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848 records · Page 48
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

GeoThermalCloud: Cloud Fusion of Big Data and Multi-Physics Models using Machine Learning for Discovery, Exploration, and Development of Hidden Geothermal Resources

The primary goals of this project are exploring hidden geothermal resources in the U.S.A. and designing profitable enhanced geothermal systems (EGS). Many processes and parameters control geothermal exploration and energy production from geothermal fields. Diverse datasets (e.g., geology, geochemistry, geophysics, satellite, airborne geophysics) are available to help characterize subsurface geothermal conditions. Sparse and multi-scale characteristics of these datasets prohibit properly leveraging these datasets for geothermal exploration and profitable EGS design. Recent advancements in machine learning (ML) promise to resolve these issues. The tremendous challenges and risks of geothermal exploration and production bring the demand for novel ML methods and tools that can (1) analyze large field datasets, (2) assimilate model simulations (large inputs and outputs), (3) process sparse datasets, (4) perform transfer learning (between sites with different exploratory levels), (5) extract hidden geothermal signatures in the field and simulation data, (6) label geothermal resources and processes, (7) identify high-value data acquisition targets, and (8) guide geothermal exploration and production by selecting optimal exploration, production, and drilling strategies. To address these necessities, ML-based geothermal resources exploration and enhanced geothermal systems (EGS) design tools have been developed. The exploration tool is called GeoThermalCloud and EGS design tool is called GeoDT-ML. GeoThermalCloud (https://github.com/SmartTensors/GeoThermalCloud.jl) utilizes a LANL unsupervised ML platform called SmartTensors (https://tensors.lanl.gov/) to automate data analyses and interpretations by extracting hidden signatures to identify geothermal prospects. Also, it enables the identification of critical measurements needed to identify geothermal resource signatures. Alternatively, GeoDT-ML (https://github.com/SmartTensors/GeoThermalCloud.jl/tree/master/EGS) is an ML-based alternative to GeoDT (https://github.com/GeoDesignTool/GeoDT.git), a fast, simplified multi-physics solver to evaluate EGS project designs in uncertain geologic systems. GeoDT-ML leverages recent advances in deep learning and high-performance computing. It is a faster and simpler version of GeoDT. To make this project a success, we used capabilities of LANL, PNNL, Google, Stanford, and Julia Computing. We analyzed eight datasets of the U.S.A. using GeothermalCloud and demonstrated potential highly prospective geothermal resources and identified key factors defining highly prospective sites. The first data set includes 44 locations in southwest New Mexico and 18 geological, hydrogeological, geophysical, geothermal, geochemical attributes. We defined low- and medium-temperature hydrothermal systems and discovered a new highly prospective site. The second data set analyzed 18 shallow water chemistry attributes at 14,342 locations in the Great Basin. It demarcated modestly, moderately, and highly prospective sites including key attributes for each type of prospectivity. The third data set analyzed Utah FORGE data including satellite (InSAR), geophysical (gravity, seismic), geochemical, and geothermal attributes. Here, we performed prospectivity analysis to identify future drilling locations using geological, geochemical, and geophysical attributes. Maps of temperature at depth and heat flow are constructed based on the available data. Prospectivity maps were generated, and drilling locations were proposed for future geothermal field exploration. The fourth data set analyzed 21 attributes at 120 locations in Tularosa Basin, New Mexico; data comes from past play fairway analyses in this region. ML analyses identified geothermal signatures associated with modestly, moderately, and highly hydrothermal systems. We also defined dominant attributes and spatial distribution of the geothermal signatures. The fifth, sixth, seventh, and eighth datasets include Tohatchi Springs, New Mexico, Hawaii, Brady site, Nevada, and EGS Collab, respectively. Moreover, we coupled GeothermalCloud and magnetotellurics data to pinpoint drilling locations for developing geothermal projects in the Tularosa Basin, New Mexico. GeothermalCloud found potential prospective locations for geothermal resources near White Sands Missile Range and McGregor Range at Fort Bliss. Magnetotellurics data determined the potential depth (~1800m) of geothermal prospects at McGregor Range based on apparent resistivity structures/layers in the subsurface. The McGregor Range consists of three resistivity layers and two resistivity structures. Magnetotellurics data also helps identify that the western portion of the McGregor Range has thick and low-resistivity earth materials. The low resistivity to the west is most likely for a fault system. Assuming temperature is consistent with a geothermal reservoir, the west-central part of the McGregor Range has the highest geothermal potential because of the increase in porosity and associated permeability attributed to the interpreted fault system. Also, we devised a coupling strategy between a process model and GeothermalCloud to characterize hydrogeological conditions and geothermal conditions, respectively. The process model characterizes hydrogeological and geothermal conditions on highly prospective geothermal sites provided by GeothermalCloud. We developed a physics-informed neural network (PINN) version of the Burns equation that can be easily coupled with GeothermalCloud. Furthermore, we performed an optimal design decision maximizing the economic value of an EGS power plant. This study optimized the range of well spacing between injection and production wells maximizing net present value in dollars (NPV). For this task, we used the GeoDT to simulate the Utah FORGE EGS development cycle from the initial well design to the end of production. Next, we accomplished another crucial task, which is predicting permeability of geothermal reservoirs. Predicting permeability of geothermal reservoirs is a non-trivial task because of huge computational runtime of simulation and lack of measurements. To avoid these limitations, we used easy-to-measure chemical concentrations in the subsurface as measurement data and convolutional neural network based ML model of a high-fidelity model. Next, we predicted permeability using Markov chain Monte Carlo simulation. We found that Markov chain Monte Carlo simulation predicts permeability with a high certainty if the prediction zone in the simulation area has chemical concentration data. Finally, we analyzed the DOE funded INGENIOUS and GeoDAWN projects data. For discovering hidden geothermal systems in the Great Basin, the INGENIOUS project accumulated old data, collected new data, and released them in 2022. The dataset includes a total of 24 geological, geophysical, and geochemical attributes. Data resolution and scale significantly vary prohibiting an appropriate usage. To avoid such limitations, we brought all data in the same resolution and scale by applying the inverse distance weighting interpolation technique for predicting data in unsampled locations. Subsequently, we analyzed LiDAR data of the GeoDAWN project. We received data in tiles format. The DOE’s overarching goal is to use ML on LiDAR data for finding favorable geological structures (e.g., step up faults in Brady, Nevada). To serve the purpose, we need to label favorable geologic structures that correspond to LiDAR data. We wrote an algorithm to label the LiDAR data with the favorable geologic structures.

15 GEOTHERMAL ENERGY↗
DOE OSTI2013

Grassland Bird Distribution and Raptor Flight Patterns in the Competitive Renewable Energy Zones of the Texas Panhandle [Thesis]

The consistent wind resource in the Great Plains of North America has encouraged the development of wind energy facilities across this region. In the Texas Panhandle, a high quality wind resource is only one factor that has led to the expansion of wind energy development. Other factors include federal tax incentives and the availability of subsidies. Moreover, the State Renewable Portfolio Standards (RPS), mandating production of 10,000 mega-watts of renewable energy in the state by 2025, has contributed to an amicable regulatory and permitting environment (State Energy Conservation Office 2010). Considering the current rate of development, the RPS will be met in coming years (American Wind Energy Association 2011) and the rate of development is likely to continue. To meet increased energy demands in the face of a chronically constrained transmission grid, Texas has developed a comprehensive plan that organizes and prioritizes new transmission systems in high quality wind resource areas called Competitive Renewable Energy Zones (CREZ). The CREZ plan provides developers a solution to transmission constraints and unlocks large areas of undeveloped wind resource areas. In the northern Texas panhandle, there are two CREZs that are classified Class 3 wind (Class 5 is the highest) and range from 862,725 to 1,772,328 ha in size (Public Utility Commission of Texas 2008). Grassland bird populations have declined more than any other bird group in North America (Peterjohn and Sauer 1999, Sauer et al. 2004). Loss of grassland habitat from agricultural development has been the greatest contributor to the decline of grassland bird populations, but development of non-renewable (i.e., oil, coal, and gas) and renewable energy (i.e., wind, solar, biomass, and geothermal) sources have contributed to the decline as well (Pimentel et al. 2002, Maybe and Paul 2007). The effects of wind energy development on declining grassland bird populations has become an area of extensive research, as we attempt to understand and minimize potential impacts of a growing energy sector on declining bird populations. Based on data from post-construction fatality surveys, two grassland bird groups have been the specific focus of research, passerines (songbird guild) and raptors (birds of prey). The effects of wind energy development on these two groups of birds, both of conservation concern, have been examined over the last decade. The primary focus of this research has been on mortality resulting from collision with wind turbines (Kuvlesky et al. 2007). Most studies just quantify post-construction fatality levels (e.g., Erickson et al. 2002) while very few studies provide a comparison of bird populations prior to development through a Before-After-Control-Impact (BACI) study design. Before-After-Control-Impact studies provide powerful evidence of avian/wind energy relationships (Anderson et al. 1999). Despite repeated urgency on conducting these types of studies (Anderson et al. 1999, Madders and Whitfield 2006, Kuvelsky et al. 2007), few have been conducted in North America. Although several European researchers (Larsson 1994, de Lucas et al. 2007) have used BACI designs to examine whether wind facilities modified raptor behavior, there is a scarcity of BACI data relating to North America grassland ecosystems that examine avian-wind energy relationships. There are less than a handful of studies in the entire United States, let alone the southern short grass prairie ecosystem, that incorporate preconstruction data to form the baseline for post-construction impact estimates (Johnson et al. 2000, Erickson et al. 2002). Although declines in grassland bird populations are well-documented (Peterjohn and Sauer 1999, Sauer et al. 2004), the causal mechanisms affecting the decline of grassland birds with increasing wind energy development in the southern short grass prairie are not well-understood (Kuvlesky et al. 2007, Maybe and Paul 2007). Several factors may potentially affect the bird population when wind turbines are constructed in areas with high bird densities (de Lucas et al. 2007). Habitat fragmentation, noise from turbines, physical movement of turbine blades, and increased vehicle traffic have been suggested as causes of decreased density of nesting grassland birds in Minnesota (Leddy et al. 1999), Oklahoma (O’Connell and Piorkowski 2006), and South Dakota (Shaffer and Johnson 2008). Similarly, constructing turbines in areas where bird flight patterns place them at similar heights of turbine blades increases the potential for bird collisions (Johnson et al. 2000, Hoover 2002). Raptor fatalities have been associated with topographic features such as ridges, saddles and rims where birds use updrafts from prevailing winds (Erickson et al. 2000, Johnson et al. 2000, Barrios and Rodriquez 2004, Hoover and Morrison 2005). Thus, wind energy development can result in indirect (e.g., habitat avoidance, decreased nest success) and direct (e.g., collision fatalities) impacts to bird populations (Anderson et al. 1999). Directly quantifying the level of potential impacts (e.g., estimated fatalities/mega-watthour) from wind energy development is beyond the scope of this study. Instead, I aim to quantify density, occupancy and flight behavior for the two bird groups mentioned earlier: obligate grassland songbirds and raptors, respectively, predict where impacts may occur, and provide management recommendations to minimize potential impacts. The United States Department of Energy (DOE), through the Office of Energy Efficiency and Renewable Energy Allocation, contracted Texas Tech University to investigate grassland bird patterns of occurrence in the anticipated CREZ in support of DOE’s 20% Wind Energy by 2030 initiative. In cooperation with Iberdrola Renewables, Inc., studies initiated by Wulff (2010) at Texas Tech University were continued at an area proposed for wind energy development and a separate reference site unassociated with wind energy development. I focused on four primary objectives and this thesis is accordingly organized in four separate chapters that address grassland bird density, grassland bird occupancy, raptor flight patterns, and finally I summarize species diversity and composition. The following chapters use formatting from the Journal of Wildlife Management guidelines (Block et al. 2011) with modifications as required by the Texas Tech University Graduate School. 1) I estimate pre-construction bird density patterns using methods that adjust for imperfect detection. I used a distance sampling protocol that effectively accounts for incomplete detection in the field where birds are present but not detected (Buckland et al. 2001). I improved density estimates with hierarchical distance sampling models, a modeling technique that effectively incorporates the detection process with environmental covariates that further influence bird density (Royle et al. 2004, Royle and Dorazio 2008). Covariates included road density and current oil and gas infrastructure to determine the relationship between existing energy development and bird density patterns. Further, I used remote sensing techniques and vegetation field data to investigate how landcover characteristics influenced bird density patterns. I focused species-specific analyses on obligate grassland birds with >70 detections per season namely grasshopper sparrow (Ammodramus savannarum) and horned lark (Eremophila alpestris). Chapter II focuses on hierarchical models that model and describe relationships between grassland bird density and anthropogenic and landscape features. 2) A large number of bird detections (>70) are needed to estimate density using distance sampling and collection of such quantity are often not feasible, particularly for cryptic species or species that naturally occur at low densities (Buckland et al. 2001). Occupancy models operate with far fewer data and are often used as a surrogate for bird abundance when there are fewer detections (MacKenzie and Nichols 2004). I used occupancy models that allow for the possibility of imperfect detection and species abundance to improve estimates of occurrence probability (Royal 2004). I focused species-specific analyses on grassland birds with few detections: Cassin’s sparrow (Peucaea cassinii), eastern meadowlark (Sturnella magna), and upland sandpiper (Bartramia longicauda). Chapter III uses a multi-season dynamic site occupancy model that incorporates bird abundance to better estimate occurrence probability. 3) When I considered the topographic relief of the study sites, the proposed design of the wind facility and its location within the central U.S. migratory corridor, I expanded the study to investigate raptor abundance and flight behavior (Hoover 2002, Miller 2008). I developed a new survey technique that improved the accuracy of raptor flight height estimates and compared seasonal counts and flight heights at the plateau rim and areas further inland. I used counts and flight behaviors to calculate species-specific collision risk indices for raptors based on topographic features. I focused species-specific analyses on raptors with the highest counts: American kestrel (Falco sparverius), northern harrier (Circus cyaneus), red-tailed hawk (Buteo jamaicensis), Swainson’s hawk (Buteo swainsoni), and turkey vulture (Cathartes aura). Chapter IV describes patterns of seasonal raptor abundance and flight behavior and how topography modulates collision risk with proposed wind energy turbines. 4) Finally, for completeness, in Chapter V I summarize morning point count data for all species and provide estimates of relative composition and species diversity with the Shannon-Wiener Diversity Index (Shannon and Weaver 1949).

17 WIND ENERGY↗