Machine Learning Application for Fracture Analysis: Use Case
This is an invited talk for Alberta Energy Regulator, Canada, for machine learning applications for CCS, unconventional hydrocarbon, and frac-hit identification based on NETL recent activities (Virtual, October 1, 2024).
Authors
- Evgeniy M. Myshakin (ORCID: https://orcid.org/0000-0002-5806-1338)
- Dustin M. Crandall (ORCID: https://orcid.org/0000-0001-7328-9464)
- Hema Siriwardane (ORCID: https://orcid.org/0000-0002-5718-4563)
- Luciane Cunha (ORCID: https://orcid.org/0000-0002-1268-2403)
- William Harbert (ORCID: https://orcid.org/0000-0002-0205-5772)
- Guoxiang Liu (ORCID: https://orcid.org/0000-0002-2799-1145)
- Abhash Kumar (ORCID: https://orcid.org/0000-0002-7900-6098)
Institutions
- Oak Ridge Associated Universities (US)
- National Energy Technology Laboratory (US)
- Oak Ridge Institute for Science and Education
Publication Details
- Published
- 2024-10-01
- DOI
- https://doi.org/10.2172/2484041
- Citations
- 1
- Primary Topic
- Anomaly Detection Techniques and Applications
- Type
- article
- Field-Weighted Citation Impact
- 0.35
Funders
- U.S. Department of Energy
- National Energy Technology Laboratory