Volume 16, Issue 2, July 2015, Pages 284–291
Khaled Saeed Ba-Jaalah1 and Abdel Waly Abd Allah Abdel Waly2
1 Department of Mining, Petroleum and Metallurgy Engineering, Cairo University, Giza, Egypt
2 Department of Mining, Petroleum and Metallurgy Engineering, Cairo University, Giza, Egypt
Original language: English
Copyright © 2015 ISSR Journals. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Predicting the relationship between the flow rate and the pressure drop performance in the reservoir is very important for continuous production optimization in the field. The inflow performance relationship (IPR) describes the relationship between the flow rate of the well (q) and the following pressure of that well (Pwf). Different inflow performance relationship correlations exist today in the petroleum industry with the most commonly used models are that of Vogel and Fetkovich. Gas condensate reservoirs are primarily gas reservoir but when reservoir pressure declines below dew point pressure the liquid begins produced. The goal of this work is to develop a new model to predict the inflow performance relationship curve for gas condensate reservoirs. This new correlation was developed using about 200 data points were collected from different Middle East gas condensate reservoirs. The development model was tested by comparing its accuracy with that of the most common inflow performance relationship models such as Vogel, Fetkovich and Wiggins models. The results of this comparison showed that the new developed model gave the best accuracy with an average absolute error of 11.38% while the other common model, Vogel, Fetkovich and Wiggins, gave an average absolute error of 69.39%, 22.65% and 45.75% respectively.
Author Keywords: IPR, model, gas condensate reservoir, relationship.
Khaled Saeed Ba-Jaalah1 and Abdel Waly Abd Allah Abdel Waly2
1 Department of Mining, Petroleum and Metallurgy Engineering, Cairo University, Giza, Egypt
2 Department of Mining, Petroleum and Metallurgy Engineering, Cairo University, Giza, Egypt
Original language: English
Copyright © 2015 ISSR Journals. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract
Predicting the relationship between the flow rate and the pressure drop performance in the reservoir is very important for continuous production optimization in the field. The inflow performance relationship (IPR) describes the relationship between the flow rate of the well (q) and the following pressure of that well (Pwf). Different inflow performance relationship correlations exist today in the petroleum industry with the most commonly used models are that of Vogel and Fetkovich. Gas condensate reservoirs are primarily gas reservoir but when reservoir pressure declines below dew point pressure the liquid begins produced. The goal of this work is to develop a new model to predict the inflow performance relationship curve for gas condensate reservoirs. This new correlation was developed using about 200 data points were collected from different Middle East gas condensate reservoirs. The development model was tested by comparing its accuracy with that of the most common inflow performance relationship models such as Vogel, Fetkovich and Wiggins models. The results of this comparison showed that the new developed model gave the best accuracy with an average absolute error of 11.38% while the other common model, Vogel, Fetkovich and Wiggins, gave an average absolute error of 69.39%, 22.65% and 45.75% respectively.
Author Keywords: IPR, model, gas condensate reservoir, relationship.
How to Cite this Article
Khaled Saeed Ba-Jaalah and Abdel Waly Abd Allah Abdel Waly, “New Inflow Performance Relationship for Gas Condensate Reservoirs,” International Journal of Innovation and Scientific Research, vol. 16, no. 2, pp. 284–291, July 2015.
