On the analytical extension of gas condensate plus fraction

Ghasemi, Maryam; Sedarat, Ehsan; Gerami, Shahab; Ebrahimzadeh, Shima; Emadi, Mohammad Ali

doi:10.1016/j.jngse.2014.07.013

Cited by 2 publications

(1 citation statement)

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“…If the default values of the controlling parameters of the distribution functions are employed to characterize the plus fractions, they become independent from the reservoir fluid type. However, it is reported in the literature that the parameters of the distribution functions are highly dependent on the fluid type and the plus fraction properties …”

Section: Methodsmentioning

confidence: 99%

Optimal distribution function determination for plus fraction splitting

2019

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Reservoir fluid modelling is one of the most important steps in reservoir simulation and modelling of flow lines as well as surface facilities. One of the most uncertain parameters of the reservoir fluids is the plus fraction. An accurate and consistent splitting scheme can reduce this uncertainty and as a result, enhance the modelling of reservoir fluids. The existing schemes for splitting plus fractions are all based on assuming a specific mole fraction-molecular weight distribution with predefined constant values that may yield inaccurate and inconsistent results. In this study, an optimization-based algorithm was developed to determine the aforementioned controlling parameters of the plus fraction distribution function, enforcing the relationship between specific gravity and molecular weight of the single carbon numbers (SCNs). The introduced optimizationbased splitting technique was applied to different samples, covering a wide range of reservoir fluids, including gas condensates, volatile oils, black oils, and heavy oils. The results showed that the proposed technique yielded a more consistent molecular weight-mole fraction distribution concerning the experimental extended analysis of plus fractions, yielding an average relative error of 25.8 % compared to 76, 33.6, and 45.9 % for the Katz, Ahmed, and Whitson methods, respectively. It was also shown that the proposed method results in more accurate and more consistent phase behaviour predictions than the existing methods concerning the experimental data. Furthermore, the results showed that the introduced optimization-based method yields monotonic split samples regarding specific gravity and molecular weight, while the conventional techniques do not guarantee to preserve the monotonicity.

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Section: Methodsmentioning

confidence: 99%