段塞流
沉积(地质)
腐蚀
流量(数学)
岩土工程
多相流
颗粒沉积
机械
两相流
滑脱
材料科学
粒子(生态学)
工作(物理)
地质学
管道流量
工程类
湍流
沉积物
复合材料
地貌学
物理
机械工程
海洋学
作者
Ronald E. Vieira,Farzin Darihaki,Siamack A. Shirazi
出处
期刊:Wear
[Elsevier BV]
日期:2023-03-21
卷期号:523: 204761-204761
被引量:4
标识
DOI:10.1016/j.wear.2023.204761
摘要
Horizontal wells play an important role in maximizing the recovery of hydrocarbons from oil and gas reservoirs. During production, if the rate of production of produced fluids with sand particles is low, the deposition of sand particles in horizontal flow lines can wreak havoc on production systems in such a way as to cause increased pressure loss and reduction or loss of production when the pipe is partially or completely blocked. On the other hand, if the production rate of fluids is high, the impact of sand particles on the wall of the pipe will erode the inner surface of the pipe fittings. In this case, erosion can be severe enough to cause a hazardous spill and complete shutdown of systems. Therefore, it is important to quantify the sand transport and erosivity of different flow regimes. Under moderate velocities, there exists a flow regime which is called slug flow which is characterized by the intermittent appearance of liquid slugs propagating through the pipe. Studying the sand deposition and erosion due to sand particles entrained in intermittent flows is extremely difficult since the solid particle velocity magnitudes are coupled with several multiphase flow parameters such as phase distribution and slippage between the phases. This work focuses on the improvement of mechanistic models for predicting critical deposition of sand particles in horizontal pipes and erosion in horizontal-horizontal elbows. In this investigation, a modified model is proposed to better predict the critical deposition velocity and the characteristic particle velocity as a function of the slug flow characteristics. The model predictions are compared with the sand transport and erosion data collected at the University of Tulsa and other research institutions. The results show that the new approach provides improved predictions for sand transport and maximum erosion in standard elbows when compared with existing models.
科研通智能强力驱动
Strongly Powered by AbleSci AI