Modeling asphaltene deposition in vertical oil wells

被引:2
|
作者
Al-Safran, Eissa [1 ,2 ]
Al -Ali, Batoul [1 ]
机构
[1] Kuwait Univ, Petr Engn, Kuwait, Kuwait
[2] Kuwait Univ, Coll Engn & Petr, Petr Engn, Kuwait, Kuwait
来源
GEOENERGY SCIENCE AND ENGINEERING | 2023年 / 221卷
关键词
Asphaltene deposition; Flow assurance; Modeling; Particle Transport coefficient; PARTICLE DEPOSITION; PREDICTION; FLOW;
D O I
10.1016/j.petrol.2022.111277
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
Asphaltene deposition in oil wells is a challenging flow assurance phenomenon that affects well production, project economics, and operational safety. While asphaltene precipitation is governed by hydrocarbon mixture thermodynamics, asphaltene deposition is governed by complex hydrodynamic behavior and characteristics. This study aims to evaluate and compare the performance of existing asphaltene deposition models based on transport mechanisms, and to improve the current theoretical understanding and prediction of asphaltene deposition phenomenon in single-phase liquid upward vertical flow. A large experimental database is collected from open literature, which falls into two categories, namely aerosol (air/metal particles), and crude oil system (oil/asphaltene particles). The aerosol transport coefficient database is divided into diffusion, diffusion-inertia, and impaction flow mechanisms, which are used to evaluate the transport coefficient models in each region. A statistical error analysis revealed that Beal (1970), Kor and Kharrat (2016), and Friedlander and Johnstone (1957) are the most accurate models in predicting transport coefficient in diffusion, diffusion-inertia, and impaction regions, respectively. Furthermore, a simplified asphaltene sticking probability model is proposed in this study, which is curve fitted using crude oil system asphaltene deposition flux data. The proposed sticking probability model is not only physically sound, but also requires fewer input data than existing models. A validation study of the proposed model slightly over-predicted the experimental data with an absolute average error of 9.8% and standard deviation of 21.8% outperforming existing model. The significance of this work is to improve current theoretical understanding, and propose predictive asphaltene deposition model in pipes that can be incorporated in an integrated asphaltene deposition model to prevent, mitigate, and manage oil field asphaltene deposition.
引用
收藏
页数:13
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