Condensate gas reservoirs pose considerable management challenges due to their intricate phase behavior and the critical need for accurate predictions of dew point pressure (PDew). A precise determination of PDew is vital for optimizing production strategies, estimating reserves, and planning enhanced oil recovery operations. Traditional methods for PDew determination often involve experimental analyses that can be both costly and time-consuming, highlighting the necessity for alternative predictive models. This review consolidates recent advancements in methodologies for predicting PDew, encompassing empirical correlations, equation-of-state models, and cutting-edge artificial intelligence techniques such as neural networks and machine learning algorithms. Key studies discussed demonstrate that AI can significantly enhance the accuracy of PDew predictions through the application of genetic programming (GP), artificial neural networks (ANNs), and XGBoost across a variety of reservoir conditions. These advanced computational methods hold promising potential for improving reservoir characterization and management practices in gas condensate fields.

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1. Al-Dhamen, M. and M. Al-Marhoun (2011). New correlations for dew-point pressure for gas condensate. SPE Saudi Arabia section young professionals technical symposium, SPE.
2. Al-Fatlawi, O., et al. (2017). "Determination of best possible correlation for gas compressibility factor to accurately predict the initial gas reserves in gas-hydrocarbon reservoirs." International Journal of Hydrogen Energy 42(40): 25492-25508.
3. Ali, J., et al. (1997). Experimental studies and modelling of gas condensate flow near the Wellbore. SPE Latin America and Caribbean Petroleum Engineering Conference, SPE.
4. AL-Jawad, M. S. and O. F. Hasan (2012). Comprehensive model for flash calculations of heavy oils using the Soave-Redlich-Kwong equation of state. SPE North Africa Technical Conference and Exhibition, SPE.
5. Alzahabi, A., et al. (2017). "A regression model for estimation of dew point pressure from down-hole fluid analyzer data." Journal of Petroleum Exploration and Production Technology 7: 1173-1183.
6. Carlson, M. M. and W. B. Cawston (1996). Obtaining PVT data for very sour retrograde condensate gas and volatile oil reservoirs: a multi-disciplinary approach. SPE Unconventional Resources Conference/Gas Technology Symposium, SPE.
7. Coats, K. H. (1980). "An equation of state compositional model." Society of Petroleum Engineers Journal 20(05): 363-376.
8. Crogh, A. (1996). Improved correlations for retrograde gases, Texas A&M University.
9. Danesh, A. (1998). PVT and phase behaviour of petroleum reservoir fluids, Elsevier.
10. Echenique, E. P. (2016). "Gas Condensate Banking-Application of Pressure Transient Analysis to Determine Production Impairment and Compositional Effects."
11. Economides, M., et al. (1987). Hysteresis effects for gas condensate wells undergoing buildup tests below the dew point pressure. SPE Annual Technical Conference and Exhibition?, SPE.
12. Eilerts, C. K. and R. V. Smith (1942). Specific volumes and phase-boundary properties of separator-gas and liquid-hydrocarbon mixtures, US Department of the Interior, Bureau of Mines.
13. Elsharkawy, A. M. (2002). "Predicting the dew point pressure for gas condensate reservoirs: empirical models and equations of state." Fluid phase equilibria 193(1-2): 147-165.
14. Fang, Y., et al. (1998). Condensate gas phase behavior and development. SPE International Oil and Gas Conference and Exhibition in China, SPE.
15. Fevang, O. (1995). "Gas condensate flow behavior and sampling." Division of Petroleum Engineering and Applied Geophysics.
16. Fevang, Ø. and C. H. Whitson (1996). "Modeling gas-condensate well deliverability." SPE Reservoir Engineering 11(04): 221-230.
17. Ganie, K., et al. (2019). "Gas condensate banking: a review of the phenomenon, accumulation, mitigation methods, and new breakthrough." Science Proceedings Series 1(2): 62-65.
18. Ganie, K., et al. (2019). "A review on the wettability alteration mechanism in condensate banking removal." Journal of Petroleum Science and Engineering 183: 106431.
19. Godwin, O. N. (2012). A new analytical method for predicting dew point pressures for gas condensate reservoirs. SPE Nigeria Annual International Conference and Exhibition, SPE.
20. Gomaa, S. (2018). "Prediction of dew point pressure in gas condensate reservoirs based on a combination of gene expression programming (GEP) and multiple regression analysis."
21. González, A., et al. (2003). "Improved neural-network model predicts dewpoint pressure of retrograde gases." Journal of Petroleum Science and Engineering 37(3-4): 183-194.
22. Haji-Savameri, M., et al. (2020). "Modeling dew point pressure of gas condensate reservoirs: comparison of hybrid soft computing approaches, correlations, and thermodynamic models." Journal of Petroleum Science and Engineering 184: 106558.
23. Humoud, A. and M. Al-Marhoun (2001). A new correlation for gas-condensate dewpoint pressure prediction. SPE Middle East Oil and Gas Show and Conference, SPE.
24. Jalali, F., et al. (2007). Using artificial neural network's capability for estimation of gas condensate reservoir's dew point pressure. EUROPEC/EAGE conference and exhibition, One Petro.
25. Marruffo, I., et al. (2002). Correlations to determine retrograde dew pressure and C7+ percentage of gas condensate reservoirs on basis of production test data of eastern Venezuelan fields. SPE Unconventional Resources Conference/Gas Technology Symposium, SPE.
26. Nemeth, L. K. (1966). A correlation of dew-point pressure with reservoir fluid composition and temperature, Texas A&M University.
27. Nemeth, L. y. and H. Kennedy (1966). A correlation of dew-point pressure with fluid composition and temperature. Journal of Petroleum Technology, SOC PETROLEUM ENG 222 PALISADES CREEK DR, RICHARDSON, TX 75080.
28. Olds, R., et al. (1945). "Volumetric and phase behavior of oil and gas from Paloma Field." Transactions of the AIME 160(01): 77-99.
29. Organick, E. and B. Golding (1952). "Prediction of saturation pressures for condensate-gas and volatile-oil mixtures." Journal of Petroleum Technology 4(05): 135-148.
30. Potsch, K. and L. Braeuer (1996). A novel graphical method for determining dewpoint pressures of gas condensates. SPE Europec featured at EAGE Conference and Exhibition?, SPE.
31. Reamer, H. and B. Sage (1950). "Volumetric behavior of oil and gas from a Louisiana field I." Journal of Petroleum Technology 2(09): 261-268.
32. Sadeq, D. J. (2018). Gas hydrates investigation: flow assurance for gas production and effects on hydrate-bearing sediments, Curtin University.
33. Shokir, E. M. E.-M. (2008). Dewpoint pressure model for gas condensate reservoirs based on genetic programming. SPE Unconventional Resources Conference/Gas Technology Symposium, SPE.

Condensate gas reservoirs pose considerable management challenges due to their intricate phase behavior and the critical need for accurate predictions of dew point pressure (PDew). A precise determination of PDew is vital for optimizing production strategies, estimating reserves, and planning enhanced oil recovery operations. Traditional methods for PDew determination often involve experimental analyses that can be both costly and time-consuming, highlighting the necessity for alternative predictive models. This review consolidates recent advancements in methodologies for predicting PDew, encompassing empirical correlations, equation-of-state models, and cutting-edge artificial intelligence techniques such as neural networks and machine learning algorithms. Key studies discussed demonstrate that AI can significantly enhance the accuracy of PDew predictions through the application of genetic programming (GP), artificial neural networks (ANNs), and XGBoost across a variety of reservoir conditions. These advanced computational methods hold promising potential for improving reservoir characterization and management practices in gas condensate fields.

🔒 This is a subscription article

Full text is available to subscribers and institutional members. Please choose an option below to access it.

1. Al-Dhamen, M. and M. Al-Marhoun (2011). New correlations for dew-point pressure for gas condensate. SPE Saudi Arabia section young professionals technical symposium, SPE.
2. Al-Fatlawi, O., et al. (2017). "Determination of best possible correlation for gas compressibility factor to accurately predict the initial gas reserves in gas-hydrocarbon reservoirs." International Journal of Hydrogen Energy 42(40): 25492-25508.
3. Ali, J., et al. (1997). Experimental studies and modelling of gas condensate flow near the Wellbore. SPE Latin America and Caribbean Petroleum Engineering Conference, SPE.
4. AL-Jawad, M. S. and O. F. Hasan (2012). Comprehensive model for flash calculations of heavy oils using the Soave-Redlich-Kwong equation of state. SPE North Africa Technical Conference and Exhibition, SPE.
5. Alzahabi, A., et al. (2017). "A regression model for estimation of dew point pressure from down-hole fluid analyzer data." Journal of Petroleum Exploration and Production Technology 7: 1173-1183.
6. Carlson, M. M. and W. B. Cawston (1996). Obtaining PVT data for very sour retrograde condensate gas and volatile oil reservoirs: a multi-disciplinary approach. SPE Unconventional Resources Conference/Gas Technology Symposium, SPE.
7. Coats, K. H. (1980). "An equation of state compositional model." Society of Petroleum Engineers Journal 20(05): 363-376.
8. Crogh, A. (1996). Improved correlations for retrograde gases, Texas A&M University.
9. Danesh, A. (1998). PVT and phase behaviour of petroleum reservoir fluids, Elsevier.
10. Echenique, E. P. (2016). "Gas Condensate Banking-Application of Pressure Transient Analysis to Determine Production Impairment and Compositional Effects."
11. Economides, M., et al. (1987). Hysteresis effects for gas condensate wells undergoing buildup tests below the dew point pressure. SPE Annual Technical Conference and Exhibition?, SPE.
12. Eilerts, C. K. and R. V. Smith (1942). Specific volumes and phase-boundary properties of separator-gas and liquid-hydrocarbon mixtures, US Department of the Interior, Bureau of Mines.
13. Elsharkawy, A. M. (2002). "Predicting the dew point pressure for gas condensate reservoirs: empirical models and equations of state." Fluid phase equilibria 193(1-2): 147-165.
14. Fang, Y., et al. (1998). Condensate gas phase behavior and development. SPE International Oil and Gas Conference and Exhibition in China, SPE.
15. Fevang, O. (1995). "Gas condensate flow behavior and sampling." Division of Petroleum Engineering and Applied Geophysics.
16. Fevang, Ø. and C. H. Whitson (1996). "Modeling gas-condensate well deliverability." SPE Reservoir Engineering 11(04): 221-230.
17. Ganie, K., et al. (2019). "Gas condensate banking: a review of the phenomenon, accumulation, mitigation methods, and new breakthrough." Science Proceedings Series 1(2): 62-65.
18. Ganie, K., et al. (2019). "A review on the wettability alteration mechanism in condensate banking removal." Journal of Petroleum Science and Engineering 183: 106431.
19. Godwin, O. N. (2012). A new analytical method for predicting dew point pressures for gas condensate reservoirs. SPE Nigeria Annual International Conference and Exhibition, SPE.
20. Gomaa, S. (2018). "Prediction of dew point pressure in gas condensate reservoirs based on a combination of gene expression programming (GEP) and multiple regression analysis."
21. González, A., et al. (2003). "Improved neural-network model predicts dewpoint pressure of retrograde gases." Journal of Petroleum Science and Engineering 37(3-4): 183-194.
22. Haji-Savameri, M., et al. (2020). "Modeling dew point pressure of gas condensate reservoirs: comparison of hybrid soft computing approaches, correlations, and thermodynamic models." Journal of Petroleum Science and Engineering 184: 106558.
23. Humoud, A. and M. Al-Marhoun (2001). A new correlation for gas-condensate dewpoint pressure prediction. SPE Middle East Oil and Gas Show and Conference, SPE.
24. Jalali, F., et al. (2007). Using artificial neural network's capability for estimation of gas condensate reservoir's dew point pressure. EUROPEC/EAGE conference and exhibition, One Petro.
25. Marruffo, I., et al. (2002). Correlations to determine retrograde dew pressure and C7+ percentage of gas condensate reservoirs on basis of production test data of eastern Venezuelan fields. SPE Unconventional Resources Conference/Gas Technology Symposium, SPE.
26. Nemeth, L. K. (1966). A correlation of dew-point pressure with reservoir fluid composition and temperature, Texas A&M University.
27. Nemeth, L. y. and H. Kennedy (1966). A correlation of dew-point pressure with fluid composition and temperature. Journal of Petroleum Technology, SOC PETROLEUM ENG 222 PALISADES CREEK DR, RICHARDSON, TX 75080.
28. Olds, R., et al. (1945). "Volumetric and phase behavior of oil and gas from Paloma Field." Transactions of the AIME 160(01): 77-99.
29. Organick, E. and B. Golding (1952). "Prediction of saturation pressures for condensate-gas and volatile-oil mixtures." Journal of Petroleum Technology 4(05): 135-148.
30. Potsch, K. and L. Braeuer (1996). A novel graphical method for determining dewpoint pressures of gas condensates. SPE Europec featured at EAGE Conference and Exhibition?, SPE.
31. Reamer, H. and B. Sage (1950). "Volumetric behavior of oil and gas from a Louisiana field I." Journal of Petroleum Technology 2(09): 261-268.
32. Sadeq, D. J. (2018). Gas hydrates investigation: flow assurance for gas production and effects on hydrate-bearing sediments, Curtin University.
33. Shokir, E. M. E.-M. (2008). Dewpoint pressure model for gas condensate reservoirs based on genetic programming. SPE Unconventional Resources Conference/Gas Technology Symposium, SPE.