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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
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.
Keywords: Adaptive constraint handling; Global search; Particle swarm; Reservoir simulation; Surrogate-based optimization; Waterflooding management
College: Faculty of Science and Engineering
Start Page: 463
End Page: 484

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