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A prediction model of critical liquid-carrying velocity of gas well considering the mechanism of liquid film shear and droplet entrainment

夹带(生物音乐学) 机械 机制(生物学) 材料科学 剪切(地质) 热力学 物理 声学 量子力学 节奏 复合材料
作者
Cheng Fu,Xiaohui Li,Meng Cai,Wei Zhang,Bin Huang
出处
期刊:Case Studies in Thermal Engineering [Elsevier BV]
卷期号:73: 106555-106555 被引量:4
标识
DOI:10.1016/j.csite.2025.106555
摘要

Extensive field data indicates that the dominant flow pattern in gas wells is typically annular mist flow, with the liquid phase predominantly existing as liquid film and liquid droplets in water-producing gas wells. In actual water-producing gas wells, the liquid phase exists simultaneously in the forms of liquid films and droplets. The liquid film moves along the inner wall of the wellbore, while the droplets are carried by the gas phase in the form of mist. This results in both the droplet model and the liquid film model being unable to accurately predict the critical liquid carrying velocity. Therefore, in this study, a new critical liquid carrying velocity model is proposed, which builds upon the mechanisms of liquid film shear and droplet entrainment. This model integrates the process by which liquid films are fragmented into droplets through wave-induced shear. Then, the proposed model is compared with classical critical liquid carrying models and filed experimental data from 50 gas wells with different pressure and tubing size, The results demonstrate that the proposed model significantly enhances the accuracy of liquid loading predictions, achieving an overall accuracy of 98 %, with one misjudged gas well. Finally, the impacts of liquid production, liquid phase composition, tubing diameter, pressure gradient, and temperature on the critical liquid carrying velocity are investigated using the proposed model. The results demonstrate that the critical liquid carrying velocity increases with an increase in pipe diameter, liquid production, and pressure gradient, with the pipe diameter exerting a greater influence than liquid production and pressure gradient.
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