On the application of artificial bee colony (ABC) algorithm for optimization of well placements in fractured reservoirs; efficiency comparison with the particle swarm optimization (PSO) methodology

Nozohour-leilabady, Behzad; Fazelabdolabadi, Babak

doi:10.1016/j.petlm.2015.11.004

Cited by 55 publications

(18 citation statements)

References 14 publications

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“…The cases used herein were similar to that described in Example 1 of the publication by Nozohour-leilabady and Fazelabdolabadi [7].…”

Section: Test Casesmentioning

confidence: 88%

“…These algorithms help to automate the otherwise cumbersome process of manual Well placement by performing direct or stochastic searches to optimize Well placement [7].…”

Section: Algorithmmentioning

confidence: 99%

“…So far, the great potential of the ABC algorithm has only been exploited by two researchers in the Petroleum industry namely; Nozohour-leilabady and Fazelabdolabadi [7], who used it to optimize well placement, and Irani and Nasimi [12], who used it to train neural network for bottom hole prediction in underbalanced drilling.…”

Section: Abc Algorithmmentioning

confidence: 99%

“…Nozohour-leilabady and Fazelabdolabadi [7]. It was defined mathematically as can be seen in Equation (5).…”

Section: Objective Function Developmentmentioning

confidence: 99%

See 3 more Smart Citations

Algorithms for the Optimization of Well Placements—A Comparative Study

Udoeyop¹,

Oboh²,

Afiakinye³

2018

ACES

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The Artificial Bee Colony (ABC) is one of the numerous stochastic algorithms for optimization that has been written for solving constrained and unconstrained optimization problems. This novel optimization algorithm is very efficient and as promising as it is; it can be favourably compared to other optimization algorithms and in some cases, it has been proven to be better than some known algorithms (like Particle Swarm Optimization (PSO)), especially when used in Well placement optimization problems that can be encountered in the Petroleum industry. In this paper, the ABC algorithm has been modified to improve its speed and convergence in finding the optimum solution to a well placement optimization problem. The effects of variations of the control parameters for both algorithms were studied, as well as the algorithms' performances in the cases studied. The modified ABC (MABC) algorithm gave better results than the Artificial Bee Colony algorithm. It was noticed that the performance of the ABC algorithm increased with increase in the number of its optimization agents for both algorithms studied. The modified ABC algorithm overcame the challenge posed by the use of uniformly generated random numbers with very rough NPV surface. This new modified ABC algorithm proposed in this work will be a great tool in optimization for the Petroleum industry as it involves Well placements for optimum oil production.

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“…The cases used herein were similar to that described in Example 1 of the publication by Nozohour-leilabady and Fazelabdolabadi [7].…”

Section: Test Casesmentioning

confidence: 88%

“…These algorithms help to automate the otherwise cumbersome process of manual Well placement by performing direct or stochastic searches to optimize Well placement [7].…”

Section: Algorithmmentioning

confidence: 99%

Section: Abc Algorithmmentioning

confidence: 99%

“…Nozohour-leilabady and Fazelabdolabadi [7]. It was defined mathematically as can be seen in Equation (5).…”

Section: Objective Function Developmentmentioning

confidence: 99%

See 2 more Smart Citations

Algorithms for the Optimization of Well Placements—A Comparative Study

Udoeyop¹,

Oboh²,

Afiakinye³

2018

ACES

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“…Those two positions change dynamically depending on the velocity of the population which is one of the most important parameters for the algorithm convergence. The PSO algorithm like other optimization algorithms [11,12] needs an objective function with parameters to optimize. In our case, the objective function is the equation describing the theoretical model of Lorentz oscillators.…”

mentioning

confidence: 99%

Numerical Characterization of Vanadium Dioxide Thin Films Applied to Thermal Building Insulation: Determination of Dielectric Constant Using PSO Algorithm

Iken¹,

Bouyghf²,

Bouazaoui³

et al. 2017

IJET

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Abstract-Radiative rectification is one of the newest solutions for thermal building insulation. It is a phenomenon which has good analogy with electronic diode principle. It consists on being blocking for solar radiations in summer and passing radiations in winter. Among materials which have rectification capacity, Vanadium dioxide (VO 2 ) offers good applicability to building insulation with quite good rectification efficiency. VO 2 has a transition temperature around 68°C. Under this temperature, the material has a semiconductor crystallographic structure; therefore it becomes transparent to visible and infrared solar spectra. Above 68°C, the crystallographic structure of VO 2 changes to a metallic state for which it becomes more reflective to the same spectra. This semiconductor/metallic transition influences the optical properties of VO 2 which are highly dependent to temperature and wavelength. These properties are refractive index n(T,λ) and extinction coefficient k(T,λ), they are necessary in the optical study of VO 2 insulation capacities. Determination of these properties is purely experimental using ellipsometry techniques. In this work we suggest a method for numerical determination of these optical properties. This method uses the Particle Swarm Optimization (PSO) algorithm and it is based on the theoretical model of Lorentz oscillators at VO 2 nanoparticles scale. We calculated n(T,λ) and k(T,λ) for 3 temperatures and compared them with experimental results. This comparison showed good agreement between numerical and experimental results.Vanadium-dioxide, radiative rectification, building insulation, optical properties, optimization algorithms. I. INTRODUCTION Vanadium dioxide is a metal oxide that attracts many research teams over the world. It offers thermo-chromic properties depending on its semiconductor/metallic phase transition around 68°C [1,2]. VO 2 has many potential applications as a photonic device. In our case, we are trying to use VO 2 as a smart solution for radiative rectification applied to thermal building insulation, a smart solution because of its thermo-chromic properties highly dependent to temperature. At temperatures less than 68°C, VO 2 has a semiconductor crystallographic structure, with inter-atomic distances which allow high radiative transmission. Above 68°C, VO 2 switches to a metallic structure with small inter-atomic distances, thus VO 2 become more reflective to solar radiations. VO 2 is proposed for application to building glazing as smart window coatings by C.G. Granqvist et al [3]. The transition temperature too high compared with the comfort temperature is one of the limitations for smart windows application. G.V. Jorgenson et al [4] and W. Burkhardt et al [5] proposed a solution to decrease the VO 2 transition temperature by doping with some materials such as tungsten (W), Fluoride (F) and Molybdenum (Mo) with calculated concentrations to adjust the transition temperature around 25°C. The second limitation is related to high absorption in the visible part of sol...

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Packet Delivery Comparison Using Artificial Bee Colony Algorithm with Dynamic Technique

Beg

Waoo

2022

Human-Centric Smart Computing

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On the application of artificial bee colony (ABC) algorithm for optimization of well placements in fractured reservoirs; efficiency comparison with the particle swarm optimization (PSO) methodology

Cited by 55 publications

References 14 publications

Algorithms for the Optimization of Well Placements—A Comparative Study

Algorithms for the Optimization of Well Placements—A Comparative Study

Numerical Characterization of Vanadium Dioxide Thin Films Applied to Thermal Building Insulation: Determination of Dielectric Constant Using PSO Algorithm

Packet Delivery Comparison Using Artificial Bee Colony Algorithm with Dynamic Technique

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