Optimization of oil production from petroleum reservoirs is an interesting and complex problem which can be done by optimal control of well parameters such as their flow rates and pressure. Different optimization techniques have been developed yet, and metaheuristic algorithms are commonly employed to enhance oil recovery projects. Among different metaheuristic techniques, the genetic algorithm (GA) and the particle swarm optimization (PSO) have received more attention in engineering problems. These methods require a population and many objective function calls to approach more the global optimal solution. However, for a water flooding project in a reservoir, each function call requires a long time reservoir simulation. Hence, it is necessary to reduce the number of required function evaluations to increase the rate of convergence of optimization techniques. In this study, performance of GA and PSO are compared with each other in an enhanced oil recovery (EOR) project, and Newton method is linked with PSO to improve its convergence speed. Furthermore, hybrid genetic algorithm-particle swarm optimization (GA-PSO) as the third optimization technique is introduced and all of these techniques are implemented to EOR in a water injection project with 13 decision variables. Results indicate that PSO with Newton method (NPSO) is remarkably faster than the standard PSO (SPSO). Also, the hybrid GA-PSO method is more capable of finding the optimal solution with respect to GA and PSO. In addition, GA-PSO, NPSO, and GA-NPSO methods are compared and, respectively, GA-NPSO and NPSO showed excellence over GA-PSO.
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October 2018
Research-Article
A Comparative Study of Genetic and Particle Swarm Optimization Algorithms and Their Hybrid Method in Water Flooding Optimization
Majid Siavashi,
Majid Siavashi
Applied Multi-Phase Fluid Dynamics Laboratory,
School of Mechanical Engineering,
Iran University of Science and Technology,
Tehran 1684613114, Iran
e-mail: msiavashi@iust.ac.ir
School of Mechanical Engineering,
Iran University of Science and Technology,
Tehran 1684613114, Iran
e-mail: msiavashi@iust.ac.ir
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Mohsen Yazdani
Mohsen Yazdani
School of Mechanical Engineering,
Iran University of Science and Technology,
Tehran 1684613114, Iran
e-mail: yazdani.msn@gmail.com
Iran University of Science and Technology,
Tehran 1684613114, Iran
e-mail: yazdani.msn@gmail.com
Search for other works by this author on:
Majid Siavashi
Applied Multi-Phase Fluid Dynamics Laboratory,
School of Mechanical Engineering,
Iran University of Science and Technology,
Tehran 1684613114, Iran
e-mail: msiavashi@iust.ac.ir
School of Mechanical Engineering,
Iran University of Science and Technology,
Tehran 1684613114, Iran
e-mail: msiavashi@iust.ac.ir
Mohsen Yazdani
School of Mechanical Engineering,
Iran University of Science and Technology,
Tehran 1684613114, Iran
e-mail: yazdani.msn@gmail.com
Iran University of Science and Technology,
Tehran 1684613114, Iran
e-mail: yazdani.msn@gmail.com
1Corresponding author.
Contributed by the Petroleum Division of ASME for publication in the JOURNAL OF ENERGY RESOURCES TECHNOLOGY. Manuscript received September 26, 2017; final manuscript received April 12, 2018; published online May 15, 2018. Assoc. Editor: John Killough.
J. Energy Resour. Technol. Oct 2018, 140(10): 102903 (10 pages)
Published Online: May 15, 2018
Article history
Received:
September 26, 2017
Revised:
April 12, 2018
Citation
Siavashi, M., and Yazdani, M. (May 15, 2018). "A Comparative Study of Genetic and Particle Swarm Optimization Algorithms and Their Hybrid Method in Water Flooding Optimization." ASME. J. Energy Resour. Technol. October 2018; 140(10): 102903. https://doi.org/10.1115/1.4040059
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