Brady Hot Springs Research Articles

Reliable Earthquake Source Parameters Using Distributed Acoustic Sensing Data Derived from Coda Envelopes

Abstract A challenge in fully using distributed acoustic sensing (DAS) data collected from fiber-optic sensors is correcting the signals to provide quantitative true ground motion. Such corrections require considering coupling and instrument response issues. In this study, we show via comparison with geophone and broadband seismometer data that we can use coda envelope calibration techniques to obtain absolute moment magnitudes and source spectra from DAS data. Here, we use DAS and nodal geophones deployed as part of a geothermal monitoring experiment at Brady Hot Springs, Nevada, and on a 20 km long dark fiber of the ESnet’s Dark Fiber Testbed–a U.S. Department of Energy user facility, in Sacramento, California. Several DAS line segments with colocated geophone stations were used to compare the amplitude variation using narrowband S-wave coda envelopes. The DAS coda envelope decay at each point showed remarkable similarity with coda envelopes from different events in each narrow frequency range examined. The coda envelopes are used to determine Mw magnitudes and source spectra from regional stations without any major scatter. Because coda waves arrive from a range of directions, the azimuthal sensitivity of DAS is somewhat ameliorated. We show that the openly available seismic coda calibration software toolkit can be used for straightforward and faster processing of large DAS datasets for source parameters and subsurface imaging.

Monitoring seismicity triggered by geothermal site shutdown with a surface DAS array at Brady Hot Springs

SUMMARY Seismicity induced by fluid injection including wastewater injection, hydrofracking and enhanced geothermal system (EGS) site production draws public attention. Dense arrays have been deployed to improve monitoring capability. In 2016 March, the PoroTomo experiment deployed an 8.6-km-long fibre-optic cable for distributed acoustic sensing (DAS) in the geothermal field at Brady Hot Springs, Nevada, covering an area of 1.5 by 0.5 km. The goal was to assess an integrated technology for characterizing and monitoring temporal changes in the rock mechanical properties of an EGS reservoir in three dimensions. We applied a neural network designed for earthquake detection called ADE-Net2 to the DAS data set to detect seismic events in continuous records. We were able to detect a total of 90 earthquakes, which included 21 events that had not been reported by a previous template-matching study. Additionally, we were able to successfully detect almost all of the active source signals, with only seven events being missed. We used the STA/LTA (short-/long-term average) method to pick arrivals and a clustering method to remove outliers. We initially tried a standard event location algorithm, but the low signal-to-noise ratio resulted in significant picking uncertainty that is up to ∼0.5 s, leading to large location uncertainty. Therefore, we developed a new location method based on the similarity between the theoretical traveltime curve and picked moveout. A grid search scheme was adopted to find the optimal point at which the traveltime curve is most similar to the picked one. Most newly detected earthquakes locate southwest of the DAS array, where five earthquakes were reported by a local seismic network. The plant began shutting down at 19:15 UTC on the March 13, and most earthquakes occurred on the March 14, indicating a relationship between the seismicity and the pressure changes caused by the shutdown of the plant. The pressure changes at epicentres obtained from a simplified model range from 71 to 157 kPa, exceeding a typical earthquake trigger threshold of 10 kPa.

Source parameter analysis using distributed acoustic sensing – an example with the PoroTomo array

SUMMARY In this study, I demonstrate that distributed acoustic sensing (DAS) raw strain rate data can directly be used to estimate spectral source parameters through an Empirical Green's Function (EGF) deconvolution analysis. Previously, DAS had been widely used in passive seismology to image the subsurface and analyze ground motion variations by converting strain or strain rate to particle velocity or acceleration prior to analysis. In this study, spectral analysis is applied to the PoroTomo joint DAS and seismic Nodal array in the Brady Hot Springs geothermal field to obtain source parameters for two M4 earthquakes via EGF analysis, where nearly collocated smaller events are used as an EGF to remove path and site effects. The EGF workflow is applied to raw DAS strain rate data without conversion to particle velocities and raw Nodal seismic data. The DAS and Nodal results are very consistent with similar features of spectral ratios, corner frequencies and moment ratios for the same event pairs. The uncertainty due to stacked spectral measurement is much lower on the DAS array, suggesting better stability of spectral shape measurement, possibly due to the much denser spatial sampling. The uncertainty due to model fitting is similar between DAS and Nodal arrays with slightly lower uncertainty on the DAS array. These observations demonstrate potential for directly using the strain rate measurements from DAS arrays for earthquake source characterizations.

Comparisons Between Array Derived Dynamic Strain Rate (ADDS) and Fiber‐Optic Distributed Acoustic Sensing (DAS) Strain Rate

AbstractDistributed acoustic sensing (DAS) strain rate and particle velocity can be compared through approximate scaling with medium velocity. We instead performed a direct comparison between array derived dynamic strain (ADDS) rate and DAS strain rate for six frequency bands. The PoroTomo project at Brady's Hot Springs, Nevada, deployed a 240‐geophone 3C array co‐located with fiber‐optic DAS system and 8.7 km of buried cable. We selected subsets of the geophone array to create four smaller arrays and computed ADDS. The horizontal components of the ADDS were rotated into the direction of the fiber‐optic cable and then compared with the observed DAS strain rates. From three example regional earthquakes of local magnitudes 2.9, 4.1, and 4.3, the ADDS are found to be coherent with DAS for frequencies ≤1 Hz. For frequencies >1‐Hz, this correlation decays quickly. Small differences between linear and areal dynamic strains at 1‐Hz suggest poor signal‐to‐noise or localized strain that is perturbed by shallow heterogeneities compare to the average strain propagating across the geophone array. The implication is that around 1‐Hz, straight fiber DAS is measuring axial strain along the fiber and can provide good approximations to translational particle motions. However, above 1‐Hz, DAS becomes more sensitive to shallow velocity gradients that can be beneficial for geophysical imaging yet becomes a limitation for traditional seismic analysis methods depending on absolute amplitude and phase from translational particle motions.

Modeling Subsurface Performance of a Geothermal Reservoir Using Machine Learning

Geothermal power plants typically show decreasing heat and power production rates over time. Mitigation strategies include optimizing the management of existing wells—increasing or decreasing the fluid flow rates across the wells—and drilling new wells at appropriate locations. The latter is expensive, time-consuming, and subject to many engineering constraints, but the former is a viable mechanism for periodic adjustment of the available fluid allocations. In this study, we describe a new approach combining reservoir modeling and machine learning to produce models that enable such a strategy. Our computational approach allows us, first, to translate sets of potential flow rates for the active wells into reservoir-wide estimates of produced energy, and second, to find optimal flow allocations among the studied sets. In our computational experiments, we utilize collections of simulations for a specific reservoir (which capture subsurface characterization and realize history matching) along with machine learning models that predict temperature and pressure timeseries for production wells. We evaluate this approach using an “open-source” reservoir we have constructed that captures many of the characteristics of Brady Hot Springs, a commercially operational geothermal field in Nevada, USA. Selected results from a reservoir model of Brady Hot Springs itself are presented to show successful application to an existing system. In both cases, energy predictions prove to be highly accurate: all observed prediction errors do not exceed 3.68% for temperatures and 4.75% for pressures. In a cumulative energy estimation, we observe prediction errors that are less than 4.04%. A typical reservoir simulation for Brady Hot Springs completes in approximately 4 h, whereas our machine learning models yield accurate 20-year predictions for temperatures, pressures, and produced energy in 0.9 s. This paper aims to demonstrate how the models and techniques from our study can be applied to achieve rapid exploration of controlled parameters and optimization of other geothermal reservoirs.

Using DAS to investigate traffic patterns at Brady Hot Springs, Nevada, USA

Although not as widespread as their use in other settings, there is a growing realization that distributed acoustic sensing (DAS) systems are suitable for traffic monitoring applications. One such application is demonstrated here using data from a surface DAS array recorded at Brady Hot Springs, Nevada, USA. Although this data set was acquired with the original intent of monitoring changes in a geothermal reservoir, it is shown that the data can also be used to identify and monitor vehicle movements on a nearby highway. Analysis of moveout patterns and recorded amplitudes confirm that this data set is dominated by signals generated by passing vehicles. During nighttime periods, the reduced traffic levels provide isolated signals that are more straightforward to analyze and interpret. During the day, however, increased traffic levels result in the signals from multiple vehicles overlapping to create a complex pattern of amplitudes recorded on the DAS array, making analysis and interpretation more challenging. Nonetheless, these signals can be separated and multiple vehicles identified along with their speeds and timings through the application of an automated workflow based on velocity stacking. The use of DAS for traffic monitoring purposes is an emerging technology, and despite challenges stemming from the nature of the measurement and the signals recorded, it can provide valuable information for the effective management of a transport network.

Pushing the limit of earthquake detection with distributed acoustic sensing and template matching: a case study at the Brady geothermal field

Template matching has been widely applied in the detection of earthquakes and other seismic events due to its power in detecting weak signals. Recent studies using synthetics have shown that application of template matching to large-N arrays can potentially detect earthquakes substantially below the noise level. Here we apply template matching to the distributed acoustic sensing (DAS) data recorded in the Brady Hot Springs geothermal field, Nevada. Using 5 catalogued events, we detect 116 events and find 68 of them well below the noise level. We confirm 112 events are true earthquakes by examining the patterns of their sensor-to-sensor cross-correlation sections. This demonstrates that the combination of DAS and template matching has capability to detect microseismicity below the noise level, which is unusual for conventional seismic arrays and methods. With the updated catalogue, we observe a surge of earthquakes during the shutdown of a geothermal power plant nearby. In addition, the rapid increases in the downhole pressure record coincide with intense swarms of earthquakes. These observations show a strong correlation between the seismicity frequencies and the downhole pressure changes. Finally, we investigate several factors that may affect the detection performance and compare different strategies for spatial down-sampling, in order to provide helpful insights for future large-N design and data processing.

Characterizing volumetric strain at Brady Hot Springs, Nevada, USA using geodetic data, numerical models and prior information

Characterizing volumetric strain at Brady Hot Springs, Nevada, USA using geodetic data, numerical models and prior information

Geothermal production and reduced seismicity: Correlation and proposed mechanism

At Brady Hot Springs, a geothermal field in Nevada, heated fluids have been extracted, cooled, and re-injected to produce electrical power since 1992. Analysis of daily pumping records and catalogs of microseismicity between 2010 and 2015 indicates a statistically significant correlation between days when the daily volume of production was at or above its long-term average rate and days when no seismic event was detected. Conversely, shutdowns in pumping for plant maintenance correlate with increased microseismicity. We hypothesize that the effective stress in the subsurface has adapted to the long-term normal operations (deep extraction) at the site. Under this hypothesis, extraction of fluids inhibits fault slip by increasing the effective stress on faults; in contrast, brief pumping cessations represent times when effective stress is decreased below its long-term average, increasing the likelihood of microseismicity.

Time-series analysis of surface deformation at Brady Hot Springs geothermal field (Nevada) using interferometric synthetic aperture radar

We analyze interferometric synthetic aperture radar (InSAR) data acquired between 2004 and 2014, by the ERS-2, Envisat, ALOS and TerraSAR-X/TanDEM-X satellite missions to measure and characterize time-dependent deformation at the Brady Hot Springs geothermal field in western Nevada due to extraction of fluids. The long axis of the ∼4km by ∼1.5km elliptical subsiding area coincides with the strike of the dominant normal fault system at Brady. Within this bowl of subsidence, the interference pattern shows several smaller features with length scales of the order of ∼1km. This signature occurs consistently in all of the well-correlated interferometric pairs spanning several months. Results from inverse modeling suggest that the deformation is a result of volumetric contraction in shallow units, no deeper than 600m, likely associated with damaged regions where fault segments mechanically interact. Such damaged zones are expected to extend downward along steeply dipping fault planes, providing a high permeability conduit to the production wells. Using time series analysis, we test the hypothesis that geothermal production drives the observed deformation. We find a good correlation between the observed deformation rate and the rate of production in the shallow wells. We also explore mechanisms that could potentially cause the observed deformation, including thermal contraction of rock, decline in pore pressure and dissolution of minerals over time.

Geothermal exploration in the Western United States

Over 90 percent of geothermal phenomena in the United States are in 13 western states, comprising more than 1000 warm and hot spring and fumarole localities. Some 100 can be considered hyperthermal. Exploration in these areas has been of two types: incidental and directed. Incidental exploration has included geologic studies and drilling in thermal areas for purposes other than geothermal development. The Salton Sea. California, geothermal field was explored initially in this manner. Directed exploration has included geological, geophysical, and geochemical methods, and has resulted in geothermal test drilling in six states. At least six produceable fields have been discovered: The Geysers, California; Salton Sea. California: Casa Diablo, California: Beowawe. Nevada: Brady's Hot Springs, Nevada: and Yellowstone National Park. Wyoming. Reservoir base temperatures exceed 200°C probably at all of these fields. The Geysers produces dry steam; Salton Sea produces brine: the others produce hot water. Only at The Geysers is electric power being generated: 83,000 kW of capacity has been installed, and facilities for an additional 110,000 kW are being constructed. At Salton Sea there is limited commercial production of calcium chloride from geothermal brine. Many insufficiently explored areas and marginal fields warrant additional directed exploration. These include: Surpise Valley, California; the Carson Sink, Nevada; the high Cascade Range in California, Oregon, and Washington; Valles Caldera, New Mexico; parts of the Aleutian Islands, Alaska; and the island of Hawaii. At least two geothermal prospects, Clear Lake, California, and Steamboat Springs, Nevada, have been abandoned because of problems of waste-water disposal and plugging of wells. These problems are encountered at other fields, including Salton Sea and Casa Diablo. Heat exchanging may provide a means to utilize these marginal fields. Several localities use geothermal water for space heating: outstanding of these is Klamath Falls, Oregon.