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

M.S. Innocente; Silvana Maria Bastos Afonso; Johann Sienz; Helen M. Davies

doi:10.1016/j.asoc.2015.05.032

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

M.S. Innocente, Silvana Maria Bastos Afonso, Johann Sienz, Helen M. Davies

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18 Citations (Scopus)

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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, 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.

Original language	English
Pages (from-to)	463-484
Number of pages	22
Journal	Applied Soft Computing
Volume	34
Early online date	28 May 2015
DOIs	https://doi.org/10.1016/j.asoc.2015.05.032
Publication status	Published - Sept 2015
Externally published	Yes

Bibliographical note

NOTICE: this is the author’s version of a work that was accepted for publication in Applied Soft Computing. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Applied Soft Computing, 34 (2015)] DOI: 10.1016/j.asoc.2015.05.032

© 2015, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/

Keywords

Adaptive constraint handling
Global search
Particle swarm
Reservoir simulation
Surrogate-based optimization
Waterflooding management

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10.1016/j.asoc.2015.05.032

Post-PrintAccepted author manuscript, 2.15 MBLicence: CC BY-NC-ND

Mauro Innocente
- Centre for Future Transport and Cities - Assistant Professor Academic
Person: Teaching and Research

Cite this

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

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, 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.",

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AB - 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.

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KW - Global search

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KW - Surrogate-based optimization

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M3 - Article

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SP - 463

EP - 484

JO - Applied Soft Computing

JF - Applied Soft Computing

ER -

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

Abstract

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Keywords

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Mauro Innocente

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