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Numerical modeling on fused silica cylindrical materials for predicting overpressures required to fracture an homogeneous pure (surrogate) material with known mechanical properties similar to igneous rock materials and later compare these values to experimental overpressures obtai...
Understanding the relationship between stress state, strain state and fracture initiation and propagation is critical to the improvement of fracture simulation capability if it is to be used as a tool for guiding hydraulic fracturing operations. The development of fracture predict...
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 simulat...
This is a presentation on Real-Time Robust Adaptive Traffic Light System and Reservoir Engineering with Machine-Learning-Based Seismicity Forecasting and Data-Driven Ground Motion Prediction (RT Forecast) by Lawrence Berkeley National Laboratory, presented by Nori Nakata. This vid...
These two reports from the University of Pittsburgh document related efforts under Utah FORGE Project 2-2439v2 to estimate in-situ stresses in well 16B(78)-32 using laboratory data, machine learning models, and physics-based simulations. One report focuses on developing and valida...
This submission contains datasets and a final manuscript associated with a project simulating carbon dioxide push-pull into a conjugate fault system modeled after Dixie Valley-
sensitivity analysis of significant parameters and uncertainty prediction by data-worth analysis.
Datas...
This dataset contains acoustic-emission (AE), ultrasonic-transmission, permeability, and hydromechanical measurements from triaxial laboratory experiments comparing differential stress-driven and pore-pressure-driven failure in saturated, thermally cracked Barre granite at 80 degr...
This report presents the far-field stress predictions at two locations along the vertical section of Utah FORGE Well 16A (78)-32 using a physics-based thermo-poro-mechanical model. Three principal stresses in far-field were obtained by solving an inverse problem based on the near-...
Machine learning can be used to predict fault properties such as shear stress, friction, and time to failure using continuous records of fault zone acoustic emissions. The files are extracted features and labels from lab data (experiment p4679). The features are extracted with a n...
This report reviews the training of machine learning algorithms to laboratory triaxial ultrasonic velocity data for Utah FORGE Well 16A(78)-32. Three machine learning (ML) predictive models were developed for the prediction of vertical and two orthogonally oriented horizontal str...
This project assessed the technical viability of a process called GeoCAES. The process stores electrical energy by injecting natural gas into shale gas formations using a compressor, storing it, and producing it through an expander to generate electricity. This data submission inc...
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. Th...
This task completion report documents the development and implementation of machine learning (ML) models for the prediction of in-situ vertical (Sv), minimum horizontal (SHmin) and maximum horizontal (SHmax) stresses in well 16A(78)-32. The detailed description of the experimental...
A review of brine chemistry data for the Mt. Simon Sandstone in the Illinois Basin is provided for calculations to predict the potential for mineral scaling and precipitation. The assessment includes expected changes in temperature, pressure, and/or exposure to air or other materi...
The maps in this submission include: heat flow, alkalinity, Cl, Mg, SiO2, Quaternary volcanic rocks, faults, and land ownership. All of the Oregon Cascade region. The work was done by John Trimble, in 2015, at Oregon State University.
Geothermal exploration and production are challenging, expensive and risky. The GeoThermalCloud uses Machine Learning to predict the location of hidden geothermal resources. This submission includes a training dataset for the GeoThermalCloud neural network. Machine Learning for Di...
*This submission revises the analysis and products for Thermal Quality Analysis for the northern half of the Appalachian Basin (https://gdr.openei.org/submissions/638)*
This submission is one of five major parts of a Low Temperature Geothermal Play Fairway Analysis. Phase 1 of the...
This is a presentation on the Probabilistic Estimation of Seismic Response Using Physics-Informed Recurrent Neural Networks by GTC Analytics, presented by Dr. Jesse Williams. This video slide presentation discusses the development of machine learning-based predictive tools to esti...
Provided here are various forms of the Stanford Thermal Earth Model, as well as the data and methods used for its creation. The predictions produced by this model were visualized in two-dimensional spatial maps across the modeled depths (0-7 km) for the conterminous United States....
This submission houses the companion code for the manuscript by Zhengfa Bi (Lawrence Berkeley National Laboratory) and Nori Nakata (Lawrence Berkeley National Laboratory; MIT). Both the companion code and manuscript are included in the resources section of this submission.
Fluid ...
This dataset includes reports and a slide presentation on discrete fracture network (DFN) generation and hydraulic fracture modeling at the Utah FORGE site. It details the characterization of natural fractures using well log and core data, as well as stochastic modeling techniques...
The Utah FORGE 2025 v1 DFN (fracture model) includes 131 discrete planar fractures which were identified using combined site data sets to capture flow pathways between wells 16A(78)-32 and 16B(78)-32 following stimulation activities in 2022 and 2024. It also includes stochastic fr...
Images of the Stripa Granite core before and after the fracture sustainability test. Photos of fracture faces of Stripa Granite core.
Natural fracture data from wells 33-7, 33A-7,52A-7, 52B-7 and 83-11 at West Flank. Fracture orientations were determined from image logs of these wells (see accompanying submissions). Data files contain depth, apparent (in wellbore reference frame) and true (in geographic referenc...
This submission includes a methods report and an accompanying Excel dataset documenting temperature-dependent Mode-I fracture toughness (KIC) measurements for Utah FORGE granite obtained using semi-circular bending (SCB) tests in accordance with the ISRM Suggested Method. Testing ...
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