, as well as faults, with a high spatial resolution. These results can provide valuable information for optimal placement of future injection and production wells to increase geothermal energy production at the Blue Mountain geothermal power plant.

58 GEOSCIENCES↗
DOE OSTI2023

Downhole Sensing and Event-Driven Sensor Fusion for Depth-of-Cut Based Autonomous Fault Response and Drilling Optimization

Achieving robust and efficient drilling is a critical part of reducing the cost of geothermal energy exploration and extraction. Drilling performance is often evaluated using one or more of three key metrics: depth of cut (DOC), rate of penetration (ROP), and mechanical specific energy (MSE). All three of these quantities are related to each other. DOC refers to the depth a bit penetrates into rock during drilling. This is an important quantity for estimating bit behavior. ROP is the simply the DOC multiplied by the rotational rate, and represents how quickly the drill bit is advancing through the ground. ROP is often the parameter used for drilling control and optimization. Finally, MSE provides insight into drilling efficiency and rock type. MSE calculations rely on ROP, drilling force, and drilling torque. Surface-based sensors at the top of the drill are often used to measure all these quantities. However, top-hole measurements can deviate substantially from the behavior at the bit due to lag, vibrations, and friction. Therefore, relying only on top-hole information can lead to suboptimal drilling control. In this work, we describe recent progress towards estimating ROP, DOC, and MSE using down-hole sensing. We assume down-hole measurements of torque, weight-on-bit (WOB). Our hypothesis is that these measurements can provide more rapid and accurate measures of drilling performance. We show how a multi-layer perceptron (MLP) machine learning algorithm can provide rapid and accurate performance when evaluated on experimental data taken from Sandia’s Hard Rock Drilling Facility. In addition, we implement our algorithms on an embedded system intended to emulate a bottom-hole-assembly for sensing and estimation. Our experimental results show that DOC can be estimated accurately and in real-time. These estimates when combined with measurements for rotary speed, torque, and force can provide improved estimates for ROP and MSE. These results have the potential to enable better drilling assessment, improved control, and extended component lifetimes.

15 GEOTHERMAL ENERGY↗
DOE OSTI2021

Play Fairway Analysis of the Snake River Plain, Idaho (Final Report)

The Snake River Plain (SRP) volcanic province overlies a thermal anomaly that extends deep into the mantle; it represents one of the highest heat flow provinces in North America. The Yellowstone hotspot continues to feed a magma system that underlies much of southern Idaho and has produced basaltic volcanism as young as 2000 years old. It has been estimated to host up to 855 MW of potential geothermal power production, most of which is associated with the Snake River Plain volcanic province in Idaho, which lies outside the area of Yellowstone National Park (Neely, K.W. and Galinato, G., 2007, Geothermal power generation in Idaho: an overview of current developments and future potential, Open File Report, Idaho Office of Energy Resources).

15 GEOTHERMAL ENERGY↗
DOE OSTI2023

GeoThermalCloud: A Machine Learning Tool for Discovery, Exploration, and Development of Hidden Geothermal Resources

In this 25 minute presentation, we showcase our open source “GeoThermalCloud” tool for identifying hidden geothermal resources using a publicly available dataset for southwestern New Mexico. The presenters include Bulbul Ahmmed and Luke Frash. All of the visuals use source material from LA-UR approved publications and this work falls under the Earth Sciences DUSA. The code shown in this video is already released with LANL approval in open source format on GitHub and DockerHub. The audio in this video includes only material on the topics of geothermal energy and machine learning applied to geothermal energy. The primary machine learning method used is LANL’s Non-negative Matrix Factorization “NMFk” method. Modeling work also mentions LANL’s Geothermal Design Tool “GeoDT” which is another approved open source code that has been released by LANL. This work was performed for DOE Geothermal Technologies Office (DE-EE-3.1.8.1). The host for the released video is intended to be YouTube or a suitable perpetual data repository such as GDR.

15 GEOTHERMAL ENERGY↗
DOE OSTI2023

Using Dark Fiber and Distributed Acoustic Sensing to Characterize a Geothermal System in the Imperial Valley, Southern California

The Imperial Valley, CA, is a tectonically active transtensional basin located south of the Salton Sea; the area hosts numerous geothermal fields, including significant hidden hydrothermal resources without surface manifestations. Development of inexpensive, rugged, and highly sensitive exploration techniques for undiscovered geothermal systems is critical for accelerating geothermal power deployment as well as unlocking a low-carbon energy future. We present a case study utilizing distributed acoustic sensing (DAS) and ambient noise interferometry for geothermal reservoir imaging, utilizing unlit fiber-optic telecommunication infrastructure (dark fiber). The study exploits two days of passive DAS data acquired in early November 2020 over a ~28-km section of fiber from Calipatria, CA to Imperial, CA. We apply ambient noise interferometry to retrieve coherent signals from DAS records and develop a bin stacking technique to attenuate the effects from persistent localized noise sources and to enhance retrieval of coherent surface waves. As a result, we are able to obtain high-resolution two-dimensional (2D) S wave velocity ($V_s$) structure to 3 km depth, based on joint inversion of both the fundamental and higher overtones. We observe a previously unmapped high $V_s$ and low $V_p$/$V_s$ ratio feature beneath the Brawley geothermal system, which we interpret to be a zone of hydrothermal mineralization and lower porosity. This interpretation is consistent with a host of other measurements including surface heat flow, gravity anomalies, and available borehole wireline data. These results demonstrate the potential utility of DAS deployed on dark fiber for geothermal system exploration and characterization in the appropriate geological settings.

Vp/Vs imaging↗
DOE OSTI2023

A VOI Web Application for Distinct Geothermal Domains: Statistical Evaluation of Different Data Types within the Great Basin

The Great Basin region contains different domains that have different structural and hydrothermal flow patterns. Depending on the characteristics of these patterns, certain data types may be more successful at detecting hidden geothermal resources. In this paper, we quantitatively evaluate if certain data types are more successful in certain domains. Given different aquifer, strain and structural conditions, we explore which data types statistically reveal positively labeled geothermal sites. We utilize value of information (VOI) metrics to help quantify the reliability of data types to discriminate against "positive" and "negative" labeled geothermal sites. We also evaluate how kernel density estimation can help generalize the statistics that inform VOI, which is necessary given the limited data in geothermal exploration. Except for the Carbonate Aquifer, the highest ranking of the Vimperfect is the Local Structural Setting. Next, the slip and dilation tendency is first for Carbonate Aquifer and second for Central Nevada Seismic Belt and Western Great Basin. For the Carbonate Aquifer, heat flow is has the lowest Vimperfect value compared to the other three domains, which is consistent with the understanding of how heat flow measurements are masked by regional groundwater flow.

Bayesian analysis↗
DOE OSTI2023

Topics and Considerations for Developing State Geothermal Regulations

The intent of this document is to provide guidance to states for developing geothermal regulations that would be inclusive of all geothermal technologies (e.g., conventional and non-conventional power production and direct-use applications) with applicability in all states. As part of this project, the NREL project team reviewed and catalogued existing state and federal geothermal regulations, compiled best practices from geothermal and other extractive industries, and established a Geothermal Regulatory Stakeholder Working Group (SWG) to advise and review the geothermal regulatory guidance. The SWG met approximately monthly over the course of one year to review and discuss specific topics as relevant to this geothermal regulatory guidance. This report is broken into five main sections, which were identified in coordination with the SWG as the main topics for inclusion. Within these sections, a list of considerations within each category were identified and, in some cases, relevant examples from existing state and federal geothermal regulations are included to further illustrate the proposed considerations. This report includes the following topics: geothermal resource definition; leasing process; exploration approval process; drilling/wellfield development approval process; and underground injection control process.

15 GEOTHERMAL ENERGY↗
DOE OSTI2026

Detecting and Characterizing Fracture Zones Using a Convolutional Neural Network

This project directly supports the Geothermal Technologies Office (GTO) objectives outlined in the Multi-Year Program Plan (MYPP) by advancing two key research areas: “Exploration and Characterization” and “Data, Modeling, and Analysis.” This project has successfully demonstrated a pre-drilling ability to image and characterize the distribution and connectivity of subsurface faults and fractures, key parameters for identifying permeable pathways that enable geothermal fluids to circulate and produce energy. Specifically, we developed and implemented innovative machine learning methodologies to enhance geothermal exploration. Large-scale faults were detected using a Convolutional Neural Network (CNN), while small-scale fractures were characterized using a novel Double-Beam Neural Network (DBNN). These tools have proven both technically effective and cost-efficient by reducing reliance on expensive exploratory drilling. Through collaboration with our geothermal industry partner, this research has significantly advanced techniques for identifying hidden geothermal systems and extending the productive lifespan of existing geothermal fields. We applied our methods to two geothermal fields—Soda Lake (Nevada) and Lightning Dock (New Mexico)—to identify shallow steam-charged fracture zones and characterize deep faults at depths of 1.5-2 km. The steam zone identified at the Soda Lake geothermal field showed excellent agreement with prior drilling data, validating the effectiveness of our approaches. In addition, the analysis revealed three new prospective drilling targets for further development and verification. The outcomes of this project improve our scientific understanding of geothermal reservoir behavior, enhance exploration efficiency, extend the economic life of existing geothermal plants. Ultimately, these advancements contribute to GTO’s goal of achieving more sustainable, affordable, and data-driven geothermal energy development across the United States.

15 GEOTHERMAL ENERGY↗