Data analytics for drilling operational states classifications
Data analytics for drilling operational states classifications
This paper provides benchmarks for the identification of best performance classifiers for the detection of operational states in industrial drilling operations. Multiple scenarios for the detection of the operational states are tested on a rig with various drilling wells. Drilling data are extremely challenging due to their non-linear and stochastic natures, notwithstanding the embedded noise in them and unbalancing. Nevertheless, there is a possibility to deploy robust classifiers to overcome such challenges and achieve good automated detection of states. Three classifiers with best classification rates of drilling operational states were identified in this study.
Veres, Galina
3c2a37d2-3904-43ce-b0cf-006f62b87337
Sabeur, Zoheir
74b55ff0-94cc-4624-84d5-bb816a7c9be6
22 April 2015
Veres, Galina
3c2a37d2-3904-43ce-b0cf-006f62b87337
Sabeur, Zoheir
74b55ff0-94cc-4624-84d5-bb816a7c9be6
Veres, Galina and Sabeur, Zoheir
(2015)
Data analytics for drilling operational states classifications.
European Symposium on Artificial Networks, Computational Intelligence and Machine Learning (ESANN2015), Bruges, Belgium.
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Conference or Workshop Item
(Paper)
Abstract
This paper provides benchmarks for the identification of best performance classifiers for the detection of operational states in industrial drilling operations. Multiple scenarios for the detection of the operational states are tested on a rig with various drilling wells. Drilling data are extremely challenging due to their non-linear and stochastic natures, notwithstanding the embedded noise in them and unbalancing. Nevertheless, there is a possibility to deploy robust classifiers to overcome such challenges and achieve good automated detection of states. Three classifiers with best classification rates of drilling operational states were identified in this study.
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Published date: 22 April 2015
Venue - Dates:
European Symposium on Artificial Networks, Computational Intelligence and Machine Learning (ESANN2015), Bruges, Belgium, 2015-04-22
Organisations:
IT Innovation
Identifiers
Local EPrints ID: 376585
URI: http://eprints.soton.ac.uk/id/eprint/376585
PURE UUID: cb7f0ea8-3ee2-4356-b14a-63e696bbad1f
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Date deposited: 30 Apr 2015 17:12
Last modified: 14 Mar 2024 19:46
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Contributors
Author:
Galina Veres
Author:
Zoheir Sabeur
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