2008
DOI: 10.1080/10916460701776823
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Deterioration of Performance of Mixing Rules in Phase Behavior Modeling of High-Density Reservoir Fluids

Abstract: The mixing rules are used in the cubic equations of state to determine the values of the attractive force parameter, a, and the repulsive force parameter, b, mixtures. The mixing rules are applied here to reservoir fluids. It was discovered that parameter a should not be treated as a constant since it varied significantly with pressure. It was therefore regressed by two straight lines, and the resulting equation of state gave a very good fit to PVT data of reservoir fluids.

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Cited by 3 publications
(4 citation statements)
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“…The authors believe that the error propagated by the assumption that parameter 'a' is a constant for a multi component mixture, is responsible for the deficiencies of equations of state such that tuning and sometime elaborate adjustments are then required. From previous works by Babalola and Susu [18], plots for the three wells have been shown to exhibit significant variations of parameter 'a' with pressure. Each of these plots was regressed to obtain a best fit.…”
Section: Model Developmentmentioning
confidence: 76%
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“…The authors believe that the error propagated by the assumption that parameter 'a' is a constant for a multi component mixture, is responsible for the deficiencies of equations of state such that tuning and sometime elaborate adjustments are then required. From previous works by Babalola and Susu [18], plots for the three wells have been shown to exhibit significant variations of parameter 'a' with pressure. Each of these plots was regressed to obtain a best fit.…”
Section: Model Developmentmentioning
confidence: 76%
“…The work of Babalola and Susu [18], in addressing this issue, showed mathematically that when the variable parameter 'a' curve is regressed by using two straight lines for a reservoir fluid sample from an oil well-one straight line for pressures below bubble point pressure and another for pressures above it, the need for tuning would be automatically eliminated. They inferred that the regression by the straight lines intrinsically 'tunes' the EOS model for more accurate performance.…”
Section: Methodsmentioning
confidence: 99%
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