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Research Papers: Petroleum Engineering

A Comparative Study of Genetic and Particle Swarm Optimization Algorithms and Their Hybrid Method in Water Flooding Optimization

[+] Author and Article Information
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

Mohsen Yazdani

School of Mechanical Engineering,
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 140(10), 102903 (May 15, 2018) (10 pages) Paper No: JERT-17-1413; doi: 10.1115/1.4040059 History: Received September 26, 2017; Revised April 12, 2018

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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Figures

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Fig. 1

Flowchart of the GA

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Fig. 2

GA-PSO algorithm performance

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Fig. 3

Repeated five-spot well arrangement and permeability distribution in the reservoir (Log10 permeability in md)

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Fig. 4

Oil saturation distribution after 500 days

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Fig. 5

COP of each producer well

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Fig. 6

Comparison of SPSO and NPSO algorithms in terms of the convergence history (COP in MSm3)

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Fig. 7

Comparison of NPSO, GA-PSO, and GA-NPSO algorithms for the rate of convergence and their precision in finding the final optimum result (MSm3)

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Fig. 8

Comparison of NPSO and GA-NPSO algorithms for the rate of convergence and their precision in finding the final optimum solution (MSm3)

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Fig. 9

Oil saturation distribution for the base case (up), second optimal case (middle), and third optimal case (down) on days of 500, 1000, and 2000, respectively, from left to right

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