Modeling and Optimization of Mechanical Cutting of Downhole Tubing

Abstract

Summary Mechanical cutting of tubing plays a vital role in solving the problem of pipe string jams in workover operations of oil wells. To improve the efficiency of downhole cutting operations and save operation costs, it is necessary to optimize the parameters of downhole-cutting operations. However, previous research did not involve related engineering problems. Therefore, in this paper, the equivalent simulation experiment of downhole cutting is conducted based on actual field data. Cutting speed, feed rate, and cutting thickness are used as parameters while cutting power (P), material removal rate (MRR), and tool chip temperature (T) are used as optimization objectives with the trade-offs between the three objectives considered. The full factorial design is used to carry out the experiments and the combination of grey relational analysis (GRA) method and entropy weight method is used to determine the weight of the three objectives. The influence of cutting parameters on the optimization objectives is analyzed, the mathematical model between cutting parameters and a single objective is established, and the adaptive weight particle swarm algorithm is used to optimize the coefficients of this model. The relationship between the multiobjective model and cutting parameters is established using a multiple nonlinear regression model, and the selection of interaction terms is completed using a stepwise regression method. The reliability of the model is also verified. This paper provides a reference for future research on downhole-cutting problems.