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Processed Lab Data for Neural Network-Based Shear Stress Level Prediction

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...
Marone, C. et al Pennsylvania State University
May 14, 2021
3 Resources
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Penn State Lab Testing Fluid-Rock Interaction in Geothermal Reservoirs

This project focused on assessment and discovery of fluid-rock interaction in geothermal reservoirs. We accomplished work in four main areas: 1) fracture formation and the relationship between fluid flow and shear failure, 2) assessment of fracture geometry and fluid permeability ...
Madara, B. et al Pennsylvania State University
Jan 01, 2018
5 Resources
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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 therm...
Elsworth, D. and Marone, C. Pennsylvania State University
Nov 22, 2022
4 Resources
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