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

• We seek optimum management of waterflooding process in oil reservoirs.
• Due to high computational cost of simulations, Kriging surrogate models are built.
• Two formulations are proposed to better control particles’ individual behavior.
• Adaptive constraint-handling technique developed and integrated into particle swarm algorithm.
• Particle swarm algorithm with adaptive constraint handling optimizes surrogate model.

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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