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Particle swarm algorithm with adaptive constraint handling and integrated surrogate model for the management of petroleum fields
Applied Soft Computing, Volume: 34, Pages: 463 - 484
Swansea University Authors:
Johann Sienz , Helen Davies
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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...
Published in: | Applied Soft Computing |
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ISSN: | 1568-4946 |
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2015
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URI: | https://cronfa.swan.ac.uk/Record/cronfa22073 |
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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 |
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facultyofscienceandengineering |
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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 |
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active_str |
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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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1763750944040812544 |
score |
11.021648 |