AndreaBozzo/ceres-open-data-index huggingface dataset:
Hybrid machine learning model to predict 3D in-situ permeability evolution

Enhanced geothermal systems (EGS) can provide a sustainable and renewable solution to the new energy transition. Its potential relies on the ability to create a reservoir and to accurately evaluate its evolving hydraulic properties to predict fluid flow and estimate ultimate thermal recovery. Here we develop a hybrid machine learning (ML) model to predict permeability evolution of intermediate-scale (~10 m) hydraulic stimulation experiments at the Sanford Underground Research Facility (the EGS Collab project).  We present a 3D map in situ permeability evolution for two of stimulation episodes in this project using microearthquakes (MEQs) data and injection histories of wellhead pressure and flow rate. This map includes both average reservoir permeability evolution over time and local fracture permeability distribution within the evolved reservoir. Compared with the ground truth of average permeability calculated from the well data, our predicted average permeability for these two episodes has a MSE value less than 2.9E-4 and R2 higher than 0.93, indicating that average permeability predicted by machine learning is consistent with good agreement with field observation.  Additionally, distributed fracture permeability calculated by empirical equation over time shows the process of fracture propagation and identify the potential fluid path for geothermal reservoir.

Tags: egs, egs-collab, energy, enhanced-geothermal-systems, flow-rate, fracture-permeability, geothermal, hydraulic, hydraulic-fracturing, induced-seismicity, machine-learning, microearthquake, newberry, permeability-evolution, processed-data, seismic-data-analysis, stimulation, wellhead-pressure
Organization: Department of 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.

Tags: ai, artificial-intelligence, code, deep-learning, egs, energy, engineered-geothermal-systems, enhanced-geothermal-systems, geophysical, geophysics, geothermal, machine-learning, meq, microearthquake, microseismicity, ml, newberry, newberry-volcanic-site, newberry-volcano, numpy, oregon, preprocessed, processed-data, python, raw-data, seismic, waveform
Organization: Department of Energy
Active Source Seismic (Ultrasonic) Data from Double-Direct Shear Lab Experiments

Active source ultrasonic data from lab experiments p5270 and p5271 including raw waveforms (WF) and mechanical data (mat). From the PSU team working on the "Machine Learning Approaches to Predicting Induced Seismicity and Imaging Geothermal Reservoir Properties" project.  The friction experiments reported here were performed in a double-direct shear (DDS) configuration in a biaxial testing apparatus in the Penn State Rock Mechanics laboratory. In addition to mechanical data acquisition, all experiments were instrumented with an ultrasonic acoustic monitoring system. Both mechanical data and ultrasonic data from experiments p5270 and p5721 are available in this submission.

Tags: acoustic, acoustics, active-source, ai, artificial-intelligence, biaxial-testing-apparatus, deep-learning, earthquake-forecasting, energy, fault, friction-experiment, geophysics, geothermal, lab-data, laboratory, machine-learning, matlab, mechanical-data, microseismicity, preprocessed, raw-data, seismic, seismic-forecasting, ultrasonic, ultrasonic-acoustic-monitoring-system, waveforms
Organization: Department of Energy
Sentinel-1 Input Data for PSInSAR Analysis

Files used to perform the Persistent Scatterer InSAR analysis with SARPROZ. The data is sourced from ESAs Sentinel-1 project and covers Brady Hot Springs and Desert Peak geothermal areas. The original titles are included for the Sentinel-1 data. The naming guide is included as a link in this submission. The data contains SAR (Radar) data from the Sentinel 1A satellite between July 2017 and December 2019. The data is necessary to replicate our results, and can be used for further PSInSAR, DInSAR and other modern interferometric analyses to determine line of sight, vertical and, possibly, East-West horizontal displacement. These displacement analysis show the soil deformation in time (including average displacement velocity), which can be used to indicate subsoil phenomena: temperature changes, subsidence, uplift, and inform other analyses like seismicity and porosity. 

Tags: aerial-photography, ai, analysis, anomaly-detection, artificial-intelligence, deep-learning, displacement, energy, geology, geospatial-data, geothermal, imaging, input-data, insar, interferometric, machine-learning, phenomena, processed-data, psinsar, radar, remote-sensing, satellite-imaging, sentinel-1, site-detection, soil-deformation, subsoil, synthetic-aperture-radar
Organization: Department of Energy
Programs and Code for Geothermal Exploration Artificial Intelligence

The scripts below are used to run the Geothermal Exploration Artificial Intelligence developed within the "Detection of Potential Geothermal Exploration Sites from Hyperspectral Images via Deep Learning" project. It includes all scripts for pre-processing and processing, including:
- Land Surface Temperature K-Means classifier
- Labeling AI using Self Organizing Maps (SOM)
- Post-processing for Permanent Scatterer InSAR (PSInSAR) analysis with SOM
- Mineral marker summarizing
- Artificial Intelligence (AI) Data splitting: creates data set from a single raster file
- Artificial Intelligence Model: creates AI from a single data set, after splitting in Train, Validation and Test subsets
- AI Mapper: creates a classification map based on a raster file
 

Tags: ai, anomaly-detection, artificial-intelligence, blind, code, deep-learning, energy, exploration, geothermal, geothermal-ai, geothermal-exploration, k-mean, k-means, land-surface-temperature, landsat-adr-lst, lst, machine-learning, numpy, python, r_, raster, remote-sensing, sbatch, self-organizing-map, shell, shell-scripts, site-detection, slurm, tensorflow
Organization: Department of Energy
Salton Sea Geodatabase for Geothermal Exploration Artificial Intelligence

These files contain the geodatabases related to Salton Sea Geothermal Field. It includes all input and output files used with the Geothermal Exploration Artificial Intelligence. Input and output files are sorted into three categories: raw data, pre-processed data, and analysis (post-processed data). In each of these categories there are six additional types of raster catalogs which are titled Radar, SWIR, Thermal, Geophysics, Geology, and Wells. The files are used with the Geothermal Exploration Artificial Intelligence for the Salton Sea Geothermal Site to identify indicators of blind geothermal systems. The included zip file is a geodatabase to be used with ArcGIS and the tar file is an inclusive database that encompasses the inputs and outputs for the Salton Sea Geothermal Site.

Tags: ai, anomaly-detection, arcgis, artificial-intelligence, blind, blind-system, california, conceptual-model-fault, database, deep-learning, deformation, energy, exploration, field-data, geodatabase, geophysical, geophysics, geospacial-database, geospatial-data, geothermal, geothermal-site-detection, gis, hydrothermal, hyperspectral, hyperspectral-imaging, land-surface-temperature, lst, machine-learning, model, preprocessed, processed-data, radar, raster, raw-data, remote-sensing, salton-sea, seismic, short-wavelength-infrared, site-detection, support-vector-machine, svm, swir, vector, well
Organization: Department of Energy
Utah FORGE: Seismic Stations and Wells GPS Survey Data, 2021

This is a CSV spreadsheet containing UTM and Latitude and Longitude coordinates and elevations for Wells 78-32, 58-32, and 16A(78)-32 and BOR1, BOR2, BOR3, FOR2, FOR5, FORK, FORU, and FORW seismic stations. These are from a GPS survey conducted by the Utah Geological Survey in June, 2021. 

Tags: coordinates, elevations, energy, geophysical, geophysics, geothermal, gps, seismic, seismicity, station, utah, utah-forge, utah-forge-gps-survey, utah-forge-seismic-station-coordinates, utah-forge-seismic-stations-elevations, utah-forge-wells-coordinates, utah-forge-wells-elevations, utah-geological-survey, utm, well
Organization: Department of Energy
Brady Geodatabase for Geothermal Exploration Artificial Intelligence

These files contain the geodatabases related to Brady's Geothermal Field. It includes all input and output files for the Geothermal Exploration Artificial Intelligence. Input and output files are sorted into three categories: raw data, pre-processed data, and analysis (post-processed data). In each of these categories there are six additional types of raster catalogs which are titled Radar, SWIR, Thermal, Geophysics, Geology, and Wells. These inputs and outputs were used with the Geothermal Exploration Artificial Intelligence to identify indicators of blind geothermal systems at the Brady Hot Springs Geothermal Site. The included zip file is a geodatabase to be used with ArcGIS and the tar file is an inclusive database that encompasses the inputs and outputs for the Brady Hot Springs Geothermal Site.

Tags: ai, anomaly-detection, arcgis, artificial-intelligence, blind, blind-system, brady, brady-hot-springs, brady-well, conceptual-model, database, deep-learning, deformation, energy, exploration, fault, field-data, geodatabase, geophysical, geophysics, geospatial-data, geospatial-database, geothermal, geothermal-site-detection, gis, hydrothermal, hyperspectral, hyperspectral-imaging, land-surface-temperature, lst, machine-learning, model, nevada, preprocessed, processed-data, radar, raster, raw-data, remote-sensing, seismic, short-wavelength-infrared, site-detection, support-vector-machine, svm, swir, vector, well
Organization: Department of Energy
Appendices for Geothermal Exploration Artificial Intelligence Report

The Geothermal Exploration Artificial Intelligence looks to use machine learning to spot geothermal identifiers from land maps. This is done to remotely detect geothermal sites for the purpose of energy uses. Such uses include enhanced geothermal system (EGS) applications, especially regarding finding locations for viable EGS sites. This submission includes the appendices and reports formerly attached to the Geothermal Exploration Artificial Intelligence Quarterly and Final Reports.

The appendices below include methodologies, results, and some data regarding what was used to train the Geothermal Exploration AI. The methodology reports explain how specific anomaly detection modes were selected for use with the Geo Exploration AI. This also includes how the detection mode is useful for finding geothermal sites. Some methodology reports also include small amounts of code. Results from these reports explain the accuracy of methods used for the selected sites (Brady Desert Peak and Salton Sea). Data from these detection modes can be found in some of the reports, such as the Mineral Markers Maps, but most of the raw data is included the DOE Database which includes Brady, Desert Peak, and Salton Sea Geothermal Sites.

Tags: ai, anomaly-detection, arcgis, artificial-intelligence, blind, blind-system, border, brady, california, code, conceptual-model, database, deep-learning, deformation, desert-peak, egs, energy, engineered-geothermal-system, enhanced-geothermal-system, exploration, fault, geodatabase, geophysical, geophysics, geospatial-data, geothermal, gis, hydrothermal, hydrothermally-altered-minerals, hyperspectral, hyperspectral-imaging, insar, land-surface-temperature, machine-learning, mineral-markers, model, morphological, morphological-features, morphology, nevada, preproccessed, processed-data, python, radar, raw-data, remote-sensing, salton-sea, seismic, short-wavelength-infrared, site-detection, support-vector-machine, svm, swir, thermal-infrared, tir, visible-near-infrared, vnir, well, zotero
Organization: Department of Energy
Subsurface Characterization and Machine Learning Predictions at Brady Hot Springs

Subsurface data analysis, reservoir modeling, and machine learning (ML) techniques have been applied to the Brady Hot Springs (BHS) geothermal field in Nevada, USA to further characterize the subsurface and assist with optimizing reservoir management. Hundreds of reservoir simulations have been conducted in TETRAD-G and CMG STARS to explore different injection and production fluid flow rates and allocations and to develop a training data set for ML. This process included simulating the historical injection and production since 1979 and prediction of future performance through 2040. ML networks were created and trained using TensorFlow based on multilayer perceptron, long short-term memory, and convolutional neural network architectures. These networks took as input selected flow rates, injection temperatures, and historical field operation data and produced estimates of future production temperatures. This approach was first successfully tested on a simplified single-fracture doublet system, followed by the application to the BHS reservoir. Using an initial BHS data set with 37 simulated scenarios, the trained and validated network predicted the production temperature for six production wells with the mean absolute percentage error of less than 8%. In a complementary analysis effort, the principal component analysis applied to 13 BHS geological parameters revealed that vertical fracture permeability shows the strongest correlation with fault density and fault intersection density. A new BHS reservoir model was developed considering the fault intersection density as proxy for permeability. This new reservoir model helps to explore underexploited zones in the reservoir. A data gathering plan to obtain additional subsurface data was developed; it includes temperature surveying for three idle injection wells at which the reservoir simulations indicate high bottom-hole temperatures. The collected data assist with calibrating the reservoir model. Data gathering activities are planned for the first quarter of 2021.

This GDR submission includes a preprint of the paper titled "Subsurface Characterization and Machine Learning Predictions at Brady Hot Springs" presented at the 46th Stanford Geothermal Workshop (SGW) on Geothermal Reservoir Engineering from February 16-18, 2021. 

Tags: brady-hot-springs, bradys-hot-springs, characterization, doublet, dual-porosity, energy, flow, geothermal, heat-map, heatmap, injection-test, machine-learning, model, pca, porotomo, prediction, pressure, principal-component-analysis, reservoir, reservoir-management, reservoir-modeling, simulation, single-fracture, stimulation, subsurface, temperature, tensorflow, time-series
Organization: Department of Energy
Utah FORGE: Well 58-32 (MU-ESW1) FMI Log Fracture Results

A Schlumberger Fullbore Formation Micro-Imager (FMI) log was run from 7390 feet to 7527 feet depth in well 58-32, originally known as well MU-ESW1. Well 58-32 was completed in 2017 as part of Utah FORGE Phase 2. It reached a depth of 7536 feet and recorded a bottom hole temperature of 386 degrees F (197 degrees C) after penetrating more than 4500 feet of granite. This submission includes a text file containing the final fracture results of the Schlumberger FMI log.

Tags: egs, energy, fmi, fmi-log, formation-microimager, fracture, fullbore, geothermal, logging, logs, mu-esw1, schlumberger, schlumberger-fmi, utah-forge, utah-geothermal, well-58-32, well-58-32-log, well-data, well-log
Organization: Department of Energy
GEOPHIRES files for DDU techno-economic simulations

During 2017-2019, the U.S. Department of Energy funded six geothermal deep direct-use (DDU) projects to investigate feasibility of DDU for heating, cooling and thermal storage in the United States. In a follow-on study conducted at the National Renewable Energy Laboratory (NREL), findings of these six projects were reviewed and analyzed, and additional simulations were conducted using the simulator GEOPHIRES to explore technical performance and cost-competitiveness of DDU. The results of the NREL study were published in the paper "Evaluating the Feasibility of Geothermal Deep Direct-Use in the United States." The GEOPHIRES files developed in that study are included in this submission. The reference for the paper under review is below:

Beckers KF, Kolker A, Pauling H, McTigue JD, and Kesseli D (2021) ?Evaluating the Feasibility of Geothermal Deep Direct-Use in the United States?, Submitted to Energy Conversion and Management, Under Review.

Tags: cooling, cornell, cost, ddu, deep-direct-use, default, dgeo-ti, economics, energy, energy-storage, feasibility, financing, geophires, geothermal, heating, model, nrel, python, sandia, simulation, techno-economic
Organization: Department of Energy
GeoThermalCloud framework for fusion of big data and multi-physics models in Nevada and Southwest New Mexico

Our GeoThermalCloud framework is designed to process geothermal datasets using a novel toolbox for unsupervised and physics-informed machine learning called SmartTensors. More information about GeoThermalCloud can be found at the GeoThermalCloud GitHub Repository. More information about SmartTensors can be found at the SmartTensors Github Repository and the SmartTensors page at LANL.gov. Links to these pages are included in this submission.

GeoThermalCloud.jl is a repository containing all the data and codes required to demonstrate applications of machine learning methods for geothermal exploration.

GeoThermalCloud.jl includes:
- site data
- simulation scripts
- jupyter notebooks
- intermediate results
- code outputs
- summary figures
- readme markdown files

GeoThermalCloud.jl showcases the machine learning analyses performed for the following geothermal sites:
- Brady: geothermal exploration of the Brady geothermal site, Nevada
- SWNM: geothermal exploration of the Southwest New Mexico (SWNM) region
- GreatBasin: geothermal exploration of the Great Basin region, Nevada

Reports, research papers, and presentations summarizing these machine learning analyses are also available and will be posted soon.

Tags: brady, brady-hot-springs, energy, geothermal, geothermal-cloud, geothermalcloud, great-basin, los-alamos-national-laboratory, machine-learning, model, multi-physics, nevada, new-mexico, simulation, site-data, smarttensors, southwest-new-mexico
Organization: Department of Energy
Utah FORGE: Well 16A(78)-32: Summary of Drilling Activities

This is a 74 page detailed summary of the drilling activities of Utah FORGE 16A(78)-32, a highly deviated well completed on February 2nd, 2021, as part of the Utah FORGE project. This well was deviated at 65 degrees from vertical after reaching 6000 feet in depth had a total measured depth of 10,987 feet.

The report summarizes the operations, lessons learned, and recommendations for each drilled section of the well.

Tags: 16a78-32, characterization, deviated-well-16a78-32, deviated-wells, drill-stem-test, drilling, egs, energy, forge, geothermal, milford, roosevelt-hot-springs, utah-forge, utah-geothermal, well-16a78-32, well-data, well-log
Organization: Department of Energy
Utah FORGE: Well 56-32 Drilling Data and Logs

This dataset consists of drilling data (Pason data spreadsheets, daily reports, days v. depth, mud logs), Schlumberger logs (FMI, shear anisotropy analysis, memory, sonic, array induction/spectral density/dual spaced neutron/gamma ray/caliper, spectral GR/temperature, and Gardner density correlation), and an end of well report (EOWR) for Utah FORGE well 56-32. This is a vertical well that will be used for seismic monitoring. It was drilled between February 7th and February 21st 2021 to a depth of 9,145 feet. More information about this well can be found at: https://utahforge.com/2021/02/09/drilling-progress-of-well-56-32/ (linked below)

Tags: array-induction, caliper, daily-drilling-report, day-vs-depth, density, drill-bit-bha, drilling, dual-spaced, egs, end-of-well-report, energy, eowr, fmi, forge, gamma, gardner-density, geothermal, gr, logging, logs, memory, mud-log, neutron, pason, schlumberger, shear-anisotropy, sonic, spectral, spectral-density, temperature, utah-forge, utah-forge-well-56-32, well-56-32, well-56-32-daily-drilling-reports, well-56-32-data, well-56-32-logs, well-56-32-pason-data, well-56-32-schlumberger-logs, well-data, well-log
Organization: Department of Energy
Utah FORGE: Seismic Velocity Models, February 2021

This dataset contains a map, showing the Utah FORGE seismic stations, and seismic velocity model data. There are 61 1-D velocity models which are in a compressed TAR file. A paper is referenced at the end of this description which discusses the use of these data in 3D modelling. The paper summary follows:

We expand the application of spatial autocorrelation (SPAC) from typical 1-D Vs profiles to quasi-3-D imaging via Bayesian Monte Carlo inversion (BMCI) using a dense nodal array (49 nodes) located at the Utah Frontier Observatory for Research in Geothermal Energy (FORGE) site. Combinations of 4 and 9 geophones in subarrays provide for 36 and 25 1-D Vs profiles, respectively. Profiles with error bars are determined by calculating coherency functions that fit observations in a frequency range of 0.2-5 Hz. Thus, a high-resolution quasi-3-D Vs model from the surface to 2.0 km depth is derived and shows that surface-parallel sedimentary strata deepen to the west, consistent with a 3-D seismic reflection survey. Moreover, the resulting Vs profile is consistent with a Vs profile derived from distributed acoustic sensing (DAS) data located in a borehole at the FORGE site. The quasi-3-D velocity model shows that the base of the basin dips ~22 degrees to the west and topography on the basement interface coincident with the Mag Lee Wash suggests that the bedrock interface is an unconformity.

Reference: Zhang, H. and K. L. Pankow (2021). High-resolution Bayesian spatial auto-correlation (SPAC) pseudo-3D Vs model of Utah FORGE site with a dense geophone array, Geophys. Res. Int, 
https://doi.org/10.1093/gji/ggab049

Tags: 1d-seismic-velocity, bayesian-monte-carlo-inversion, distributed-acoustic-sensing, egs, energy, forge, geophone, geophysics, geospatial-data, geothermal, milford, model, modeling, pseudo-3d, roosevelt-hot-springs, sedimentary-basin, seismic, seismic-data, seismic-noise, seismic-velocity, spac, spatial-auto-correlation, tar, utah, utah-forge, utah-geothermal, velocity, velocity-models, waveform-inversion
Organization: Department of Energy