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Utah FORGE 5-2419: Seismicity-Permeability Relationships Probed via Nonlinear Acoustic Imaging Workshop Presentation

This is a presentation on the Seismicity-Permeability Relationships Probed via Nonlinear Acoustic Imaging project by Pennsylvania State University, presented by Derek Elsworth. The project's objectives were to explore controls and acoustic signatures of aseismic through seismic e...

Elsworth, D. et al Pennsylvania State University

Sep 08, 2023
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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
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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 fric...

Marone, C. Pennsylvania State University

May 05, 2021
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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...

Zhu, T. Pennsylvania State University

May 05, 2021
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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
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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
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