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Utah FORGE 3-2418: Wellbore Fracture Imaging Using Inflow Detection 2024 Annual Workshop Presentation

This is a presentation on the Wellbore Fracture Imaging Using Inflow Detection by Stanford University and Sandia National Laboratory, presented by Roland Horde. This is a video presentation on wells, both before and after stimulation, using chloride or other ions to map fractures ...

Horne, R. and Schneider, M. Energy and Geoscience Institute at the University of Utah

Sep 13, 2024
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Utah FORGE 3-2418: Wellbore Fracture Imaging Using Inflow Detection Measurements Workshop Presentation

This is a presentation on the Wellbore Fracture Imaging Using Inflow Detection Measurements project by Stanford University, presented by Dr. Roland N. Horne. The project's objective was to make measurements in the Utah FORGE wells, both before and after fracturing experiments, to...

Horne, R. and Su, J. Stanford University

Sep 08, 2023
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EGS Collab Experiment 1: 3D Seismic Velocity Model and Updated Microseismic Catalog Using Transfer-Learning Aided Double-Difference Tomography

This package contains a 3D Seismic velocity model and an updated microseismic catalog associated with a proceedings paper (Chai et al., 2020) published in the 45th Workshop on Geothermal Reservoir Engineering. The 3D_seismic_velocity_model text file contains x (m), y(m), z(m), P-w...

Chai, C. et al Oak Ridge National Laboratory

Apr 20, 2020
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EGS Collab Experiment 1: 3D Seismic Velocity Model and Updated Microseismic Catalog from Double-Difference Seismic Tomography

This package contains a 3D Seismic velocity model and an updated microseismic catalog obtained for a double-difference seismic tomography study. The 3D_seismic_velocity_model text file contains x (m), y(m), z(m), P-wave velocity (km/s), P-wave velocity quality indicator (1 for we...

Chai, C. et al Oak Ridge National Laboratory

Jun 01, 2020
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Artificial Intelligence for Robust Integration of AMI and Synchrophasor Data to Significantly Boost Solar Adoption

The overarching goal of the project is to create a highly efficient framework of machine learning (ML) methods that provide consistent and accurate real-time knowledge of system states from diverse advanced metering infrastructure (AMI) devices and phasor measurement units (PMUs) ...

Ayyanar, R. et al Arizona State University

Feb 01, 2025
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