A Novel Prediction Model for Steam Temperature Field of Downhole Multi-Thermal Fluid Generator

被引:1
|
作者
He, Yanfeng [1 ,2 ]
Huang, Zhiqiang [1 ,2 ]
Dou, Xiangji [1 ,2 ]
Zhang, Yisong [1 ]
Hua, Le [1 ]
Guo, Jing [1 ]
机构
[1] Changzhou Univ, Sch Petr & Nat Gas Engn, Changzhou 213164, Peoples R China
[2] CNPC CZU Innovat Alliance, Changzhou 213164, Peoples R China
关键词
multiple-thermal fluid; heat loss; temperature field; annular space; downhole multi-thermal fluid generator; ARTIFICIAL-INTELLIGENCE; WELLBORE; PARAMETERS;
D O I
10.3390/pr12030485
中图分类号
TQ [化学工业];
学科分类号
0817 ;
摘要
Aiming at the low efficiency of heavy-oil thermal recovery, a downhole multi-thermal fluid generator (DMTFG) can improve the viscosity reduction effect by reducing the heat loss of multi-thermal fluid in the process of wellbore transportation. The steam generated by the MDTFG causes damage to the packer and casing, owing to the return upwards along the annular space passage of the oil casing. To mitigate this damage, a heat transfer model for multi-channel coiled tubing wells and a prediction model for the upward return of the steam temperature field in the annulus were established with the basic laws of thermodynamics. Models were further verified by ANSYS. The results indicate the following four conclusions. First of all, when the surface pressure is constant, the deeper the located DMTFG, the shorter the distance for the steam to return would be. It is easier to liquefy the steam. Second, the higher the temperature of the steam produced by the downhole polythermal fluid generator, the larger the upward distance of the steam would be. Third, the higher the steam pressure at the outlet of the downhole polythermal fluid generator, the smaller the distance of steam upward return would be. Finally, the larger the diameter of the multi-channel conversion piping, the greater the distance of the steam return would be. It is meaningful to provide valuable theoretical guidance for packer position designing in the field. Meanwhile, the study also provides a modeling basis for the subsequent study of artificial intelligence in the downhole temperature field.
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页数:19
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