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Particle swarm algorithm with adaptive constraint handling and integrated surrogate model for the management of petroleum fields / Johann, Sienz; Helen, Davies

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

Swansesa University Authors: Johann, Sienz, Helen, Davies

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
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa22073
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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.
Keywords: Adaptive constraint handling; Global search; Particle swarm; Reservoir simulation; Surrogate-based optimization; Waterflooding management
College: College of Engineering
Start Page: 463
End Page: 484