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Dimensionality reduction for multi-criteria problems: An application to the decommissioning of oil and gas installations

Dimensionality reduction for multi-criteria problems: An application to the decommissioning of oil and gas installations
Dimensionality reduction for multi-criteria problems: An application to the decommissioning of oil and gas installations

This paper is motivated by decommissioning studies in the field of oil and gas, which comprise a very large number of installations and are of interest to a large number of stakeholders. Generally, the problem gives rise to complicated multi-criteria decision aid tools that rely upon the costly evaluation of multiple criteria for every piece of equipment. We propose the use of machine learning techniques to reduce the number of criteria by feature selection, thereby reducing the number of required evaluations and producing a simplified decision aid tool with no sacrifice in performance. In addition, we also propose the use of machine learning to explore the patterns of the multi-criteria decision aid tool in a training set. Hence, we predict the outcome of the analysis for the remaining pieces of equipment, effectively replacing the multi-criteria analysis by the computational intelligence acquired from running it in the training set. Computational experiments illustrate the effectiveness of the proposed approach.

Decommissioning, Dimensionality reduction, Feature selection, Machine learning, Multi-criteria decision analysis, Oil and gas
0957-4174
Martins, Isabelle D.
ed39ebd4-3657-449d-92fb-9a58494fedac
Bahiense, Laura
48a5c2a4-d43b-4445-983f-e0637753074e
Infante, Carlos E.D.
7eaaee47-c950-4f2b-8e61-b2543d481a34
Arruda, Edilson F.
8eb3bd83-e883-4bf3-bfbc-7887c5daa911
Martins, Isabelle D.
ed39ebd4-3657-449d-92fb-9a58494fedac
Bahiense, Laura
48a5c2a4-d43b-4445-983f-e0637753074e
Infante, Carlos E.D.
7eaaee47-c950-4f2b-8e61-b2543d481a34
Arruda, Edilson F.
8eb3bd83-e883-4bf3-bfbc-7887c5daa911

Martins, Isabelle D., Bahiense, Laura, Infante, Carlos E.D. and Arruda, Edilson F. (2020) Dimensionality reduction for multi-criteria problems: An application to the decommissioning of oil and gas installations. Expert Systems with Applications, 148, [113236]. (doi:10.1016/j.eswa.2020.113236).

Record type: Article

Abstract

This paper is motivated by decommissioning studies in the field of oil and gas, which comprise a very large number of installations and are of interest to a large number of stakeholders. Generally, the problem gives rise to complicated multi-criteria decision aid tools that rely upon the costly evaluation of multiple criteria for every piece of equipment. We propose the use of machine learning techniques to reduce the number of criteria by feature selection, thereby reducing the number of required evaluations and producing a simplified decision aid tool with no sacrifice in performance. In addition, we also propose the use of machine learning to explore the patterns of the multi-criteria decision aid tool in a training set. Hence, we predict the outcome of the analysis for the remaining pieces of equipment, effectively replacing the multi-criteria analysis by the computational intelligence acquired from running it in the training set. Computational experiments illustrate the effectiveness of the proposed approach.

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More information

Accepted/In Press date: 22 January 2020
e-pub ahead of print date: 23 January 2020
Published date: 15 June 2020
Keywords: Decommissioning, Dimensionality reduction, Feature selection, Machine learning, Multi-criteria decision analysis, Oil and gas

Identifiers

Local EPrints ID: 445481
URI: http://eprints.soton.ac.uk/id/eprint/445481
ISSN: 0957-4174
PURE UUID: 026c5430-e7bb-49e8-8c84-000042aec0aa
ORCID for Edilson F. Arruda: ORCID iD orcid.org/0000-0002-9835-352X

Catalogue record

Date deposited: 10 Dec 2020 17:32
Last modified: 28 Apr 2022 02:31

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Contributors

Author: Isabelle D. Martins
Author: Laura Bahiense
Author: Carlos E.D. Infante

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