Determination of Bottom-Hole Pressure Considering Gas-Liquid Hydrodynamics Based on Wellhead Information

  • Authors

    • Mirza A Dadash-Zade Department of Oil and Gas Engineering, Azerbaijan State Oil and Industry University, Baku, Azerbaijan, AZ1010
    • Ru Cao Department of Oil and Gas Engineering, Azerbaijan State Oil and Industry University, Baku, Azerbaijan, AZ1010
    https://doi.org/10.14419/xj2c5639

    Received date: June 26, 2025

    Accepted date: July 8, 2025

    Published date: July 18, 2025

  • Bottom-Hole Pressure; Flowing Well; Gas-Liquid Mixture; Industrial Application; Mathematical Modeling; Two-Phase Fluid

  • Abstract

    This study presents a novel method for analyzing two-phase fluid flow in pipelines using a mathematical model. By establishing a relationship between mass gas content and spin gas content from experimental data, key parameters of the liquid-gas two-phase system were de‎rived. The method enables the determination of the main parameters of flowing wells and the estimation of bottom-hole pressure. Validation against ‎measured data demonstrates the method's accuracy and practical applicability in industrial settings. The findings contribute to enhancing ‎extraction efficiency and optimizing production processes in oil and gas operations. The proposed method addresses the limitations of exist‎ing models that neglect the coupling effect of spin gas content and mass transfer, which is critical in high-productivity wells. Through systematic wellhead pressure measurements and mathematical models, this study provides a reliable alternative to directly measuring bottom-hole pressure using deep-well pressure gauges, which often present technical challenges and increased measurement errors with increasing well depth. ‎The method is particularly effective in high-productivity wells and efficiently developed reservoirs, offering significant theoretical support ‎for further advancements in petroleum engineering. The results of this research are based on laboratory experiments, theoretical studies, and ‎industrial field observations, ensuring the robustness and applicability of the proposed method in real-world scenarios‎.

  • References

    1. Xu, B., Zhang, X., Wang, Y., Liu, W., & Meng, Z. (2023). Self-adaptive physical information neural network model for prediction of two-phase flow annulus pressure. Acta Petrolei Sinica, 44(3), 545–555.
    2. Li, X., Zhang, H., & Zhou, D. (2019). Experimental study on gas-liquid two-phase flow characteristics in horizontal wellbore. Chemical Engineering Research and Design, 148, 1–10. https://doi.org/10.1016/j.cherd.2019.06.015.
    3. Chen, Y., Li, G., & Zhang, S. (2022). Study on pressure drop characteristics of gas-liquid two-phase flow in inclined wellbore. International Journal of Multiphase Flow, 148, 103931.
    4. Luo, X.-Q., Ge, Z.-G., Feng, J.-J., Zhu, G.-J., Li, C.-H., & He, D.-H. (2024). Experimental study on pressure fluctuation of mixed-flow pump under gas-liquid two-phase flow conditions. Physics of Fluids, 36(2), 025121. https://doi.org/10.1063/5.0123456.
    5. Gao, Y., Sun, H., & Liu, Y. (2020). Investigation of transient two-phase flow behavior in wellbore during gas kick in deepwater drilling. Journal of Natural Gas Science and Engineering, 76, 103211. https://doi.org/10.1016/j.jngse.2020.103211.
    6. Sun, Q., Wang, Z., & Liu, J. (2023). Numerical analysis of gas-liquid two-phase flow in wellbore during managed pressure drilling. Journal of Petroleum Science and Engineering, 219, 110939.
    7. Wang, J., Liu, H., & Zhang, Y. (2018). Analysis of gas-liquid two-phase flow in vertical wellbore during drilling operations. Energy Reports, 4, 603–610. https://doi.org/10.1016/j.egyr.2018.06.002.
    8. Lu, J., Nie, Q., & Yang, E. (2024). A new mathematical model of pressure buildup analysis for a well produced at constant bottomhole pressure. Computational Energy Science, 1(3), 138–149. https://doi.org/10.46690/compes.2024.03.02.
    9. Jahanandish, I. E., Salimifard, B., & Jalalifar, H. (2011). Predicting bottomhole pressure in vertical multiphase flowing wells using artificial neural networks. Journal of Petroleum Science and Engineering, 75, 336–342. https://doi.org/10.1016/j.petrol.2011.01.012.
    10. Osman, E. A., Ayoub, M. A., & Aggour, M. A. (n.d.). Artificial neural network model for predicting bottomhole flowing pressure in vertical multiphase flow [Conference presentation]. In SPE Middle East Oil and Gas Show and Conference (SPE-93632). https://doi.org/10.2118/93632-MS.
    11. Falavand-Jozaei, A., Hajidavalloo, E., Shekari, Y., & Ghobadpouri, S. (2022). Modeling and simulation of non-isothermal three-phase flow for accurate prediction in underbalanced drilling. Petroleum Exploration and Development, 49(2), 358–368. https://doi.org/10.1016/S1876-3804(22)60034-X.
    12. Zhou, D., Zhang, H., & Li, X. (2021). Numerical simulation of gas-liquid two-phase flow in wellbore during underbalanced drilling. Journal of Petroleum Science and Engineering, 196, 107634.
    13. Wang, X., Huang, L., Li, X., Bi, S., Li, H., Zhang, J., & Sun, X. (2022). Multiphase flow behavior of gas kick in deepwater horizontal drilling. Frontiers in Physics, 10, 1049547. https://doi.org/10.3389/fphy.2022.1049547.

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  • How to Cite

    Dadash-Zade, M. A., & Cao, R. (2025). Determination of Bottom-Hole Pressure Considering Gas-Liquid Hydrodynamics Based on Wellhead Information. International Journal of Basic and Applied Sciences, 14(3), 132-142. https://doi.org/10.14419/xj2c5639