Liquid Film Model for Prediction and Identification of Liquid Loading in Vertical Gas Wells

2020 ◽  
Author(s):  
Arnold Landjobo Pagou ◽  
Xiaodong Wu
2020 ◽  
Vol 188 ◽  
pp. 106896
Author(s):  
Arnold Landjobo Pagou ◽  
Xiaodong Wu ◽  
Zhiying Zhu ◽  
Long Peng

Processes ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 923
Author(s):  
Wenqi Ke ◽  
Lintong Hou ◽  
Lisong Wang ◽  
Jun Niu ◽  
Jingyu Xu

Liquid loading in gas wells may slash production rates, shorten production life, or even stop production. In order to reveal the mechanism of liquid loading in gas wells and predict its critical flowrates, theoretical research and laboratory experiments were conducted in this work. A new model of liquid-film reversal was established based on Newton’s law of internal friction and gas–liquid two-phase force balance, with the critical reverse point obtained using the minimum gas–liquid interface shear force method. In this model, the influences of the pipe angle on the liquid film thickness were considered, and the friction coefficient of the gas–liquid interface was refined based on the experimental data. The results showed that the interfacial shear force increases by increasing the liquid superficial velocity, which leads first to an increase of the critical liquid-carrying gas velocity and then to a decrease, and the critical production also decreases. With 0° as the vertical position of the pipeline and an increase of the inclination angle, the critical liquid-carrying velocity first increases and then decreases, and the maximum liquid-carrying velocity appears in the range of 30–40°. In addition, the critical liquid-carrying gas velocity is positively correlated with the pipe diameter. Compared with the previous model, the model in this work performed better considering its prediction discrepancy with experiment data was less than 10%, which shows that the model can be used to calculate the critical liquid-carrying flow rate of gas wells. The outcome of this work provides better understanding of the liquid-loading mechanism. Furthermore, the prediction model proposed can provide guidance in field design to prevent liquid loading.


Author(s):  
Hiroshi Kanno ◽  
Youngbae Han ◽  
Yusuke Saito ◽  
Naoki Shikazono

Heat transfer in micro scale two-phase flow attracts large attention since it can achieve large heat transfer area per density. At high quality, annular flow becomes one of the major flow regimes in micro two-phase flow. Heat is transferred by evaporation or condensation of the liquid film, which are the dominant mechanisms of micro scale heat transfer. Therefore, liquid film thickness is one of the most important parameters in modeling the phenomena. In macro tubes, large numbers of researches have been conducted to investigate the liquid film thickness. However, in micro tubes, quantitative information for the annular liquid film thickness is still limited. In the present study, annular liquid film thickness is measured using a confocal method, which is used in the previous study [1, 2]. Glass tubes with inner diameters of 0.3, 0.5 and 1.0 mm are used. Degassed water and FC40 are used as working fluids, and the total mass flux is varied from G = 100 to 500 kg/m2s. Liquid film thickness is measured by laser confocal displacement meter (LCDM), and the liquid-gas interface profile is observed by a high-speed camera. Mean liquid film thickness is then plotted against quality for different flow rates and tube diameters. Mean thickness data is compared with the smooth annular film model of Revellin et al. [3]. Annular film model predictions overestimated the experimental values especially at low quality. It is considered that this overestimation is attributed to the disturbances caused by the interface ripples.


2021 ◽  
Vol 26 (3) ◽  
pp. 245
Author(s):  
Chuan Xie ◽  
Chunyu Xie ◽  
Yulong Zhao ◽  
Liehui Zhang ◽  
Yonghui Liu ◽  
...  

2009 ◽  
Vol 35 (5) ◽  
pp. 417-424 ◽  
Author(s):  
Toru Ishigami ◽  
Tsuyoshi Kameda ◽  
Hiroshi Suzuki ◽  
Yoshiyuki Komoda

2018 ◽  
Vol 60 ◽  
pp. 153-163 ◽  
Author(s):  
Zhennan Zhang ◽  
Baojiang Sun ◽  
Zhiyuan Wang ◽  
Yonghai Gao ◽  
Shujie Liu ◽  
...  

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