Well placement optimization: A survey with special focus on application for gas/gas-condensate reservoirs

Nasrabadi, Hadi; Morales, Adrián; Zhu, Ding

doi:10.1016/j.jngse.2011.10.002

Cited by 45 publications

(19 citation statements)

References 70 publications

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“…Several new methods are suggested by researchers, and only a few studies have presented a careful comparison of their performance with more popular genetic algorithmbased and gradient-based optimization techniques. (Onswnulu and Durlofski 2010;Nasrabadi et al 2012).…”

Section: Optimization Methodsmentioning

confidence: 99%

“…Gradient-based method is an important class of optimization methods. This method provides an improved objective function; each iteration results in a better well placement scenario, close to the original selection within a few iterations (Nasrabadi et al 2012).…”

Section: Gradient Methodsmentioning

confidence: 99%

“…A genetic algorithm, in its purest form, will try to replicate the concepts of natural evolution, in a controlled and mathematical environment. In a well placement optimization problem, the different individuals in a generation are replaced with well location data, and their cumulative production or NPV is a measure of their chance of survival (Nasrabadi et al 2012). The first step in optimization of well placement by genetic algorithm is to generate an initial population (randomly selected well locations).…”

Section: Genetic Algorithmmentioning

confidence: 99%

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Investigating the effect of heterogeneity on infill wells

Bagheri

Masihi

2015

J Petrol Explor Prod Technol

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In recent years, improving oil recovery (IOR) has become an important subject for the petroleum industry. One IOR method is infill drilling, which improves hydrocarbon recovery from virgin zones of the reservoir. Determining the appropriate location for the infill wells is very challenging and greatly depends on different factors such as the reservoir heterogeneity. This study aims to investigate the effect of reservoir heterogeneity on the location of infill well. In order to characterize the effect of heterogeneity on infill well locations, some geostatistical methods, e.g., sequential gaussian simulation, have been applied to generate various heterogeneity models. In particular, different correlation ranges (R) were used to observe the effect of heterogeneity. Results revealed that an increase in correlation ranges will lead to (1) a higher field oil production total, and (2) a faster expansion of the drainage radius which consequently reduced the need for infill wells. The results of this study will help engineers to appropriately design infill drilling schemes.

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Section: Optimization Methodsmentioning

confidence: 99%

Section: Gradient Methodsmentioning

confidence: 99%

Section: Genetic Algorithmmentioning

confidence: 99%

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Investigating the effect of heterogeneity on infill wells

Bagheri

Masihi

2015

J Petrol Explor Prod Technol

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“…The aspects of determining the best option of field development were studied by C. I. Nkemakolam [1], H. Nasrabadi [2], A. Abdrakhmanov [3], M. A.…”

Section: Introductionmentioning

confidence: 99%

Mathematical modeling and optimization of gas-condensate field development

Kalugin

Yakovlev

Kalugin

2015

Journal of Natural Gas Science and Engineering

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“…For instance, it is typical for the well placement optimization problem to be solved around optimizing the well location, configuration (vertical, horizontal, multilateral), type (producer, injector), operation status (open, closed) (Nasrabadi, H., 2012). Decision variables with uncertainties and solution constraints lead to a very complex and large solution space resulting in many possible solution combinations.…”

Section: Introductionmentioning

confidence: 99%

Well Placement Optimization Using Imperialist Competition Algorithm

Dossary

Nasrabadi

2015

Day 3 Wed, September 16, 2015

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Developing an efficient and optimized field development plan is a crucial and vital task that aims to increase the productivities as well as the recovery factors of oil and gas fields and hence increase the profitability in the most effective manner. In this paper, a novel algorithm has been applied for the first time in oil & gas industry to find the best optimum well location for maximum well productivity. The Imperialist Competitive Algorithm (ICA) is an evolutionary algorithm that mimics the social political imperialist competition. In this algorithm, an initial population that consists of colonies and imperialists are assigned to several empires. The empires then compete with each other and weak empires collapse and the powerful empires possess their colonies. The ICA performance was compared with the well-known Genetic Algorithm (GA) in three optimization scenarios: 1) a vertical well in a heterogeneous reservoir, 2) a vertical well in a channeled reservoir, and 3) a horizontal well in a channeled reservoir. In all three scenarios, it was observed that the ICA resulted in better solution compared to GA at a fixed number of simulation runs.

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Well placement optimization: A survey with special focus on application for gas/gas-condensate reservoirs

Cited by 45 publications

References 70 publications

Investigating the effect of heterogeneity on infill wells

Investigating the effect of heterogeneity on infill wells

Mathematical modeling and optimization of gas-condensate field development

Well Placement Optimization Using Imperialist Competition Algorithm

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