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A Comparison of Thermal Models for Temperature Profiles in Gas-Lift Wells

Author

Listed:
  • Langfeng Mu

    (Chinese Academy of Geological Sciences, No. 26 Baiwanzhuang Street, Beijing 100037, China)

  • Qiushi Zhang

    (Petroleum Systems Engineering, University of Regina, Regina, SK S4S 0A2, Canada)

  • Qi Li

    (Daqing Oilfield Exploration and Development Institute, Daqing 163712, China)

  • Fanhua Zeng

    (Petroleum Systems Engineering, University of Regina, Regina, SK S4S 0A2, Canada)

Abstract

Gas lift is a simple, reliable artificial lift method which is frequently used in offshore oil field developments. In order to enhance the efficiency of production by gas lift, it is vital to exactly predict the distribution of temperature-field for fluid within the wellbore. A new mechanistic model is developed for computing flowing fluid temperature profiles in both conduits simultaneously for a continuous-flow gas-lift operation. This model assumes steady heat transfer in the formation, as well as steady heat transfer in the conduits. A micro-units discrete from the wellbore, whose heat transfer process is analyzed and whose heat transfer equation is set up according to the law of conservation of energy. A simplified algebraic solution to our model is conducted to analyze the temperature profile. Sensitivity analysis was conducted with the new model. The results indicate that mass flow rate of oil and the tubing overall heat transfer coefficient are the main factors that influence the temperature distribution inside the tubing and that the mass flow rate of oil is the main factor affecting temperature distribution in the annulus. Finally, the new model was tested in three various wells and compared with other models. The results showed that the new model is more accurate and provides significant references for temperature prediction in gas lift well.

Suggested Citation

  • Langfeng Mu & Qiushi Zhang & Qi Li & Fanhua Zeng, 2018. "A Comparison of Thermal Models for Temperature Profiles in Gas-Lift Wells," Energies, MDPI, vol. 11(3), pages 1-18, February.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:3:p:489-:d:133455
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    References listed on IDEAS

    as
    1. Cheng, Wen-Long & Nian, Yong-Le & Li, Tong-Tong & Wang, Chang-Long, 2014. "A novel method for predicting spatial distribution of thermal properties and oil saturation of steam injection well from temperature logs," Energy, Elsevier, vol. 66(C), pages 898-906.
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