Found 10 datasets matching "Machine Learning for in-situ stress".
-
This is a presentation on A Multi-Component Approach to Characterizing In-Situ Stress at the Utah FORGE Site: Laboratory Modelling and Field Measurements project by The University of Pittsburgh,...
Search relevance: 185.86 | Views last month: 0 -
This is a presentation on A Multi-Component Approach to Characterizing In-Situ Stress at the U.S DOE FORGE EGS Site: Laboratory, Modeling and Field Measurement project by University of Pittsburgh,...
Search relevance: 164.16 | Views last month: 3 -
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...
Search relevance: 160.74 | Views last month: 4 -
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...
Search relevance: 159.97 | Views last month: 2 -
This is a presentation on A Multi-Component Approach to Characterizing In-Situ Stress at the U.S DOE FORGE EGS Site: Laboratory, Modeling and Field Measurement project by Battelle [Columbus, OH],...
Search relevance: 158.48 | Views last month: 0 -
This comprehensive technical report documents a multi-component approach to in-situ stress characterization at the Utah FORGE EGS site that integrates Machine Learning (ML) methods for predicting...
Search relevance: 157.04 | Views last month: 0 -
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...
Search relevance: 69.54 | Views last month: 0 -
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...
Search relevance: 63.29 | Views last month: 0 -
The ECO1BMAPRAD Version 1 data product was decommissioned on February 14, 2025. Users are encouraged to use the [ECO_L1CT_RAD](https://doi.org/10.5067/ECOSTRESS/ECO_L1CT_RAD.002) Version 2 and...
Search relevance: 35.56 | Views last month: 3 -
The ECO1BRAD Version 1 data product was decommissioned on May 21, 2025. Users are encouraged to use the [ECO_L1CT_RAD](https://doi.org/10.5067/ECOSTRESS/ECO_L1CT_RAD.002) Version 2 and...
Search relevance: 31.74 | Views last month: 0
