Automated operational states detection for drilling systems control in critical conditions
Automated operational states detection for drilling systems control in critical conditions
Critical events in industrial drilling should be overcome by engineers while they maintain safety and achieve their targeted operational drilling plans. Geophysical drilling requires maximum awareness of critical situations such as “Kicks”, “Fluid loss” and “Stuck pipe”. These may compromise safety and potentially halt operations with the need of staff rapid evacuations from rigs. In this paper, a robust method for the detection of operational states is proposed. Specifically, Echo State Networks (ESNs) were benchmarked and tested rigorously despite the challenging unbalanced datasets used for training. Nevertheless, these challenges were overcome and led to acceptable ESNs performances.
978-2-87419-081-0
Veres, Galina
3c2a37d2-3904-43ce-b0cf-006f62b87337
Sabeur, Zoheir
74b55ff0-94cc-4624-84d5-bb816a7c9be6
24 April 2013
Veres, Galina
3c2a37d2-3904-43ce-b0cf-006f62b87337
Sabeur, Zoheir
74b55ff0-94cc-4624-84d5-bb816a7c9be6
Veres, Galina and Sabeur, Zoheir
(2013)
Automated operational states detection for drilling systems control in critical conditions.
ESANN 2013, Bruges, Belgium.
24 - 26 Apr 2013.
6 pp
.
Record type:
Conference or Workshop Item
(Paper)
Abstract
Critical events in industrial drilling should be overcome by engineers while they maintain safety and achieve their targeted operational drilling plans. Geophysical drilling requires maximum awareness of critical situations such as “Kicks”, “Fluid loss” and “Stuck pipe”. These may compromise safety and potentially halt operations with the need of staff rapid evacuations from rigs. In this paper, a robust method for the detection of operational states is proposed. Specifically, Echo State Networks (ESNs) were benchmarked and tested rigorously despite the challenging unbalanced datasets used for training. Nevertheless, these challenges were overcome and led to acceptable ESNs performances.
More information
Published date: 24 April 2013
Venue - Dates:
ESANN 2013, Bruges, Belgium, 2013-04-24 - 2013-04-26
Organisations:
IT Innovation
Identifiers
Local EPrints ID: 354193
URI: http://eprints.soton.ac.uk/id/eprint/354193
ISBN: 978-2-87419-081-0
PURE UUID: d8ec1c5e-d4fc-4692-bed7-206fc3fd137b
Catalogue record
Date deposited: 29 Jul 2013 15:57
Last modified: 14 Mar 2024 14:15
Export record
Contributors
Author:
Galina Veres
Author:
Zoheir Sabeur
Download statistics
Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.
View more statistics