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Evaluating the Effect of New Gas Solubility and Bubble Point Pressure Models on PVT Parameters and Optimizing Injected Gas Rate in Gas-Lift Dual Gradient Drilling

Author

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  • Naser Golsanami

    (State Key Laboratory of Mining Disaster Prevention and Control, Shandong University of Science and Technology, Qingdao 266590, China
    College of Energy and Mining Engineering, Shandong University of Science and Technology, Qingdao 266590, China)

  • Bin Gong

    (College of Energy and Mining Engineering, Shandong University of Science and Technology, Qingdao 266590, China)

  • Sajjad Negahban

    (Faculty of Mining, Petroleum and Geophysics Engineering, Shahrood University of Technology, Shahrood 3619995161, Iran)

Abstract

Gas-lift dual gradient drilling (DGD) is a solution for the complex problems caused by narrow drilling windows in deepwater drilling. Investigations are lacking on using oil-based drilling fluid in DGD, which is the principal novel idea of the present study. This research compares the results obtained from two new models with those of Standing’s correlations for solubility and bubble point pressure. Nitrogen was selected as the injection gas, then the PVT behavior of drilling fluid (oil/water/Nitrogen) in gas-lift DGD was evaluated and compared by coding in MATLAB. Then, these results were used to calculate the bottom hole pressure and finally investigate the optimization of injected gas flow rate. According to the achieved results, the Standing model has some errors in evaluating the PVT behavior of “Nitrogen and oil-based drilling fluids” and is not recommended for the mixtures in the gas-lift DGD. Regarding optimizing gas flow rate, a discrepancy was observed between pressure values obtained from the new models and the Standing model for the case of high liquid flow rates at low gas flow rates because of the difference in PVT parameters. The developed codes are deposited on an online data repository for future users. This study lays the foundation for better planning of drilling in deepwater drilling projects.

Suggested Citation

  • Naser Golsanami & Bin Gong & Sajjad Negahban, 2022. "Evaluating the Effect of New Gas Solubility and Bubble Point Pressure Models on PVT Parameters and Optimizing Injected Gas Rate in Gas-Lift Dual Gradient Drilling," Energies, MDPI, vol. 15(3), pages 1-25, February.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:3:p:1212-:d:743768
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    References listed on IDEAS

    as
    1. Golsanami, Naser & Jayasuriya, Madusanka N. & Yan, Weichao & Fernando, Shanilka G. & Liu, Xuefeng & Cui, Likai & Zhang, Xuepeng & Yasin, Qamar & Dong, Huaimin & Dong, Xu, 2022. "Characterizing clay textures and their impact on the reservoir using deep learning and Lattice-Boltzmann simulation applied to SEM images," Energy, Elsevier, vol. 240(C).
    2. Xuexian Gao & Haidong Zheng & Yan Zhang & Naser Golsanami, 2019. "Tax Policy, Environmental Concern and Level of Emission Reduction," Sustainability, MDPI, vol. 11(4), pages 1-17, February.
    3. Naser Golsanami & Xuepeng Zhang & Weichao Yan & Linjun Yu & Huaimin Dong & Xu Dong & Likai Cui & Madusanka Nirosh Jayasuriya & Shanilka Gimhan Fernando & Ehsan Barzgar, 2021. "NMR-Based Study of the Pore Types’ Contribution to the Elastic Response of the Reservoir Rock," Energies, MDPI, vol. 14(5), pages 1-26, March.
    4. Weichao Yan & Fujing Sun & Jianmeng Sun & Naser Golsanami, 2021. "Distribution Model of Fluid Components and Quantitative Calculation of Movable Oil in Inter-Salt Shale Using 2D NMR," Energies, MDPI, vol. 14(9), pages 1-17, April.
    5. Pengfei Shen & Gang Li & Jiangfeng Liu & Xiaosen Li & Jinming Zhang, 2019. "Gas Permeability and Production Potential of Marine Hydrate Deposits in South China Sea," Energies, MDPI, vol. 12(21), pages 1-20, October.
    6. Zhang Qiang & Qamar Yasin & Naser Golsanami & Qizhen Du, 2020. "Prediction of Reservoir Quality from Log-Core and Seismic Inversion Analysis with an Artificial Neural Network: A Case Study from the Sawan Gas Field, Pakistan," Energies, MDPI, vol. 13(2), pages 1-19, January.
    7. Saad Alatefi & Abdullah M. Almeshal, 2021. "A New Model for Estimation of Bubble Point Pressure Using a Bayesian Optimized Least Square Gradient Boosting Ensemble," Energies, MDPI, vol. 14(9), pages 1-21, May.
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