Modeling asphaltene deposition in vertical oil wells

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.

Automated summary

Asphaltene deposition in oil wells is a challenging flow assurance phenomenon. This study aims to evaluate and compare existing asphaltene deposition models and improve the current understanding and prediction of this phenomenon. A large experimental database is collected and a new sticking probability model is proposed, outperforming existing models in predicting asphaltene deposition.