Kick is one of the most important drilling problems, and because its occurrence makes drilling engineering extremely complex, it is essential to predict the possibility of kick as soon as possible. In this study, k-means clustering was combined with four artificial neural networks: regularized RBFNN, generalized RBFNN, GRNN, and PNN, to estimate the kick risk. To reduce data redundancy and normalize the drilling data, which contain kick conditions, k-means clustering was introduced. The output layer weights were then determined using a brute-force search with different Gaussian function widths, resulting in a series of artificial neural networks composed of different clustering samples and different Gaussian function widths. The results showed that the prediction accuracy of regularized RBFNN + k-means model was the highest, that of the GRNN + k-means model was the lowest. The kick prediction accuracy for regularized RBFNN, generalized RBFNN, GRNN, and PNN were 75.90%, 65.20%, 51.70%, and 70.16%, respectively. This method can be used to enhance the speed and accuracy of kick risk prediction in the field while facilitating the use of and advances in risk warning technology for deep and high-temperature and high-pressure wells.

During perforation in ultra-deep wells, the blast shock wave can induce dynamic responses of the perforating tubing, leading to potential downhole accidents such as vibration, deformation, and even fracture of the perforating tubing. To comprehend the dynamic response characteristics of the perforating tubing under blast impact load, we conducted a joint finite element simulation using SolidWorks, Hypermesh, and LS-DYNA. The simulation included deformation analysis, motion analysis, and strength analysis of the perforating tubing. By analyzing these factors, we obtained the change in velocity, acceleration, and equivalent stress of the perforating tubing over time under the blast load. The finite element analysis indicates the following: (a) the bottom of the perforating tubing is susceptible to significant tension compression cycle; (b) the velocity amplitude variation is smallest at the top of the perforating tubing, while the frequency and peak values of velocity changes are maximal at the bottom of the perforating tubing; and (c) the top and bottom of the tubing string are the vulnerable parts of the perforating tubing system.