Compressibility Factor for Gas Condensates

Elsharkawy, Adel M.; Hashem, Yousef S. Kh. S.; Alikhan, Abbas A.

doi:10.2118/59702-ms

Cited by 10 publications

(10 citation statements)

References 12 publications

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“…(11) proposed by Sutton, (2005) were also employed in this study. According to Sutton, (2005) these two equations outperform other well-known pseudo critical properties in the literature such as Elsharkawy et al, (2000), Sutton, (1985) and Piper et al,(1993). Fig.…”

Section: Accepted Manuscriptmentioning

confidence: 80%

“…New correlation was incorporated with two-phase compressibility factor of Rayes et al, (1992) in generating PVT tables and determining pseudopressure integral. Pseudo critical temperature (Tpc) and pressure (Ppc) proposed by Elsharkawy et al, (2000), which developed for gas condensate reservoirs were also used to model two phase compressibility factor. Transient pressure test data from high temperature heavy gas condensate well was obtained from Economides et al, (1989) and utilized to generate the IPR curves.…”

Section: Accepted Manuscriptmentioning

confidence: 99%

“…( Accurate prediction of gas condensate reservoirs require using more accurate compressibility factor (Elsharkawy et al, 2000;Whitson et al, 1999). In fact compressibility factor dictates gas and condensate recoveries in gas condensate reservoirs (Whitson et al, 2000(Whitson et al, , 1999.…”

Section: Accepted Manuscriptmentioning

confidence: 99%

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Development of inflow performance model in high temperature gas-condensate reservoirs

Faraji¹,

Ugwu²,

Nabhani³

et al. 2019

Journal of Petroleum Science and Engineering

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Inflow Performance Relationships (IPRs) are important element for reservoir engineers in the design of new wells and also for monitoring and optimizing existing wells. IPRs are used to determine optimum production of gas rate and condensate rate in a well for any specified value of average reservoir pressure and predict the performance. Jokhio and Tiab proposed a simple method of establishing IPR for gas condensate wells. The method uses transient pressure test data to estimate effective permeability as a function of pressure. Effective permeability data used to convert production bottomhole flow pressure into pseudopressure to establish well performance. Despite the effectiveness of the method, single phase correlations were used in PVT calculations of each phase, which over simplified the fluid flow in gas condensate wells. Single phase dry gas equations do not reflect the multiphase flow behaviour of gas condensate wells below the dew point. Due to this limitation Jokhio and Tiab method modified by this study and new analytical IPRs for gas condensate well proposed. The major improvement of the above method is incorporating new viscosity correlation developed by this study and using two-phase compressibility factor as key parameters for predicting gas condensate inflow performance. Therefore, the main contribution of this study is development of viscosity correlation which is a critical issue in predicting gas condensate inflow performance both above and below the dew point. Optimization techniques and nonlinear regression used to develop a new viscosity correlation for high temperature heavy gas condensate reservoirs under depletion. The application of the new model is illustrated with field example for current IPR curves. Compositional simulation study of the well performed in PIPSIM simulator. The proposal approach provides reasonable estimates of simulator input reservoir properties (e.g. IPRs). Accuracy of the new method compared with compositional simulation study. The proposed method presents average absolute relative deviation (AARD) of 5.8% for gas IPR and 7.5% for condensate IPR compare to compositional simulation results. New method provides a tool for quick estimation of gas condensate wells without need of relative permeability curves and expensive and time consuming compositional simulation.

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Section: Accepted Manuscriptmentioning

confidence: 80%

Section: Accepted Manuscriptmentioning

confidence: 99%

See 1 more Smart Citation

Development of inflow performance model in high temperature gas-condensate reservoirs

Faraji¹,

Ugwu²,

Nabhani³

et al. 2019

Journal of Petroleum Science and Engineering

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“…Many other researchers including Azizi and Behbahani, 32 Elsharkawy et al, 2,26 Ghiasi et al 33 Kareem, 35 Kareem et al, 34 and Kamari et al 36 developed explicit Z-factor correlations valid for the pseudopressure range up to 0.2 ≤ p pr ≤ 15. Azizi et al 37 predicted a gas deviation factor using black box type of artificial neural network (ANN), which means that the users of their article will not be able to use their model for the Z-factor prediction.…”

Section: Energy and Fuelsmentioning

confidence: 99%

“…5,6 In a laboratory, the gas deviation factor is determined by applying the real gas law with the assumption that real gas behavior is identical to that of ideal gas at ambient pressure and temperature conditions. Real gas law can be stated by eq 2 = pV nZRT (2) where "p" is the specified pressure, "v" is the specified volume of the gas, "n" is the number of moles, "R" is the universal gas constant, "Z" is the gas deviation factor, and "T" is the specified temperature.…”

Section: Introductionmentioning

confidence: 99%

New Correlation for the Gas Deviation Factor for High-Temperature and High-Pressure Gas Reservoirs Using Neural Networks

Tariq

Mahmoud

2019

Energy Fuels

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The gas deviation factor (Z-factor) is an effective thermodynamic property required to address the deviation of real gas behavior from that of an ideal gas. Empirical models and correlations to compute Z-factor based on equation of states are often implicit because they needed a huge number of iterations and thus were computationally very expensive. Many explicit empirical correlations are also reported in the literature to improve the simplicity; yet, no individual explicit correlation was formulated for the complete full range of pseudoreduced temperatures and pseudoreduced pressures, which demonstrates a significant research gap. The inaccuracy in determining the gas deviation factor will result in a huge error in computing subsequent natural gas engineering properties such as gas expansion factor (E g), formation volume factor (B g), gas compressibility (c g), and original gas in place. Previously reported empirical correlations provide better estimation of the gas deviation factor at lower pressures, but at higher reservoir pressures, their accuracies become questionable. One of the examples of high-pressure reservoirs is the abnormal pressured reservoir, where the reservoir pressure exists up to 20 000 psia. In this study, an improved Z-factor empirical correlation is presented in a linear fashion using a robust artificial intelligence tool, the artificial neural network (ANN). The new correlation is trained on more than 3000 data points from laboratory experiments obtained from several published sources. The proposed correlation is only a function of pseudoreduced temperature (T pr) and pseudoreduced pressure (p pr) of the gases, which makes it easier to implement than the reported implicit and complicated explicit correlations. The proposed correlation can be valid for p pr ranges between 0.1 and 40 and T pr ranges between 1.05 and 3.05. The accuracy and generalization capabilities of the proposed ANN-based correlation are also tested against previously published correlations at low and high gas reservoir pressures on an unseen published dataset. The comparative results on a published dataset show that the new correlation outperformed other methods of predicting Z-factor by giving less average absolute percentage error, less root-mean-square error, and high coefficient of determination (R 2). The error obtained was less than 3% compared to the measured data, while the other correlations predicted the gas deviation factor with an error up to 4% at low pressure and up to 20% at high pressure. The new proposed ANN-based correlation can be utilized to estimate the Z-factor at any pressure range (on which the model is trained) especially for high pressures. The new proposed correlation is very easy to be used, and it requires only the gas-specific gravity that is needed to determine the pseudocritical properties of the real gas and from which the Z-factor can be determined.

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Constructing a unique two-phase compressibility factor model for lean gas condensates

Moayyedi¹,

Azamifard

Gharesheikhlou³

et al. 2014

Korean J. Chem. Eng.

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Compressibility Factor for Gas Condensates

Cited by 10 publications

References 12 publications

Development of inflow performance model in high temperature gas-condensate reservoirs

Development of inflow performance model in high temperature gas-condensate reservoirs

New Correlation for the Gas Deviation Factor for High-Temperature and High-Pressure Gas Reservoirs Using Neural Networks

Constructing a unique two-phase compressibility factor model for lean gas condensates

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