Classification of drilling stick slip severity using machine learning-Reference-Cited by-同舟云学术

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Classification of drilling stick slip severity using machine learning

Published:2019-08 Issue: Volume:179 Page:1023-1036
ISSN:0920-4105
Container-title:Journal of Petroleum Science and Engineering
language:en
Short-container-title:Journal of Petroleum Science and Engineering

Author:

Hegde Chiranth,Millwater Harry,Gray Ken

Funder

University of Texas at Austin

BHP Billiton

British Petroleum

Chevron

ConocoPhillips

ExxonMobil

Halliburton

Marathon

National Oilwell Varco

Occidental Oil and Gas and Shell

Publisher

Elsevier BV

Subject

Geotechnical Engineering and Engineering Geology,Fuel Technology

Reference52 articles.

1. Solving stick-slip dilemma: dynamic modeling system significantly reduces vibration, increases ROP by 54%;Rahman,2012

2. Drillstring Vibrations and Vibration Modeling;Anon,2010

3. SPE 135248 drilling mechanics challenges and strategies to reach new exploratory deepwater frontiers in Brazil;Arevalo;Society,2010

4. SPE 147747 Quantification of Drillstring Integrity Failure Risk Using Real-Time Vibration Measurements;Arevalo,2011

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