Accelerating Hydraulic Fracture Imaging by Deep Transfer Learning
Author:
Affiliation:
1. Department of Electrical and Computer Engineering, Duke University, Durham, NC, USA
2. Bureau of Economic Geology, The University of Texas at Austin, Austin, TX, USA
Funder
U.S. Department of Energy, National Energy Technology Laboratory
Advanced Energy Consortium with ExxonMobil, Repsol, Total, and Shell as members
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering
Link
https://ieeexplore.ieee.org/ielam/8/9843191/9743614-aam.pdf
Reference26 articles.
1. Hydraulic Fracture Scattering Simulation by Thin Dielectric Structure-Based Higher Order Surface Integral Equations
2. Validation of the Utility of the Contrast-Agent-Assisted Electromagnetic Tomography Method for Precise Imaging of a Hydraulically Induced Fracture Network
3. Imaging Hydraulic Fractures Under Energized Steel Casing by Convolutional Neural Networks
4. Deep learning prior models from seismic images for full-waveform inversion
5. Deep-learning tomography
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