LAUSR

To avoid the considerable challenges and losses caused by stuck drilling to normal drilling operations, this article analyses the mechanism of stuck drilling, then combines the artificial fish swarm algorithm (AFSA) and support vector machine (SVM), and finally proposes an early warning model for the stuck-in medical drilling process based on the AFSA and SVM. The model realizes real-time sticking risk warnings by using the four parameters of riser pressure, torque, speed and hook load collected in real time and promotes real-time drilling parameter monitoring for the real-time dynamic warning of sticking risk. By comparing the AFSA-SVM sticking prediction model with the particle swarm optimization model and the traditional cross-validation optimization model, it is found that the AFSA-SVM stuck prediction model accuracy can reach 97.561% and that the training and testing times are 5.874 s and 0.76 s, respectively. Its accuracy and computational efficiency are higher than those of the particle swarm optimization model and traditional cross-validation optimization model. In comparison with the existing technology, the four-parameter early sticking warning model based on AFSA-SVM presented in this paper shows powerful comprehensive performance and field application value.