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Results for “Geothermal Resources Council Transactions”

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27 records · Page 2Linked to original sources
USGS2008

Isotopic constraints on the chemical evolution of geothermal fluids, Long Valley, CA

A spatial survey of the chemical and isotopic composition of fluids from the Long Valley hydrothermal system was conducted. Starting at the presumed hydrothermal upwelling zone in the west moat of the caldera, samples were collected from the Casa Diablo geothermal field and a series of monitoring wells defining a nearly linear, ~14 km long, west-to-east trend along the proposed fluid flow path (Sorey et al., 1991). Samples were analyzed for the isotopes of water, Sr, Ca, and noble gases, the concentrations of major cations and anions and total CO 2 . Our data confirm earlier models in which the variations in water isotopes along the flow path reflect mixing of a single hydrothermal fluid with local groundwater. Variations in Sr data are poorly constrained and reflect fluid mixing, multiple fluid-pathways or water-rock exchange along the flow path as suggested by Goff et al., (1991). Correlated variations among total CO 2 , noble gases and the concentration and isotopic composition of Ca suggest progressive fluid degassing (loss of CO 2 , noble gases) driving calcite precipitation as the fluid flows west-to-east across the caldera. This is the first evidence that Ca isotopes may trace and provide definitive evidence of calcite precipitation along fluid flow paths in geothermal systems.

California↗
USGS2022

Advancing geophysical techniques to image a stratigraphic hydrothermal resource

Sedimentary-hosted geothermal energy systems are permeable structural, structural-stratigraphic, and/or stratigraphic horizons with sufficient temperature for direct use and/or electricity generation. Sedimentary-hosted (i.e., stratigraphic) geothermal reservoirs may be present in multiple locations across the central and eastern Great Basin of the USA, thereby constituting a potentially large base of untapped, economically accessible energy resources. Sandia National Laboratories has partnered with a multi-disciplinary group of collaborators to evaluate a stratigraphic system in Steptoe Valley, Nevada using both established and novel geophysical imaging techniques. The goal of this study is to inform an optimized strategy for subsequent exploration and development of this and analogous resources. Building from prior Nevada Play Fairway Analysis (PFA), this team is primarily 1) collecting additional geophysical data, 2) employing novel joint geophysical inversion/modeling techniques to update existing 3D geologic models, and 3) integrating the geophysical results to produce a working, geologically constrained thermo-hydrological reservoir model. Prior PFA work highlights Steptoe Valley as a favorable resource basin that likely has both sedimentary and hydrothermal characteristics. However, there remains significant uncertainty on the nature and architecture of the resource(s) at depth, which increases the risk in exploratory drilling. Newly acquired gravity, magnetic, magnetotelluric, and controlled-source electromagnetic data, in conjunction with new and preceding geoscientific measurements and observations, are being integrated and evaluated in this study for efficacy in understanding stratigraphic geothermal resources and mitigating exploration risk. Furthermore, the influence of hydrothermal activity on sedimentary-hosted reservoirs in favorable structural settings (i.e., whether fault-controlled systems may locally enhance temperature and permeability in some deep stratigraphic reservoirs) will also be evaluated. This paper provides details and current updates on the course of this study in-progress.

Geothermal Resources Council Transactions↗
USGS2023

Detrending Great Basin elevation to identify structural patterns for identifying geothermal favorability

Topography provides information about the structural controls of the Great Basin and therefore information that may be used to identify favorable structural settings for geothermal systems. The Nevada Machine Learning Project (NVML) tested the use of a digital elevation map (DEM) of topography as an input feature to predict geothermal system favorability. A recent study re-examines the NVML data, identifying the DEM as the most important feature, showing a broad uniform pattern of high-favorability in the lower-elevation west and low-favorability in the higher elevation east of their study area in north-central Nevada. This regional elevation trend conflicts with the geologic notion that local relative topography should be used to identify geologic structures associated with favorable structural settings for hydrothermal upflow. Specifically, local relative topography gives information about position in the mountains, in the valleys, or at the transitions between, aiding in identification of faults and fault intersections. As part of U.S. Geological Survey efforts to engineer features that are useful for predicting geothermal resources, we construct a detrended elevation map that emphasizes local relative topography and highlights features that geologists use for identifying geothermal systems (i.e., providing machine learning algorithms with features that may improve predictive skill by emphasizing the information used by geologists). Herein, we describe the removal of the regional trend in elevation to emphasize the basin-and-range scale structural features, creating detrended elevation maps. Regional elevation trends were estimated using a local linear regression and subtracted from the actual elevation using a 30-m DEM. In an effort to optimize the detrended surface, alternate versions were produced with different rates of smoothness resulting in three detrended elevation maps. The resulting elevation trend surfaces (a proxy for crustal thickness) are compared with conductive heat flow maps, and a general pattern was observed of a negative correlation between heat flow and regional elevation in many areas, indicating that thinner crust may be causing elevated heat flow in some areas and thicker crust may cause the observed heat flow lows. Because these detrended elevation maps emphasize geologic structure and relative displacement, these products may also be useful for other geologic research including mineral exploration, hydrologic research, and defining geologic provinces.

Geothermal Resources Council Transactions↗
USGS2022

UAS-based tools for mapping and monitoring hydrothermal systems: An example from Mammoth Lakes, California

Unoccupied Aerial Systems (UAS) can accommodate a variety of tools for mapping and monitoring hydrothermal systems (e.g., magnetic, gas, photogrammetry, and thermal infrared [TIR]). These platforms offer increased speed, coverage area, and uniformity compared to ground-based measurements, as well as lower flight height – and therefore higher resolution – than occupied aircraft. We adapted a suite of tools for use with UAS and implemented these methods in a study focused on the area around Shady Rest Park, Mammoth Lakes, California, within the Long Valley Caldera. This location, which contains tree kills, gas vents, soil gas emissions, heated ground, and hydrothermal alteration, is the site of ongoing efforts to monitor changes in the surface expression of the local hydrothermal system. The methods applied in this study include: (1) airborne visible imagery for surficial mapping and the creation of high-resolution digital elevation models; (2) airborne magnetic measurements; (3) airborne TIR imagery; (4) airborne gas emission measurements; and (5) ground-based gravity measurements. We conducted these surveys in May and October of 2021, in part to establish baseline TIR and gas data against which future changes to the hydrothermal system may be assessed. UAS-based magnetic and ground-based gravity data were collected to map subsurface geology and to characterize potential subsurface controls on thermal anomalies and gas emissions. Results of these efforts at mapping and monitoring the hydrothermal system at Mammoth Lakes demonstrate how an integrated UAS- and ground-based approach may be applied more broadly to study other known or potential hydrothermal and volcanic systems. We consider the benefits and limitations of each method, particularly the TIR and gas sensors, which have less well-developed processing techniques in place for UAS applications. By integrating results from several of these different methods, however, the limitations facing each individual approach may be mitigated, and a better understanding of the hydrothermal system may be reached.

California↗
USGS2023

Cursed? Why one does not simply add new data sets to supervised geothermal machine learning models

Recent advances in machine learning (ML) identifying areas favorable to hydrothermal systems indicate that the resolution of feature data remains a subject of necessary improvement before ML can reliably produce better models. Herein, we consider the value of adding new features or replacing other, low-value features with new input features in existing ML pipelines. Our previous work identified stress and seismicity as having less value than the other feature types (i.e., heat flow, distance to faults, and distance to magmatic activity) for the 2008 USGS hydrothermal energy assessment; hence, a fundamental question regards if the addition of new but partially correlated features will improve resulting models for hydrothermal favorability. Therefore, we add new maps for shear strain rate and dilation strain rate to fit logistic regression and XGBoost models, resulting in new 7-feature models that are compared to the old 5-feature models. Because these new features share a degree of correlation with the original relatively uninformative stress and seismicity features, we also consider replacement of the two lower-value features with the two new features, creating new 5-feature models. Adding the new features improves the predictive skill of the new 7-feature model over that of the old 5-feature model; albeit, that improvement is not statistically significant because the new features are correlated with the old features and, consequently, the new features do not present considerable new information. However, the new 5-feature XGBoost model has a statistically significant increase in predictive skill for known positives over the old 5-feature model at p = 0.06. This improved performance is due to the lower-dimensional feature space of the former than that of the latter. In higher-dimensional feature space, relationships between features and the presence or absence of hydrothermal systems are harder to discern (i.e., the 7-feature model likely suffers from the “curse of dimensionality”).

Geothermal Resources Council Transactions↗
USGS2023

Predicting large hydrothermal systems

We train five models using two machine learning (ML) regression algorithms (i.e., linear regression and XGBoost) to predict hydrothermal upflow in the Great Basin. Feature data are extracted from datasets supporting the INnovative Geothermal Exploration through Novel Investigations Of Undiscovered Systems project (INGENIOUS). The label data (the reported convective signals) are extracted from measured thermal gradients in wells by comparing the total estimated heat flow at the wells to the modeled background conductive heat flow. That is, the reported convective signal is the difference between the background conductive heat flow and the well heat flow. The reported convective signals contain outliers that may affect upflow prediction, so the influence of outliers is tested by constructing models for two cases: 1) using all the data (i.e., -91 to 11,105 mW/m2), and 2) truncating the range of labels to include only reported convective signals between -25 and 200 mW/m2. Because hydrothermal systems are sparse, models that predict high convective signal in smaller areas better match the natural frequency of hydrothermal systems. Early results demonstrate that XGBoost outperforms linear regression. For XGBoost using the truncated range of labels, half of the high reported signals are within < 3 % of the highest predictions. For XGBoost using the entire range of labels, half of the high reported signals are in < 13 % of the highest predictions. While this implies that the truncated regression is superior, the all-data model better predicts the locations of power-producing systems (i.e., the operating power plants are in a smaller fraction of the study area given by the highest predictions). Even though the models generally predict greater hydrothermal upflow for higher reported convective signals than for lower reported convective signals, both XGBoost models consistently underpredict the magnitude of higher signals. This behavior is attributed to low resolution/granularity of input features compared with the scale of a hydrothermal upflow zone (a few km or less across). Trouble estimating exact values while still reliably predicting high versus low convective signals suggests that a future strategy such as ranked ordinal regression (e.g., classifying into ordered bins for low, medium, high, and very high convective signal) might fit better models, since doing so reduces problems introduced by outliers while preserving the property of larger versus smaller signals.

Geothermal Resources Council Transactions↗
USGS2023

Don’t Let Negatives Hold You Back: Accounting for Underlying Physics and Natural Distributions of Hydrothermal Systems When Selecting Negative Training Sites Leads to Better Machine Learning Predictions

Selecting negative training sites is an important challenge to resolve when utilizing machine learning (ML) for predicting hydrothermal resource favorability because ideal models would discriminate between hydrothermal systems (positives) and all types of locations without hydrothermal systems (negatives). The Nevada Machine Learning project (NVML) fit an artificial neural network to identify areas favorable for hydrothermal systems by selecting 62 negative sites where the research team had confidence that no hydrothermal resource exists. Herein, we compare the implications of the expert selection of negatives (i.e., the NVML strategy) with a random sample strategy, where it is assumed that areas outside the favorable structural ellipses defined by NVML are negative. Because hydrothermal systems are sparse, it is highly probable that, in the absence of a favorable geological structure, hydrothermal favorability is low. We compare three training strategies: 1) the positive and negative labeled examples from NVML; 2) the positive examples from NVML with randomly selected negatives in equal frequency as NVML; and 3) the positive examples from NVML with randomly selected negatives reflecting the expected natural distribution of hydrothermal systems relative to the total area. We apply these training strategies to the NVML feature data (input data) using two ML algorithms (XGBoost and logistic regression) to create six favorability maps for hydrothermal resources. When accounting for the expected natural distribution of hydrothermal systems, we find that XGBoost performs better than the NVML neural network and its negatives. Model validation was less reliable using F1 scores, a common performance metric, than comparing probability estimates at known positives, likely because of the extreme natural class imbalance and the lack of negatively labeled sites. This work demonstrates that expert selection of negatives for training in NVML likely imparted modeling bias. Accounting for the sparsity of hydrothermal systems and all the types of locations without hydrothermal systems allows us to create better models for predicting hydrothermal resource favorability.

Geothermal Resources Council Transactions↗
USGS2022

A geophysical characterization of structure and geology of the Northern Granite Springs Valley Geothermal System, Northwestern Nevada

The northern Granite Springs Valley in northwestern Nevada is the focus of recent studies for its potential for hosting undiscovered geothermal resources. Although the area lacks definitive surface manifestations of an active hydrothermal system, previous studies identify this region as having potential for hosting a blind geothermal resource, based on elevated subsurface temperatures and a favorable structural framework of the area. As part of the Nevada Play Fairway Project, we conducted high resolution geophysical surveys to better characterize the valley’s geothermal resources. This included ground magnetic, gravity, magnetotelluric, and rock property studies aimed at mapping and modeling subsurface geology and structure. Various derivative and filtering methods were employed to delineate buried faults and contacts from gravity and magnetic data. A depth to basement gravity inversion reveals that the basin is deepest on the west side of the valley. Flanking the basin to the east is a prominent gravity high interpreted as an intra-basin horst. A new high-resolution ground magnetic survey reveals a prominent elongate NW-trending magnetic high, interpreted as an unexposed subsurface dike swarm situated near the boundary between the basin and horst and confined to basement. Geophysical models help constrain basin fill comprised of Cenozoic sediments and volcanic rocks. These overlie Mesozoic crystalline basement that, in the west, consists of Cretaceous granitic intrusives and, to the east, dominantly Mesozoic metasedimentary rocks. The contact between these basement lithologies is not certain but inferred to coincide with the geophysically mapped dike swarm. This is partly supported by the fact that the dikes, as projected along strike to the northwest, intersect the contact between the Cretaceous intrusions and older Mesozoic basement rocks to the north of the study area. Although the age of the inferred dike swarm is not known, the trend of the anomaly is consistent with some of the Tertiary dikes in the nearby Sahwave Range, suggesting emplacement predated or was coeval with early development of the basin. The coincidence of the geothermal system, horst, dike swarm, and terminating normal fault zone suggests that basin tectonics and hydrothermal activity were influenced by both pre-existing basement structure and recent deformation. This relationship may pertain more generally to other hydrothermal settings throughout the Great Basin. If so, future efforts focused on mapping basement geology and structure may prove important to understanding underlying structural controls on geothermal systems. This work is supporting the next phase of research involving additional 3D geophysical and geologic modeling under the U.S. Department of Energy funded INGENIOUS project. The focus of this new work is on the western flank and structural corners of the horst block, based on evidence from detailed geophysical structural mapping, new shallow temperature data, and detailed 3D geologic and geophysical modeling, all aimed at identifying sites for temperature gradient drilling that may intersect zones with sufficient permeability and temperature to support geothermal development.

Geothermal Resources Council Transactions↗
USGS2022

Exploring declustering methodology for addressing geothermal exploration bias

Geothermal resources assessments use data that are unevenly distributed in space, with more data collected in areas with known thermal features. To meet the assumptions for geostatistical modeling (e.g., variography and kriging) such as having a random sample representative of the population, declustering may be needed to correct for spatial sample bias. Several declustering methods exist and to understand how best to use these methods, we apply these to real data and samples of that data. The work described herein summarizes the application of cell-based declustering to shallow temperature data (~20 cm) collected in a survey across a thermal feature in the Lower Geyser Basin, Yellowstone National Park, Wyoming. The sample dataset is a regular grid (3-m spacing) of temperatures across a 72-m square area, providing a shallow, subsurface temperature dataset collected with minimal spatial bias (a few grid locations near a hot spring could not be sampled). To test the influence of sample clustering on geothermal estimates, this dense dataset is sub-sampled irregularly to evaluate bias on temperature estimation. Three sampling strategies were tested: a simple random sample, a stratified random sample, and a stratified biased random sample. The naive mean (before declustering) values for each dataset were compared to the post-declustering mean to evaluate the effectiveness of declustering on correcting the mean for spatial bias. For the limited number of sample datasets evaluated, we found that although cell-based declustering did partially correct the mean, some bias remained (i.e., the estimate was improved, but not fully corrected). It is possible that the procedure documented herein (applied here to only a few random samples) could be applied to many random samples, so that robust conclusions might be drawn (e.g., Is there always some remaining bias in declustered estimates? Does it depend on the number of sample points?). In particular, bias could be evaluated for persistency, and uncertainty could be evaluated.

Geothermal Resources Council Transactions↗