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Particle swarm algorithm with adaptive constraint handling and integrated surrogate model for the management of petroleum fields

Mauro Sebastián Innocente, Silvana Maria Bastos Afonso, Johann Sienz Orcid Logo, Helen Davies Orcid Logo

Applied Soft Computing, Volume: 34, Pages: 463 - 484

Swansea University Authors: Johann Sienz Orcid Logo, Helen Davies Orcid Logo

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DOI (Published version): 10.1016/j.asoc.2015.05.032

Abstract

This paper deals with the development of effective techniques to automatically obtain the optimum management of petroleum fields aiming to increase the oil production during a given concession period of exploration. The optimization formulations of such a problem turn out to be highly multimodal, an...

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Published in: Applied Soft Computing
ISSN: 1568-4946
Published: 2015
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URI: https://cronfa.swan.ac.uk/Record/cronfa22073
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first_indexed 2015-06-16T02:06:51Z
last_indexed 2018-02-09T05:00:10Z
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spelling 2017-07-03T12:55:57.6945781 v2 22073 2015-06-15 Particle swarm algorithm with adaptive constraint handling and integrated surrogate model for the management of petroleum fields 17bf1dd287bff2cb01b53d98ceb28a31 0000-0003-3136-5718 Johann Sienz Johann Sienz true false a5277aa17f0f10a481da9e9751ccaeef 0000-0003-4838-9572 Helen Davies Helen Davies true false 2015-06-15 FGSEN This paper deals with the development of effective techniques to automatically obtain the optimum management of petroleum fields aiming to increase the oil production during a given concession period of exploration. The optimization formulations of such a problem turn out to be highly multimodal, and may involve constraints. In this paper, we develop a robust particle swarm algorithm coupled with a novel adaptive constraint-handling technique to search for the global optimum of these formulations. However, this is a population-based method, which therefore requires a high number of evaluations of an objective function. Since the performance evaluation of a given management scheme requires a computationally expensive high-fidelity simulation, it is not practicable to use it directly to guide the search. In order to overcome this drawback, a Kriging surrogate model is used, which is trained offline via evaluations of a High-Fidelity simulator on a number of sample points. The optimizer then seeks the optimum of the surrogate model. Journal Article Applied Soft Computing 34 463 484 1568-4946 Adaptive constraint handling; Global search; Particle swarm; Reservoir simulation; Surrogate-based optimization; Waterflooding management 30 9 2015 2015-09-30 10.1016/j.asoc.2015.05.032 COLLEGE NANME Science and Engineering - Faculty COLLEGE CODE FGSEN Swansea University 2017-07-03T12:55:57.6945781 2015-06-15T13:04:01.3025522 Faculty of Science and Engineering School of Engineering and Applied Sciences - Uncategorised Mauro Sebastián Innocente 1 Silvana Maria Bastos Afonso 2 Johann Sienz 0000-0003-3136-5718 3 Helen Davies 0000-0003-4838-9572 4 0022073-15062015192845.pdf PSA__with__Adaptive__CH__&__Surrogate__Model__for__Management__Petroleum__Fields.pdf 2015-06-15T19:28:45.4030000 Output 2136763 application/pdf Accepted Manuscript true 2015-06-15T00:00:00.0000000 false
title Particle swarm algorithm with adaptive constraint handling and integrated surrogate model for the management of petroleum fields
spellingShingle Particle swarm algorithm with adaptive constraint handling and integrated surrogate model for the management of petroleum fields
Johann Sienz
Helen Davies
title_short Particle swarm algorithm with adaptive constraint handling and integrated surrogate model for the management of petroleum fields
title_full Particle swarm algorithm with adaptive constraint handling and integrated surrogate model for the management of petroleum fields
title_fullStr Particle swarm algorithm with adaptive constraint handling and integrated surrogate model for the management of petroleum fields
title_full_unstemmed Particle swarm algorithm with adaptive constraint handling and integrated surrogate model for the management of petroleum fields
title_sort Particle swarm algorithm with adaptive constraint handling and integrated surrogate model for the management of petroleum fields
author_id_str_mv 17bf1dd287bff2cb01b53d98ceb28a31
a5277aa17f0f10a481da9e9751ccaeef
author_id_fullname_str_mv 17bf1dd287bff2cb01b53d98ceb28a31_***_Johann Sienz
a5277aa17f0f10a481da9e9751ccaeef_***_Helen Davies
author Johann Sienz
Helen Davies
author2 Mauro Sebastián Innocente
Silvana Maria Bastos Afonso
Johann Sienz
Helen Davies
format Journal article
container_title Applied Soft Computing
container_volume 34
container_start_page 463
publishDate 2015
institution Swansea University
issn 1568-4946
doi_str_mv 10.1016/j.asoc.2015.05.032
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Engineering and Applied Sciences - Uncategorised{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Engineering and Applied Sciences - Uncategorised
document_store_str 1
active_str 0
description This paper deals with the development of effective techniques to automatically obtain the optimum management of petroleum fields aiming to increase the oil production during a given concession period of exploration. The optimization formulations of such a problem turn out to be highly multimodal, and may involve constraints. In this paper, we develop a robust particle swarm algorithm coupled with a novel adaptive constraint-handling technique to search for the global optimum of these formulations. However, this is a population-based method, which therefore requires a high number of evaluations of an objective function. Since the performance evaluation of a given management scheme requires a computationally expensive high-fidelity simulation, it is not practicable to use it directly to guide the search. In order to overcome this drawback, a Kriging surrogate model is used, which is trained offline via evaluations of a High-Fidelity simulator on a number of sample points. The optimizer then seeks the optimum of the surrogate model.
published_date 2015-09-30T03:26:15Z
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score 11.01753

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