Characterizing methane hydrate formation in the non-Newtonian fluid flowing system

Developing natural gas hydrates in deep water faces serious well control problem and flow assurance problem, induced by the reformation of gas hydrate in the drilling fluid. In order to solve this problem, developing a hydrate formation predicting model becomes necessary. In this work, the methane hydrate formation experiments are performed in the xanthan gum (XG) aqueous solution under flow velocities from 1.8 to 1.5 m/s, XG concentrations from 0.1 to 0.3% and void fraction of 4.5%. The hydrate formation rates decrease with the XG concentration increasing and increases with the flow velocity increasing. The hydrate formation rate at the moment of the experiment onset will decrease sharply due to the formation of hydrate shell on gas bubbles and gas hydrates will form at the almost constant rate, because the second growth of hydrate shells on the fractures of gas bubbles and collisions between gas bubbles enhances the hydrate formation rates. The hydrate formation rate increases rapidly when the hydrate formation process is near the end of hydrate formation since the breakage rates of gas bubbles increase the hydrate formation rates. A mass transfer model is developed to describe the methane hydrate formation under the non-Newtonian fluid flowing condition. Since the volumetric mass transfer coefficient closely depends on the rheological properties of carrying fluid, the rheological experiments for the XG aqueous solution are conducted and an empirical rheology model is developed correspondingly. An integrated constant is proposed to improve the accuracy of the model which reflects influences of the hydrate shell formation, the second growth of hydrate shell and the bubble breakage on methane hydrate formation. The correlations of the integrated constant are functions of the XG concentration, the flow velocity and the subcooling temperature. Through validation, the proposed mass transfer model shows good agreements with experimental data and the maximum discrepancy is 11.64%.