Multi-objective optimization for rapid and robust optimal oilfield development under geological uncertainty

Chang, Yuqing; Bouzarkouna, Zyed; Devegowda, Deepak

doi:10.1007/s10596-015-9507-6

Cited by 62 publications

(7 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The new NSGA-II multi-objective optimization algorithm was used to solve the proposed model. This algorithm is based on References [29][30][31] (described in detail in Reference [32]), and it performs far better than the NSGA algorithm, in which non-dominated fronts, called the Pareto fronts, are obtained for the problem of locating the capacitors in the presence of harmonics for various objectives. Figure 2 shows the flow of the NSGA-II algorithm [32].…”

Section: Proposed Multi-objective Optimization Nsga-ii Methodsmentioning

confidence: 99%

A Novel Method of Optimal Capacitor Placement in the Presence of Harmonics for Power Distribution Network Using NSGA-II Multi-Objective Genetic Optimization Algorithm

Moghadam

Falaghi

Farhadi

2020

MCA

View full text Add to dashboard Cite

One of the effective ways of reducing power system losses is local compensation of part of the reactive power consumption by deploying shunt capacitor banks. Since the capacitor’s impedance is frequency-dependent and it is possible to generate resonances at harmonic frequencies, it is important to provide an efficient method for the placement of capacitor banks in the presence of nonlinear loads which are the main cause of harmonic generation. This paper proposes a solution for a multi-objective optimization problem to address the optimal placement of capacitor banks in the presence of nonlinear loads, and it establishes a reasonable reconciliation between costs, along with improvement of harmonic distortion and a voltage index. In this paper, while using the harmonic power flow method to calculate the electrical quantities of the grid in terms of harmonic effects, the non-dominated sorting genetic (NSGA)-II multi-objective genetic optimization algorithm was used to obtain a set of solutions named the Pareto front for the problem. To evaluate the effectiveness of the proposed method, the problem was tested for an IEEE 18-bus system. The results were compared with the methods used in eight other studies. The simulation results show the considerable efficiency and superiority of the proposed flexible method over other methods.

show abstract

Section: Proposed Multi-objective Optimization Nsga-ii Methodsmentioning

confidence: 99%

A Novel Method of Optimal Capacitor Placement in the Presence of Harmonics for Power Distribution Network Using NSGA-II Multi-Objective Genetic Optimization Algorithm

Moghadam

Falaghi

Farhadi

2020

MCA

View full text Add to dashboard Cite

show abstract

“…They concluded that the NSGA II as an excellent algorithm for solving a multi objective optimization problem. It has also been shown that the multi-objective optimization technique NSGA-II applied to a project was efficient in searching for multiple solutions and was able to find a pareto front after a few iterations during the optimization process [31]. NSGA-II applies an elitist strategy which improves the convergence of an MOEA and avoids the loss of optimal solutions after getting them [32].…”

Section: Nsga-ii (Non-dominated Sorting Genetic Algorithm Ii)mentioning

confidence: 99%

Simulation and Genetic Algorithms to Improve the Performance of an Automated Manufacturing Line

Ruane

Walsh

Cosgrove

2022

Acta Tech. Jaurinensis

View full text Add to dashboard Cite

Simulation in manufacturing is often applied in situations where conducting experiments on a real system is very difficult often because of cost or the time to carry out the experiment is too long. Optimization is the organized search for such designs and operating modes to find the best available solution from a set of feasible solutions. It determines the set of actions or elements that must be implemented to achieve an optimized manufacturing line. As a result of being able to concurrently simulate and optimize equipment processes, the understanding of how the actual production system will perform under varying conditions is achieved. The author has adopted an open-source simulation tool (JaamSim) to develop a digital model of an automated tray loader manufacturing system in the Johnson & Johnson Vision Care (JJVC) manufacturing facility. This paper demonstrates how a digital model developed using JaamSim was integrated with an author developed genetic algorithm optimization system and how both tools can be used for the optimization and development of an automated manufacturing line in the medical devices industry.

show abstract

“…Alternatively trade-offs between long-term and short term optimization may be presented in the form of a Pareto front. Robust versions of such hierarchical and/or multi-objective optimization studies have Post-print been published by, e.g., Chen et al (2012), Fonseca et al (2015), Chang et al (2015) and Liu and Reynolds (2016).…”

Section: Application Case Reservoir Engineering -Long-term Reservoir mentioning

confidence: 99%

Recent Developments in Closed-Loop Approaches for Real-Time Mining and Petroleum Extraction

Benndorf

2017

Math Geosci

View full text Add to dashboard Cite

Advanced data acquisition and process modelling technology provide 'real-time' data and decision support capacity for different aspects of the resource extraction process. Closed-loop approaches have recently been applied to utilize information extracted from these data in combination with advanced computing technology for improved production control in mineral resource extraction. Similar techniques have been developed in the petroleum industry combining computer-assisted model updating with model-based production optimization. This contribution reviews recent developments and methods applied, highlights differences and assesses the potential value added for both application domains. The focus here is on the two main constituents of closed-loop concepts, data assimilation and optimization. Technological readiness of the constituents is assessed, and gaps for further technology development are identified. The value added is illustrated by means of selected cases. Key Wordsclosed-loop reservoir management, real-time mining, data assimilation, optimization Published as Benndorf, J. and Jansen, J.D., 2017: Recent developments in closed-loop approaches for realtime mining and petroleum extraction. Mathematical Geosciences 49 (3) 277-306.

show abstract

Multi-objective optimization for rapid and robust optimal oilfield development under geological uncertainty

Cited by 62 publications

References 45 publications

A Novel Method of Optimal Capacitor Placement in the Presence of Harmonics for Power Distribution Network Using NSGA-II Multi-Objective Genetic Optimization Algorithm

A Novel Method of Optimal Capacitor Placement in the Presence of Harmonics for Power Distribution Network Using NSGA-II Multi-Objective Genetic Optimization Algorithm

Simulation and Genetic Algorithms to Improve the Performance of an Automated Manufacturing Line

Recent Developments in Closed-Loop Approaches for Real-Time Mining and Petroleum Extraction

Contact Info

Product

Resources

About