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At least 19 records

Machine Learning‐Assisted Microearthquake Location Workflow for Monitoring the Newberry Enhanced Geothermal System

Abstract Enhanced geothermal systems (EGS) offer a sustainable energy source but face challenges in accurately locating microearthquakes induced during reservoir stimulation. Locating these microearthquakes provides reliable feedback on the stimulation progress. Current deep learning methods for locating earthquakes require extensive data sets for training, which is problematic as detected microearthquakes are often limited. To address the scarcity of training data, we propose a practical workflow using probabilistic multilayer perceptron (PMLP) which predicts microearthquake locations from cross‐correlation time lags in waveforms. Utilizing a 3D velocity model of Newberry site derived from ambient noise interferometry, we generate numerous synthetic microearthquakes and 3D acoustic waveforms for PMLP training. Accurate synthetic tests prompt us to apply the trained network to the 2012 and 2014 stimulation field waveforms. To enhance the accuracy of source localization, we carefully handpick the P‐arrival times. Predictions on the 2012 stimulation data set show major microseismic activity at depths of 0.5–1.2 km, correlating with a known casing leakage scenario. In the 2014 data set, the majority of predictions concentrate at 2.0–2.9 km depths, consistent with results obtained from conventional physics‐based inversion, and align with the presence of natural fractures from 2.0 to 2.7 km. We validate our findings by comparing the synthetic and field picks, demonstrating a satisfactory match for the first arrivals. By combining the benefits of quick inference speeds and accurate location predictions, we demonstrate the feasibility of using realistic synthetic data set to locate microseismicity for EGS monitoring.

15 GEOTHERMAL ENERGY↗

Deep learning forecasts the spatiotemporal evolution of fluid-induced microearthquakes

Microearthquakes generated by subsurface fluid injection record the evolving stress state and permeability of reservoirs. Forecasting their spatiotemporal evolution is therefore critical for applications such as enhanced geothermal systems, carbon dioxide sequestration and other geoengineering applications. Here we propose a transformer neural network model that ingests hydraulic stimulation history and prior microearthquake observations to forecast four key quantities: cumulative microearthquake count, cumulative logarithmic seismic moment, and the 50th- and 95th-percentile extents of the microearthquake cloud. Applied to the EGS Collab Experiment 1 dataset, the model achieves R2 > 0.98 for the 1-s forecast horizon and R2 > 0.88 for the 15-s forecast horizon across all targets, and supplies uncertainty estimates through a learned standard deviation term. These accurate, uncertainty-quantified forecasts enable real-time inference of fracture propagation and permeability evolution, demonstrating the strong potential of deep-learning approaches to improve seismic-risk assessment and guide mitigation strategies in future fluid-injection operations.

Chung, Jaehong↗

Optimal design of 3D borehole seismic arrays for microearthquake monitoring in anisotropic media during stimulations in the EGS collab project

Multiple U.S. national laboratories, universities and industrial collaborators are conducting collaborative research under the EGS Collab project supported by the U.S. Department of Energy, to understand the fracture creation and imaging during fracturing in enhanced geothermal systems. Microearthquake hypocenter locations and focal mechanisms are used to monitor hydraulic fracturing growth and characterization at the EGS Collab experimental site at the Sanford Underground Research Facility using seismic receivers in multiple monitoring wells. In this study, we develop a methodology for optimal design a 3D borehole seismic array for cost-effective seismic monitoring in anisotropic media using not only the relationship between receiver distributions and standard deviation errors of microearthquake hypocenter locations, but also that between receiver distributions and focal-mechanism inversion errors. Our results indicate that microearthquake hypocenter locations and focal mechanisms can be reasonably well reconstructed for the EGS Collab Experiment I using six monitoring wells, including four fracture-parallel monitoring wells and two orthogonal wells. Lastly, eight seismic receivers evenly distributed in four parallel monitoring wells or twelve receivers in all six monitoring wells are required for hypocenter location, and twelve receivers evenly distributed in six wells or sixteen receivers in four wells are needed for focal-mechanism inversion.

58 GEOSCIENCES↗

Microearthquake Studies at the Salton Sea Geothermal Field

The objective of this project is to detect and locate microearthquakes to aid in the characterization of reservoir fracture networks. Accurate identification and mapping of the large numbers of microearthquakes induced in EGS is one technique that provides diagnostic information when determining the location, orientation and length of underground crack systems for use in reservoir development and management applications. Conventional earthquake location techniques often are employed to locate microearthquakes. However, these techniques require labor-intensive picking of individual seismic phase onsets across a network of sensors. For this project we adapt the Matched Field Processing (MFP) technique to the elastic propagation problem in geothermal reservoirs to identify more and smaller events than traditional methods alone.

15 GEOTHERMAL ENERGY↗

Data Arrays for Microearthquake (MEQ) Monitoring using Deep Learning for the Newberry EGS Sites

The 'Machine Learning Approaches to Predicting Induced Seismicity and Imaging Geothermal Reservoir Properties' project looks to apply machine learning (ML) methods to Microearthquake (MEQ) data for imaging geothermal reservoir properties and forecasting seismic events, in order to advance geothermal exploration and safe geothermal energy production. As part of the project, this submission provides data arrays for 149 microearthquakes between the year 2012 and 2013 at the Newberry EGS Site for use with the Deep Learning Algorithm that has been developed. The data provided includes raw waveform data, location data, normalized waveform data, and processed waveform data. Penn State Geothermal Team has shared the following files from the project: - 149 microearthquakes (MEQs) between 2012 and 2013 at Newberry EGS sites, 'Normalized Waveform Inputs.npz' are normalized waveforms. - labels of 149 MEQs: Processed Waveform Inputs.npz - location labels of 149 MEQs: Location Data.npz Note: .npz is the python file format by NumPy that provides storage of array data.

15 GEOTHERMAL ENERGY↗

Seismic monitoring of EGS fracture stimulations at Utah FORGE (Part 1): Time-lapse variations of b-values and Shear-wave Splitting Rates of induced microearthquakes

Characterizing geologic formations near geothermal wells is crucial for efficient extraction of subsurface geothermal energy resources. To monitor effective fracture stimulation of enhanced geothermal systems (EGS), we analyze a catalog of induced microearthquakes to characterize seismicity induced by Phase 2C fracture stimulations of the Utah FORGE project and derive the b-values for 424 induced microearthquakes in three stages of fracture stimulation. Here, we find the time-lapse variations of these b-values. We apply eigenvector rotation and waveform correlation to 3C geophone data to separate fast S 1 and slow S 2 waves, determine their directions, measure their lag time, and calculate the S-wave splitting rate (SSR) for six induced microearthquakes in Stage 1 and 21 in Stage 3. Both SSR values and b-values increase with time. The increase in these values may be related to heavily fractured zones created by fracture stimulations during Stage 2 injections. Large and highly variable SSR values in Stage 3 may reflect heterogeneities in the fracture zones. Time-lapse variations of SSR values and b-values may be one of the most useful approaches to monitoring and characterizing the effectiveness and efficiency of various types of fracture stimulations.

58 GEOSCIENCES↗

INDUCED MICROEARTHQUAKES AND SEISMICITY-PERMEABILITY RELATIONSHIPS IN FRACTURES

In the past several years, induced microearthquakes (MEQs) related to energy development projects have garnered public attention. Large-scale fluid injection in geoengineering activities, such as enhanced geothermal reservoir stimulation, geological storage of CO2, shale reservoir stimulation, and deep disposal of wastewater can generate significant fluid overpressures and induce microearthquakes by reactivating preexisting faults and fractures that are widely distributed throughout the upper crust. The monitoring of fluid-injection-induced microearthquakes can provide significant information (e.g., timing, special distribution, and moment magnitude) in evaluating reservoir development (e.g., fracture distribution and reservoir hydraulic conductivity evolution), in informing production strategies (e.g., accommodation of production wells), and in assessing the risks of fluid injection activities (e.g., caprock integrity of CO2 reservoir). The following questions are addressed in this study: (1) what are the mechanisms and implications of in-situ feedbacks (e.g., monitored fluid-injection-induced MEQs distribution, seismic moment magnitudes, and fracture behaviors) in geothermal stimulation? (2) What information can be derived from MEQs to inform geothermal reservoir stimulation strategies? (3) Are there any potential relationships between induced seismic or aseismic slip and permeability evolution of fractures in such unconventional reservoirs and caprocks? (4) What factors play an important role in controlling these relationships? These questions are addressed in the five individual chapters of this thesis.

58 GEOSCIENCES↗

Locating Microearthquakes In The Salton Sea Geothermal Field, California

We report the results of a 6-month microearthquake study of the Salton Sea Geothermal Field, California. This active geothermal field is also seismically active due to the flow of fluids through pre-existing fractures of rocks in the subsurface which can cause faults to slip. Locating these seismic events within the geothermal field enables researchers to determine whether the earthquakes are anthropogenic or tectonic from the nearby Brawley Seismic Zone. We analyzed November 2009 to April 2010 seismic waveform data retrieved from a network of continuously recording seismometers covering an area of 10 km 2 . 2,271 microearthquake hypocenters were processed out of 2,609 total recorded events and their locations suggest the majority could be at approximately the same depth as the fluid injection wells.

58 GEOSCIENCES↗

Crustal permeability generated through microearthquakes is constrained by seismic moment

We link changes in crustal permeability to informative features of microearthquakes (MEQs) using two field hydraulic stimulation experiments where both MEQs and permeability evolution are recorded simultaneously. The Bidirectional Long Short-Term Memory (Bi-LSTM) model effectively predicts permeability evolution and ultimate permeability increase. Our findings confirm the form of key features linking the MEQs to permeability, offering mechanistically consistent interpretations of this association. Transfer learning correctly predicts permeability evolution of one experiment from a model trained on an alternate dataset and locale, which further reinforces the innate interdependency of permeability-to-seismicity. Models representing permeability evolution on reactivated fractures in both shear and tension suggest scaling relationships in which changes in permeability (Δk) are linearly related to the seismic moment (M) of individual MEQs as Δk ∝ M. This scaling relation rationalizes our observation of the permeability-to-seismicity linkage, contributes to its predictive robustness and accentuates its potential in characterizing crustal permeability evolution using MEQs.

15 GEOTHERMAL ENERGY↗

Quantifying rupture characteristics of microearthquakes in the Parkfield Area using a high-resolution borehole network

It is well known that large earthquakes often exhibit significant rupture complexity such as well separated subevents. With improved recording and data processing techniques, small earthquakes have been found to exhibit rupture complexity as well. Studying these small earthquakes offers the opportunity to better understand the possible causes of rupture complexities. Specifically, if they are random or are related to fault properties. We examine microearthquakes (M < 3) in the Parkfield, California, area that are recorded by a high-resolution borehole network. We quantify earthquake complexity by the deviation of source time functions and source spectra from simple circular (omega-square) source models. We establish thresholds to declare complexity, and find that it can be detected in earthquakes larger than magnitude 2, with the best resolution above M2.5. Comparison between the two approaches reveals good agreement (>90 per cent), implying both methods are characterizing the same source complexity. For the two methods, 60–80 per cent (M 2.6–3) of the resolved events are complex depending on the method. The complex events we observe tend to cluster in areas of previously identified structural complexity; a larger fraction of the earthquakes exhibit complexity in the days following the M w 6 2004 Parkfield earthquake. Ignoring the complexity of these small events can introduce artefacts or add uncertainty to stress drop measurements. Focusing only on simple events however could lead to systematic bias, scaling artefacts and the lack of measurements of stress in structurally complex regions.

58 GEOSCIENCES↗

Induced microearthquakes predict permeability creation in the brittle crust

Predicting the evolution of permeability accurately during stimulation at the reservoir scale and at the resolution of individual fractures is essential to characterize the fluid transport and the reactive/heat-transfer characteristics of reservoirs where stress exerts significant control. Here, we develop a hybrid machine learning (ML) model to visualize in situ permeability evolution for an intermediate-scale (~10 m) hydraulic stimulation experiment. This model includes an ML model that was trained using the well history of flow rate and wellhead pressure and MEQ data from the first three stimulation episodes to predict average permeability from the statistical features of the MEQs alone for later episodes. Moreover, a physics-inspired model is integrated to estimate in situ fracture permeability spatially. This method relates fracture permeability to fracture dilation and scales dilation to the equivalent MEQ magnitude, according to laboratory observations. The seismic data are then applied to define incremental changes in permeability in both space and time. Our results confirm the excellent agreement between the ground truth and model-predicted permeability evolution. The resulting permeability map defines and quantifies flow paths in the reservoir with the averaged permeability comparing favorably with the ground truth of permeability.

induced seismicity↗

Tectonic implications of the microearthquake seismicity and fault plane solutions in southern Peru

Because the contortion in the seismic zone in southern Peru is aligned approximately parallel to the direction of relative plate motion, rather than perpendicular to the coast of Peru, the position of the contortion need not migrate with respect to the overriding South American plate as the Nazca plate subducts beneath it, and the flow in the surrounding asthenosphere could be in a steady state. In addition, the position of the contortion defines the northern boundary of the volcanic arc in southern Peru. The inference that a wedge of asthenospheric material must overlie the downgoing slab for subduction-related volcanism to occur is thereby strengthened.

Grange, F.↗

Improved Microseismicity Detection During Newberry EGS Stimulations

Effective enhanced geothermal systems (EGS) require optimal fracture networks for efficient heat transfer between hot rock and fluid. Microseismic mapping is a key tool used to infer the subsurface fracture geometry. Traditional earthquake detection and location techniques are often employed to identify microearthquakes in geothermal regions. However, most commonly used algorithms may miss events if the seismic signal of an earthquake is small relative to the background noise level or if a microearthquake occurs within the coda of a larger event. Consequently, we have developed a set of algorithms that provide improved microearthquake detection. Our objective is to investigate the microseismicity at the DOE Newberry EGS site to better image the active regions of the underground fracture network during and immediately after the EGS stimulation. Detection of more microearthquakes during EGS stimulations will allow for better seismic delineation of the active regions of the underground fracture system. This improved knowledge of the reservoir network will improve our understanding of subsurface conditions, and allow improvement of the stimulation strategy that will optimize heat extraction and maximize economic return.

15 GEOTHERMAL ENERGY↗

Improved Microseismicity Detection During Newberry EGS Stimulations

Effective enhanced geothermal systems (EGS) require optimal fracture networks for efficient heat transfer between hot rock and fluid. Microseismic mapping is a key tool used to infer the subsurface fracture geometry. Traditional earthquake detection and location techniques are often employed to identify microearthquakes in geothermal regions. However, most commonly used algorithms may miss events if the seismic signal of an earthquake is small relative to the background noise level or if a microearthquake occurs within the coda of a larger event. Consequently, we have developed a set of algorithms that provide improved microearthquake detection. Our objective is to investigate the microseismicity at the DOE Newberry EGS site to better image the active regions of the underground fracture network during and immediately after the EGS stimulation. Detection of more microearthquakes during EGS stimulations will allow for better seismic delineation of the active regions of the underground fracture system. This improved knowledge of the reservoir network will improve our understanding of subsurface conditions, and allow improvement of the stimulation strategy that will optimize heat extraction and maximize economic return.

15 GEOTHERMAL ENERGY↗

INL Seismic Monitoring Annual Report: January 1, 2011 - December 31, 2011

During 2011, the Idaho National Laboratory Seismic Monitoring Program evaluated 21,928 independent triggers that included earthquakes from around the world, the western United States, and local region of the Snake River Plain. Seismologists located 2,063 earthquakes and man-made blasts within and near the 161-km (or 100-mile) radius of the Idaho National Laboratory. Of these events, 16 were small-to-moderate size earthquakes ranging in magnitude (M) from 3.0 to 4.4. Within the 161-km radius, the majority of 941 earthquakes (M < 4.4) occurred in the active regions of the Basin and Range Province with only six microearthquakes occurring in the Snake River Plain. In the northern and southeastern Basin and Range, eight earthquake swarms occurred and included over 325 events. Five of the Snake River Plain earthquakes were located within and near the northern and southern ends of the Great Rift volcanic rift zone. All have anomalously deep focal depths (16 to 38 km) and waveforms indicative of fluid movement at mid- and lower-crustal levels and are a continuation of activity observed at Craters of the Moon National Monument since 2007. Since 1972, the Idaho National Laboratory has recorded 55 small-magnitude microearthquakes (M ≤ 2.2) within the eastern Snake River Plain and 25 deep microearthquakes (M ≤ 2.3) in the vicinity of Craters of the Moon National Monument.

58 GEOSCIENCES↗

Final Report to DOE EERE – Geothermal Technologies Program Project Title: Monitoring and modeling of fluid flow in a developing enhanced geothermal system (EGS) reservoir

The primary objective of this project was to improve our ability to predict performance of an Enhanced Geothermal System (EGS) reservoir over time by relating, in a quantitative manner, microseismic imaging with fluid and temperature changes within the reservoir. Historically, microseismic data have been used qualitatively to place bounds on the growth of EGS reservoirs created by large hydraulic fracturing experiments. Previous investigators used an experimentally based fracture opening relationship (fracture aperture as a function of pressure), the spatial extent of microseismic events, and some assumptions about fracture frequency to determine the size of an EGS reservoir created during large pumping tests. We addressed a number of issues (1) locating microearthquakes that occur during hydraulic fracturing, (2) obtaining more information about a reservoir than the microearthquake locations from the microearthquake data, for example, information about the seismic velocity structure of the reservoir or the scattering of seismic waves within the reservoir, (3) developing an improved methodology for estimating properties of fractures that intersect wellbores in a reservoir, and (4) developing a conceptual model for explaining the downward growth of observed seismicity that accompanies some hydraulic injections into geothermal reservoirs. We used two primary microseismic datasets for our work. The work was motivated by a dataset from the Salak Geothermal Field in Indonesia where seismicity accompanying a hydraulic injection was observed to migrate downward. We also used data from the Soultz EGS site in France. We also used Vertical Seismic Profiling data from a well in the United States. The work conducted is of benefit for characterizing reservoirs that are created by hydraulic fracturing for both EGS and for petroleum recovery.

15 GEOTHERMAL ENERGY↗