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Title: Kick Detection at the Bit: Early Detection using Borehole Geophysics

Abstract

Presented by NETL at Machine Learning for Unconventional Resources, 11/19

Authors:
 [1];  [2];  [2];  [3];  [4];  [5]
  1. ORISE
  2. NETL
  3. LRST
  4. Draper and Associates
  5. 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}
}

Conference:
Other availability
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