A Novel Prediction Model of the Drag Coefficient of Irregular Particles in Power-Law Fluids

被引:1
|
作者
Hou, Zhaokai [1 ]
Jiang, Enyuan [2 ]
Chen, Ye [3 ]
Wang, Huaishan [1 ]
Feng, Jinyu [1 ]
Tao, Xutao [1 ]
机构
[1] Northeast Petr Univ, Sanya Offshore Oil & Gas Res Inst, Sanya 572025, Peoples R China
[2] China Natl Oil & Gas Explorat & Dev Co Ltd, Dev Dept, Beijing 100034, Peoples R China
[3] PetroChina Southwest Oil & Gasfield Co, Engn Technol Res Inst, Chengdu 610017, Peoples R China
关键词
power-law fluids; circularity; Reynolds number; irregular particle; drag coefficient; SETTLING VELOCITY; CORRELATION FORMULA; BEHAVIOR; SPHERE; RESISTANCE; MOTION;
D O I
10.3390/pr11113213
中图分类号
TQ [化学工业];
学科分类号
0817 ;
摘要
The settlement drag coefficient of irregular particles in power-law fluids is a crucial parameter in the field of petroleum engineering. However, the irregular shape of the particle and the non-Newtonian rheological properties of the fluid make it challenging to predict the settlement drag coefficient. In this study, the spherical and irregular particle sedimentation processes in power-law fluids have been analyzed using a visual device and a high-speed camera system. A mechanical model dependent on the force balance of settlement particles was adopted to conduct a detailed statistical analysis of 114 spherical particle experimental results, and a prediction model of the drag coefficient of spherical particles in the power-law fluid was established with a mean relative error of 3.85%. On this basis, considering the influence of geometric shape on the law of particle sedimentation, a new irregular particle sedimentation resistance coefficient model in power-law fluid is established via the incorporation of the parameter circularity of 2D shape description c into the spherical particle sedimentation resistance coefficient predictive model. The parameters in the new irregular particle sedimentation resistance coefficient predictive model can be obtained via nonlinear data fitting of the 211 groups of irregular particles using experimental results in the power-law fluid. The model has high prediction accuracy for the drag coefficient of irregular particles in power-law fluid, with a mean relative error of 4.47, and expands the scope of engineering applications, which is of great significance for fracturing scheme design and wellbore cleaning.
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页数:14
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