Understanding the hydrate deposition law can provide ideas for the optimization of intervention operation plans and the prevention and control of hydrate in the wellbore of deepwater gas wells. On the basis of the gas?liquid two?phase flow model, a wellbore pressure and temperature prediction model was developed based on the heat exchange and frictional gradient changes caused by the lowering of the intervention tool, and the temperature and pressure models were coupled to solve by the iterative method. Based on the hydrate growth kinetic model, combined with the wellbore temperature and pressure prediction results, a hydrate deposition model was established, and the hydrate deposition law in the wellbore under intervention operations was analyzed. The results showed that the increase of production led to the increase of pressure difference in the wellbore and the higher wellbore temperature at the mudline under high production. With the release of the intervention tool, the pressure in the wellbore increases, but the pressure increase gradually decreases, and the maximum pressure increase at the wellhead is about 3.0 MPa. When the proportion of the intervention tool diameter is less than 50%, the larger the tool diameter, the higher the wellbore pressure. The location of the wellbore mudline is a high?risk area for hydrate deposition plugging, and the rate of hydrate deposition in low?producing wells is faster than that in high?producing wells. The wellbore hydrate deposition rate is the fastest when the intervention tool is placed near the mudline, and the wellbore hydrate deposition rate is faster when the intervention tool diameter accounts for 50%.

In this work, the inner and outer loop control strategy is adopted for the unmanned aerial vehicles (UAVs) formation system. Firstly, a sliding mode control method based on memory event?triggered mechanism (METM) is proposed for the position subsystem under delay and disturbance. The second?order model is established for each UAV, and the leader?follower framework is adopted to realize the desired flight formation. Secondly, an adaptive METM is proposed to alleviate the transmission burden, in which control input feedback is introduced. For the resulting communication delay of the control input in the proposed METM and the external disturbance to the system, a sliding mode controller is designed, which maintains desirable control performance and solves the influence of communication delay and bounded disturbance to a certain extent. Thirdly, the stability of the closed?loop system is proved by applying Lyapunov theory and H_∞ control theory, and a method that facilitates solving controller gain and triggering parameter through a series of linear matrix inequalities (LMIs) is proposed. Moreover, based on the obtained virtual control quantity, a tracking controller is designed for the attitude subsystem. Finally, a simulated example is exploited to verify the effectiveness of the proposed method.