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Frequency : 12 issues per year
Subject : Computer Applications and Technology
ISSN : 2319–8656 (Online)
Home Archives Volume 8 Issue 9 ijcatr08091003

IJCATR Volume 8 Issue 9

Optimization of Lift Gas Allocation using Evolutionary Algorithms

Sofía López, Urhan Koç, Emma Bakker, Javad Rahmani
10.7753/IJCATR0809.1003
keywords : particle swarm optimization; crude oil lifting; lift gas allocation; optimization; artificial neural network; genetic algorithm.

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In this paper, the particle swarm optimization (PSO) algorithm is proposed to solve the lift gas optimization problem in the crude oil production industry. Two evolutionary algorithms, genetic algorithm (GA) and PSO, are applied to optimize the gas distribution for oil lifting problem for a 6-well and a 56-well site. The performance plots of the gas intakes are estimated through the artificial neural network (ANN) method in MATLAB. Comparing the simulation results using the evolutionary optimization algorithms and the classical methods, proved the better performance and faster convergence of the evolutionary methods over the classical approaches. Moreover, the convergence rate of PSO is 13 times faster than GA's for this problem.
@artical{s892019ijcatr08091003,
Title = "Optimization of Lift Gas Allocation using Evolutionary Algorithms",
Journal ="International Journal of Computer Applications Technology and Research(IJCATR)",
Volume = "8",
Issue ="9",
Pages ="353 - 357",
Year = "2019",
Authors ="Sofía López, Urhan Koç, Emma Bakker, Javad Rahmani"}
  • The paper proposes a particle swarm optimization (PSO) algorithm for the lift gas optimization problem in a crude oil production industry.
  • Two evolutionary algorithms, genetic algorithm (GA) and PSO, are applied to optimize the gas distribution for oil lifting problem for a 6-well and a 56-well site.
  • Comparing the simulation results using the evolutionary optimization algorithms and the classical methods, proved the better performance and faster convergence of the evolutionary methods over the classical approaches.
  • The convergence rate of PSO in the results was 13 times faster than the GA’s.
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