Particle swarm algorithm with adaptive constraint handling and integrated surrogate model for the management of petroleum fields |
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Affiliation: | 1. Civil & Computational Engineering Centre, College of Engineering, Swansea University, Singleton Park, Swansea SA2 8PP, United Kingdom;2. Dept. Eng. Civil, Universidade Federal de Pernambuco, Recife, PE, Brazil;1. CominLabs, Université Europénne de Bretagne (UEB) and Supélec, Rennes, France;2. Department of Computer Engineering, Nanyang Technological University, Singapore;3. Department of Signal, Communication et Electronique Embarquée (SCEE), Supélec/IETR, Rennes, France |
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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. |
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Keywords: | Adaptive constraint handling Global search Particle swarm Reservoir simulation Surrogate-based optimization Waterflooding management |
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