Kick Detection at the Bit: Early Detection using Borehole Geophysics
Abstract
Presented by NETL at Machine Learning for Unconventional Resources, 11/19
- Authors:
-
- ORISE
- NETL
- LRST
- Draper and Associates
- USC
- Publication Date:
- Research Org.:
- National Energy Technology Laboratory (NETL), Pittsburgh, PA, Morgantown, WV (United States)
- Sponsoring Org.:
- FE
- OSTI Identifier:
- 1599050
- Report Number(s):
- NETL-PUB-22677
- Resource Type:
- Conference
- Resource Relation:
- Conference: Machine Learning for Unconventional Resouces Houston, TX 11/18/2019 - 11/19/2019
- Country of Publication:
- United States
- Language:
- English
- Subject:
- Kick, Machine Learning, Logging-while-drilling, Measurements
Citation Formats
Suhaj, Anuj, Rose, Kelly K, Carney, Janine E, Tost, Brian C, Zimmer, Mike, and Aminzadeh, Fred. Kick Detection at the Bit: Early Detection using Borehole Geophysics. United States: N. p., 2019.
Web.
Suhaj, Anuj, Rose, Kelly K, Carney, Janine E, Tost, Brian C, Zimmer, Mike, & Aminzadeh, Fred. Kick Detection at the Bit: Early Detection using Borehole Geophysics. United States.
Suhaj, Anuj, Rose, Kelly K, Carney, Janine E, Tost, Brian C, Zimmer, Mike, and Aminzadeh, Fred. 2019.
"Kick Detection at the Bit: Early Detection using Borehole Geophysics". United States. https://www.osti.gov/servlets/purl/1599050.
@article{osti_1599050,
title = {Kick Detection at the Bit: Early Detection using Borehole Geophysics},
author = {Suhaj, Anuj and Rose, Kelly K and Carney, Janine E and Tost, Brian C and Zimmer, Mike and Aminzadeh, Fred},
abstractNote = {Presented by NETL at Machine Learning for Unconventional Resources, 11/19},
doi = {},
url = {https://www.osti.gov/biblio/1599050},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Fri Oct 18 00:00:00 EDT 2019},
month = {Fri Oct 18 00:00:00 EDT 2019}
}
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