Research on the influencing factors and prediction model of sulfur particle aggregation diameter in high-sulfur gas wells

Shuqiang Shi , Xin Wang , Xiuli Gao , Qiang Luo , Haozhuang Zhang , Shilin Huang , Yaning Wang , Yongcai Zhang , Qingyin Yu , Danlin Chen , Shaokang Lin

Petroleum ›› 2026, Vol. 12 ›› Issue (4) : 711 -741.

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Petroleum ›› 2026, Vol. 12 ›› Issue (4) :711 -741. DOI: 10.1016/j.petlm.2026.04.008
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Research on the influencing factors and prediction model of sulfur particle aggregation diameter in high-sulfur gas wells
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Abstract

The size of sulfur particle aggregates directly influences the critical sulfur particle carrying velocity, flow resistance, sulfur blockage thickness, corrosion rate, and scaling rate in high-sulfur gas wells. This can significantly impact the normal production of the gas wells. Although considerable research has been conducted on the mechanisms and behaviors of particle aggregation, studies specifically addressing the diameter of sulfur particle aggregates remain limited. In this paper, utilizes numerical simulation and multiphase pipe flow experiments to investigate the gas-particle sulfur two-phase pipe flow. It focuses on the effects of gas velocity, particle flow velocity, initial particle diameter, and initial particle concentration on the size of sulfur particle aggregates. The results indicate that the maximum diameter of sulfur particle aggregates diminishes as gas velocity and particle flow velocity increase, whereas it escalates with larger initial particle diameters and higher particle concentrations. A predictive model for the diameter of sulfur particle aggregates has been established based on the principles of mechanical equilibrium, incorporating factors such as particle collision frequency, aggregation probability, and the influences of van der Waals forces and liquid bridge forces between particles. This model has been validated with experimental data (six sets), numerical simulations (twenty sets), and relevant literature (ten sources), demonstrating high concordance between predicted and measured diameters, with an average absolute error of 6.35%. By predicting sulfur particle aggregate diameters in the wellbore, the model aids in optimizing gas well production, enhancing flow efficiency, and reducing wellbore blockages, providing a strong theoretical basis for optimizing multiphase flow.

Keywords

High-sulfur gas well / Particle aggregation model / Sulfur particles / Particle aggregate diameter

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Shuqiang Shi, Xin Wang, Xiuli Gao, Qiang Luo, Haozhuang Zhang, Shilin Huang, Yaning Wang, Yongcai Zhang, Qingyin Yu, Danlin Chen, Shaokang Lin. Research on the influencing factors and prediction model of sulfur particle aggregation diameter in high-sulfur gas wells. Petroleum, 2026, 12 (4) : 711-741 DOI:10.1016/j.petlm.2026.04.008

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