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At least 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Efficient Generalized Boundary Detection Using a Sliding Information Distance

In this work, we present a general machine learning algorithm for boundary detection within general signals based on an efficient, accurate, and robust approximation of the universal normalized information distance. Our approach uses an adaptive sliding information distance (SLID) combined with a wavelet-based approach for peak identification to locate the boundaries. Special emphasis is placed on developing an adaptive formulation of SLID to handle general signals with multiple unknown and/or drifting section lengths. Although specialized algorithms may outperform SLID when domain knowledge is available, these algorithms are limited to specific applications and do not generalize. SLID excels in these cases. We demonstrate the versatility and efficacy of SLID on a variety of signal types, including synthetically generated sequences of tokens, binary executables for reverse engineering applications, and time series of seismic events.

42 ENGINEERING↗
DOE OSTI2025

The Dynamic Networks Experiments: Virtual Experiments to Quantify Gains in Nuclear Explosion Monitoring

We describe an ongoing series of virtual experiments conducted collaboratively by four United States National Laboratories: Sandia National Laboratories, Los Alamos National Laboratory, Lawrence Livermore National Laboratory, and Pacific Northwest National Laboratory. These Dynamic Network Experiments (DNEs) provide an experimental framework to evaluate the potential impact of new research tools on nuclear explosion monitoring. The second DNE (DNE2), completed in 2024, exploited waveform data (seismic, infrasound, and electromagnetic) that was recorded by multi-modal sensors within and near the Nevada National Security Site and synthetic radionuclide signatures over multiple time periods. During the execution of DNE2, we processed and analyzed data through a multi-stage event processing pipeline that ingested raw data, performed quality control, detected signals, built events from these signals, located these events, and characterized the events’ source types and sizes. For each stage and over the entire event processing pipeline, we evaluated performance changes by comparing the performance of new data processing methods, models, and algorithms against a baseline. We also performed an additional execution phase to assess event processing pipeline function, speed, and efficiency against that of an expert analyst, including computational and manual efforts. Finally, we assessed the impact and effort of modern computing infrastructure on the monitoring pipeline. This paper describes key elements of the DNEs, from formulation through execution, as demonstrated in DNE2. The DNEs introduce several novel concepts to quantitatively measure the potential impact of new methods on explosion monitoring, including the collaborative design of multi-modal datasets, performance and logistical metrics, and integrated analyses.

42 ENGINEERING↗
DOE OSTI2024

A comparative study on deep learning models for condition monitoring of advanced reactor piping systems

Advanced nuclear reactors offer innovative applications due to their portability, reliability, resiliency, and high capacity factors. To operate them on a wider scale, reducing maintenance life-cycle costs while ensuring their integrity is essential. Autonomous operations in advanced nuclear reactors using augmented Digital Twin (DT) technology can serve as a cost-effective solution by increasing awareness about the system’s health. A key component of nuclear DT frameworks is the condition monitoring of safety systems, such as piping-equipment systems, which involves acquiring and monitoring the plant’s sensor data. Here, this research proposes a condition monitoring methodology utilizing deep learning algorithms, such as multilayer perceptions (MLP) and convolutional neural networks (CNNs), to detect degradation and its severity in nuclear piping-equipment systems. Sensor signals are processed to obtain the power spectral density and the Short-Time Fourier transform, and feature extraction methodologies are proposed to develop degradation-sensitive data repositories. The performance of MLP, one-dimensional (1D) CNN, and 2D CNN within the proposed condition monitoring framework is compared using a finite element model of a 3D piping system subjected to seismic loads as the application case study. Various approaches, such as dropout, k-Fold validation, regularization, and early stopping of training the network, are investigated to avoid overfitting the models to the input sensor data. The predictive capability and computational capacity of the deep learning algorithms are also compared to detect degradation in the Z-pipe system of the Experimental Breeder Reactor II (EBRII). The Z-pipe system is subjected to harmonic excitations that represent normal operating loads, such as pump-induced vibrations. The findings of the study indicate that the proposed artificial intelligence (AI)-driven condition monitoring framework demonstrates superior prediction accuracies with a 2D CNN, whereas the MLP exhibits higher computational efficiency.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

SUBTASK 1.6 – BASIN ELECTRIC CARBON STORAGE RESEARCH PROJECT: NOVEL MONITORING TECHNIQUES

The Energy & Environmental Research Center (EERC) conducted baseline activities associated with an applied research project at Basin Electric Power Cooperative’s (Basin’s) carbon capture and storage (CCS) site in Beulah, North Dakota, to establish novel carbon storage-monitoring techniques as commercial methods under Cooperative Agreement No. DE-FE0024233, Subtask 1.6. The following report summarizes the baseline activities performed and briefly describes the subsequent (operational monitoring) activities that have been proposed to the U.S. Department of Energy (DOE) as part of the overall project to develop and demonstrate novel monitoring techniques at North America’s largest permitted CCS operation. Dakota Gasification Company (DGC), a wholly owned subsidiary of Basin, owns and operates the Great Plains Synfuels Plant (GPSP) approximately 5 miles northwest of the town of Beulah, North Dakota (Figure 1). In 2023, DGC received approval from the North Dakota Industrial Commission (NDIC) to develop a storage facility on-site for injecting a stream of carbon dioxide (CO2) captured from GPSP. DGC will transport the captured CO2 stream with approximately 6.8 miles of transmission lines that extend north of GPSP and inject >1 million tonnes (MMt) of CO2 annually (>1 MMt/yr) over a 12-year period with up to six underground injection control (UIC) Class VI-compliant injection wells completed in the Broom Creek Formation, a predominantly sandstone reservoir and saline aquifer underlying GPSP. The Broom Creek Formation lies approximately 5900 feet (ft) below ground surface (bgs) at GPSP. The commercial scale (i.e., >1 MMt/yr) of DGC’s permitted carbon storage project is ideal for developing and testing the novel monitoring techniques included within Subtask 1.6. The goals of this project are to demonstrate 1) the cost-effectiveness of novel monitoring technologies included as part of this research, 2) technology capability for tracking the CO2 plume and/or associated pressure response in the subsurface and monitoring out-of-zone migration, and 3) compliance with UIC Class VI program requirements. The research activities proposed for the overall project include 1) design of an automated, integrated, modular (AIM) monitoring station; 2) time-lapse electromagnetic (EM) field surveys; 3) drone-based surveillance studies; 4) time-lapse monitoring with seismic methods; 5) advanced wellbore-monitoring methods; 6) deployment of an AIM monitoring network; 7) EM monitoring of CO2 with real-time data processing; 8) continued seasonal drone-based surveillance studies; 9) seismic monitoring with passive and active surveys; and 10) wellbore monitoring with nuclear magnetic resonance (NMR) for near-surface characterization. Completion of Activities 1.0–5.0 (baseline activities) are described in this report. Upon authorization of funding by DOE, the EERC will initiate Activities 6.0– 10.0 (operational monitoring activities). Current state-of-the-art (SOA) carbon storage-monitoring techniques require countless labor hours dedicated to the acquisition of data. Once data are gathered, these SOA techniques often rely on commercial facilities to process raw data from the field. However, it is anticipated that next-generation monitoring techniques, such as those being demonstrated, will lower acquisition footprints, be less operationally intensive, and improve data acquisition efficiencies. These new techniques are more conducive to the application of machine learning, artificial intelligence, and automation, thus providing a pathway for integration into active control systems, informing site operability, and improving the integration of data for future CCS projects across the United States. Additionally, reclaimed and active mining lands are present within the project site, creating a unique opportunity to demonstrate the effectiveness of remote sensing and surface-based geophysics monitoring techniques at similar project sites that may include disturbed, unconsolidated, or actively excavated near-surface environments. The efforts included in the overall project will produce necessary designs, learnings, and data acquired during the baseline and operational monitoring periods that are necessary for time-lapse demonstration and validation of the described monitoring techniques. In addition, it is anticipated that the monitoring technologies included in this study will be compliant with UIC Class VI requirements to enable the potential for implementation at other CCS sites across the United States.

42 ENGINEERING↗
DOE OSTI2024

2-D seismic wave propagation using the distributional finite-difference method: further developments and potential for global seismology

SUMMARY We present a time-domain distributional finite-difference scheme based on the Lebedev staggered grid for the numerical simulation of wave propagation in acoustic and elastic media. The central aspect of the proposed method is the representation of the stresses and displacements with different sets of B-splines functions organized according to the staggered grid. The distributional finite-difference approach allows domain-decomposition, heterogeneity of the medium, curvilinear mesh, anisotropy, non-conformal interfaces, discontinuous grid and fluid–solid interfaces. Numerical examples show that the proposed scheme is suitable to model wave propagation through the Earth, where sharp interfaces separate large, relatively homogeneous layers. A few domains or elements are sufficient to represent the Earth’s internal structure without relying on advanced meshing techniques. We compare seismograms obtained with the proposed scheme and the spectral element method, and we show that our approach offers superior accuracy, reduced memory usage, and comparable efficiency.

Geochemistry & Geophysics↗
DOE OSTI2020

New Architecture to Support Integration and Processing of Seismic Data from Heterogeneous Sources

The Geophysical Monitoring Program (GMP) at Lawrence Livermore National Lab (LLNL) maintains a database and supporting infrastructure for geophysical data used in support of the Nuclear Detonation Detection mission. This database includes data from multiple sources, many of which do not distribute data to the public or for which there is no automated means of access. For example, Figure 1 shows (left) the distribution of waveform data in our database by source. The Incorporated Research Institutions for Seismology Data Management Center (IRISDMC) is our major source of waveform data and those data may be retrieved at will using the Federated Digital Seismograph Networks FDSN web Application Programming Interface (API). However, the next 6 most important sources of waveform data have no or only limited automated access to waveforms. As Figure 1 (right) shows, it is very common for waveform records associate with an event in our database to come from two or more sources, and in some cases data come from 10 sources. This diversity of data sources drives our need for efficient and correct integration of metadata, parametric data, and waveform data.

58 GEOSCIENCES↗
DOE OSTI2023

An autoencoder compression approach for accelerating large-scale inverse problems

Abstract Partial differential equation (PDE)-constrained inverse problems are some of the most challenging and computationally demanding problems in computational science today. Fine meshes required to accurately compute the PDE solution introduce an enormous number of parameters and require large-scale computing resources such as more processors and more memory to solve such systems in a reasonable time. For inverse problems constrained by time-dependent PDEs, the adjoint method often employed to compute gradients and higher order derivatives efficiently requires solving a time-reversed, so-called adjoint PDE that depends on the forward PDE solution at each timestep. This necessitates the storage of a high-dimensional forward solution vector at every timestep. Such a procedure quickly exhausts the available memory resources. Several approaches that trade additional computation for reduced memory footprint have been proposed to mitigate the memory bottleneck, including checkpointing and compression strategies. In this work, we propose a close-to-ideal scalable compression approach using autoencoders to eliminate the need for checkpointing and substantial memory storage, thereby reducing the time-to-solution and memory requirements. We compare our approach with checkpointing and an off-the-shelf compression approach on an earth-scale ill-posed seismic inverse problem. The results verify the expected close-to-ideal speedup for the gradient and Hessian-vector product using the proposed autoencoder compression approach. To highlight the usefulness of the proposed approach, we combine the autoencoder compression with the data-informed active subspace (DIAS) prior showing how the DIAS method can be affordably extended to large-scale problems without the need for checkpointing and large memory.

Mathematics↗
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↗
DOE OSTI2025

Mathematical Morphological Filtering with a Self-Adaptive Reconstruction Technique and Application to Local Seismic Data

Recorded seismic data are generally contaminated by noise from different sources, which masks the signals of interest. In the seismology community, frequency filtering (FF) is the standard method for noise suppression. However, when the signal of interest and noise share the same frequency band, the latter cannot be filtered out without infringing on the former. We implemented a noise suppression approach based on the mathematical morphology theorem. The method involves compound operations of dilation and erosion using structuring elements of varying lengths and decomposes an input noisy waveform into several time functions with differing characteristics. Further, the filtered waveform is constructed from the time functions using a self-adaptive reconstruction technique. Application to a data set of >4700 local waveforms suggests that the implemented mathematical morphological filtering (MMF) approach is efficient for data with low signal-to-noise ratio (SNR) and significantly outperforms FF in that SNR range. For most of the dataset, FF, machine learning (ML) denoising, and continuous wavelet transform (CWT) thresholding result in higher SNR values compared with the MMF method. However, for ~42% of the waveforms, MMF outperforms FF, and the SNR gain achieved with MMF is as large as ~23 dB. Compared to ML denoising and CWT thresholding, this proportion drops to only ~10%–14%. Our results suggests that in an operational setting, MMF cannot replace the other noise suppression methods; however, signal detection can be improved if MMF is used to supplement them in some scenarios. MMF could help detect signals in problematic low-SNR data, which are currently being missed particularly when using FF alone.

58 GEOSCIENCES↗
DOE OSTI2021

Analysis of Seismic Signals Generated by Vehicle Traffic with Application to Derivation of Subsurface Q-Values

Correct identification and modeling of anthropogenic sources of ground motion are of considerable importance for many studies, including detection of small earthquakes and imaging seismic properties below the surface. Here, to understand signals generated by common vehicle traffic, we use seismic data recorded by closely spaced geophones normal to roads at two sites on San Jacinto fault zone. To quantify the spatiotemporal and frequency variations of the recorded ground motions, we develop a simple analytical solution accounting for propagation and attenuation of surface waves. The model reproduces well-observed bell-shaped spectrograms of car signals recorded by geophones close to roads, and it can be used to estimate frequency-dependent Q-values of the subsurface materials. The data analysis indicates Q-values of 3–40, for frequencies up to 150 Hz for road-receiver paths at the two examined sites. The derived Q-values are consistent with attenuation factors of surface waves previously obtained with other methods. The analytical results and analysis procedure provide a highly efficient method for deriving Q-values of shallow subsurface materials.

58 GEOSCIENCES↗
DOE OSTI2020

The Value of Information From Horizontal Distributed Acoustic Sensing Compared to Multicomponent Geophones Via Machine Learning

Abstract Faults play an important role in recharging many geothermal reservoirs, and seismic information can image the locations of these faults. The value of information (VOI) metric is used to objectively quantify and compare the value of two types of seismic receiver data via a machine learning approach. The demonstrated VOI methodology is novel by including spatial models from seismic data and obtaining the information statistics from machine learning. Our two-dimensional numerical experiments compare images created from sparsely spaced (80 m), two-component geophone sampling to high spatial resolution (1 m), single-component DAS. We used a three-fold cross validation of a U-Net convolutional neural networks to achieve average classification statistics. The results suggest that when horizontal sources are utilized, geophones and DAS identify reflectors and non-reflectors at roughly the same rate. The average F1 score for horizontal DAS is 0.939 and 0.931 for geophones. For images created from a vertical source, DAS performed marginally better (F1 = 0.919) than geophones (F1 = 0.877). Our transferrable methodology can provide guidance on which acquisition scenarios can improve images of important structures in the subsurface and present an efficient method for obtaining reliability statistics from high-dimensional, spatial data.

Energy & Fuels↗
DOE OSTI2021

Report on the LLNL Global Full-waveform Inversion Workflow and Progress

LLNL has developed the SPiRaL global seismic tomography model based upon millions of body wave travel times and surface wave dispersion curves in the period range of 25-200 seconds (Simmons et al., 2021). The motivation to construct the SPiRaL model (which stands for S-, P-, Rayleigh, and Love waves) is based upon the guidance provided by a 3-D model workshop held in Berkeley in 2007 and documented in a paper by Zucca et al. (2009). It was concluded in Zucca et al. (2009) that a “practical and obtainable” model is one that predicts body wave travel times for event location while also predicting surface wave velocities for moment tensor estimation. We have largely followed that guidance which has led to the construction of SPiRaL. Looking forward, we now seek to improve the global model through full waveform inversion (FWI). This necessitates the development/customization of an efficient and flexible modeling workflow to automate the process using LLNL’s supercomputing platforms. This report briefly outlines the work that being performed at LLNL to develop the necessary tools and framework to update the SPiRaL global model with full waveform information.

58 GEOSCIENCES↗
DOE OSTI2025

Manipulation of Geographic Information in Global Seismology

Geographic data, such as seismic event locations, station locations, etc., are generally given in geographic latitude Φ ’, longitude θ , and depth below sea level, ζ , using the WGS84 ellipsoid as a reference. In software systems that use this type of geographic data, it is necessary to manipulate the data mathematically in order to perform such tasks as finding the angular distance or azimuth from one point to another, to find an array of points along a great circle, to rotate a point about a pole of rotation, to move a point some angular distance in a specified direction, to find the intersections of two great circles or to find the intersections of a great circle and a small circle. In this paper, equations are presented that convert geographic locations first to geocentric coordinates and then to Earth-centered Cartesian coordinates where many mathematical manipulations can be performed conveniently and efficiently.

58 GEOSCIENCES↗
DOE OSTI2025

Applying Machine Learning and Bayesian Inference to Identify and Locate Moving Anthropogenic Sources Using Distributed Acoustic Sensing Data

Distributed acoustic sensing (DAS) systems, which use existing telecommunication fibers, offer high‐resolution capabilities ideal for recording anthropogenic sources. However, the complexity of urban environments and the large amount of data recorded by DAS require automated methods to efficiently detect and categorize anthropogenic sources. Here, we evaluate how well three machine learning models (k‐nearest neighbor [k‐NN], convolutional neural networks, and recurrent‐convolutional neural networks) can identify various anthropogenic sources recorded by DAS. Our findings reveal that both k‐NN and neural network methods perform well in high signal‐to‐noise ratio (SNR) settings. However, their accuracy decreases at SNRs <4. We also use Kalman filtering, a form of Bayesian inference, on backprojected locations of these sources to recover locations that generally fall within standard smartphone Global Positioning System errors. By combining machine learning and Kalman filter results, we calculate a multidimensional model of moving anthropogenic sources. These results demonstrate the potential of DAS data in urban seismology for accurately identifying and locating such sources. Depending on the research objectives, these sources can be further studied or filtered out to improve the quality of seismic data for earthquake studies. Such methods provide a valuable tool for urban seismology and seismic hazard analysis.

Luckie, Thomas William [Sandia National Laboratori↗
DOE OSTI2020

Efficient 3-D velocity model building using joint inline and crossline plane-wave wave-equation migration velocity analyses

SUMMARY Wave-equation migration velocity analysis (WEMVA) is an image-domain inversion method for velocity model building. Automatic plane-wave WEMVA (PWEMVA) calculates the moveouts of plane-wave common-image gathers (CIGs) by searching a best-fitting parabola with semblance analysis and backprojects residual CIG moveouts into wavefield wave paths with a reflection tomographic kernel. However, 3-D PWEMVA is very computationally expensive because 3-D reflection tomographic inversion requires at least five 3-D reverse-time migrations per iteration and stores two types of source wavefields at model boundaries. We develop a joint inline and crossline PWEMVA method for efficient 3-D velocity model building. We alternatively implement the inline and crossline PWEMVAs with a constraint for each other, in which we iteratively construct the 3-D velocity model update through 1-D spline interpolation of 2-D gradients. The inline and crossline joint inversion is practical since PWEMVA only inverts for low-wavenumber velocity perturbations along wave paths, and the method can take less than 1 per cent of the computational cost of full 3-D PWEMVA. To construct unaliased plane waves for our joint inline and crossline PWEMVA, we develop a 3-D data interpolation method in the frequency–wavenumber (FK) domain to recover regularly and randomly missing traces. The method minimizes the misfit on sufficiently localized data subsets with iterative optimal step lengths and a gradient preconditioner that iteratively selects dominant dips along different azimuths. In numerical experiments, we use a 3-D synthetic seismic data set and a land 3-D field seismic data set acquired at the Farnsworth CO2-EOR (enhanced oil recovery) field to demonstrate the efficacy of our velocity model building and data interpolation methods.

Liu, Xuejian↗
DOE OSTI2025

LATTE: open-source, high-performance traveltime computation, tomography and source location in acoustic and elastic media

Traveltime-based tomography and source location are fundamental approaches for imaging subsurface structures and understanding the spatiotemporal distribution of seismicity from local to global scales. We present an open-source, high-performance framework integrating eikonal equation solvers and adjoint-state theory for traveltime computation, velocity tomography, source location and joint tomography-location in 2-D/3-D acoustic and elastic media. We introduce novel regularization schemes based on total generalized p-variation, structural similarity and multitask machine learning to enhance the fidelity and interpretability of inverted models and source locations. Key features of our implementation also include the ability to leverage both absolute-difference and double-difference traveltime misfits for high-fidelity velocity tomography and source parameter estimation; support for traveltime computation and inversion in diverse 2-D/3-D scenarios with arbitrary source and receiver distributions; and a perturbation-based optimal step-size estimation method to reduce computational costs. In addition, our implementation employs shared-memory and distributed-memory parallelization to provide an efficient solution for traveltime computation, tomography, and source location. In conclusion, we validate the efficacy and accuracy of our approach through multiple synthetic data examples.

58 GEOSCIENCES↗
DOE OSTI2025

Differential Seismic Phase Detection Probability as a Potential Discriminant of Explosions and Earthquakes

Deep learning models trained to estimate the probability of seismic P and S phases are rapidly expanding the scale of local event detections. Here, we evaluate the potential for deep learning model output phase detection probabilities to contribute to event‐type classification, particularly discrimination of single‐fired borehole explosions and earthquakes at local distances (<300 km). Motivated by the empirical success of P/S amplitude ratios, we consider the difference between P and S pick probability output from previously developed phase detection models, P prob −S prob ⁠, as a discriminant. Test data include M L ∼1–4 earthquakes and explosions observed by common seismographs in ten geologically diverse localities. Depending on the picking model and training data, binary classification using P prob −S prob with at least three stations can achieve approximately equivalent classification accuracy as P/S amplitude ratios without requiring any customization. Joint classification with P/S and P prob −S prob improves accuracy for most quality control scenarios. Pick probabilities are an efficient attribute to consider in explosion discrimination because they can be automated byproducts of event detection. They avoid the binary choice of picking or not picking weakly visible S waves common to explosions.

Duan, Chenglong [Rice Univ., Houston, TX (United S↗
DOE OSTI2023

Utah FORGE: Optimization of a Plug-and-Perf Stimulation (Fervo Energy)

Information around the plug-and-perf treatment design at Utah FORGE by Fervo Energy. Objective and Purpose: - Develop a multistage hydraulic stimulation approach designed specifically to target the top three factors that control the technical and commercial viability of an EGS system: i) Achieving sufficient injectivity to support high cross-well flow rates ii) Distributing flow evenly across the wellbore and reservoir to maximize heat mining efficiency, ensure sustained heat transfer, and mitigate thermal breakthrough iii) Overcoming the effects of stress heterogeneity, stress shadowing, and variations in natural fracture properties during the stimulation treatment, leading to a more predictable stimulated reservoir volume and offset well placement - The following activities will be performed: i) Design, plan, and execute a multistage plug-and-perf stimulation treatment at a Fervo site with data acquisition and well testing activities aimed at addressing key technical aspects of the issues above ii) Perform data processing and interpretation of field results to translate the results form the Fervo site to a site-specific design at the Utah FORGE site iii) Design, plan, and execute a multistage plug-and-perf stimulation treatment design at the Utah FORGE site Methods and Approach: - Design a detailed data acquisition plan to maximize learning around: i) DFIT testing ii) Petrophysical logging, image logging iii) Permanent DAS/DTS fiber optic monitoring iv) Deep borehole microseismic monitoring v) Shallow borehole induced seismicity monitoring vi) Injection/production testing (RTA analysis, tracer testing) vii) Integrated numerical modeling and production forecasting

15 GEOTHERMAL ENERGY↗