Domain adaptation neural network based prediction of real-time drill bit tooth wear

Abstract

Drill bit wear reduces drilling efficiency and escalates operational risks. The drill bit tooth wear grading is a measure of lost tooth height. Accurate monitoring of drill bit tooth wear grade is crucial to adjust drilling parameters and plan timely bit replacement, significantly enhancing drilling efficiency and safety. Current real-time monitoring methods for drill bit wear exhibit limitations, particularly in accounting for variations in the geological formation and drilling parameters, resulting in poor model stability. This paper proposes an intelligent modeling approach that utilizes a Domain Adaptation Neural Network (DANN) to establish an ideal drilling rate of penetration (ROP) prediction model. By incorporating measured ROP, wear coefficients are calculated, and a relationship between wear coefficient and tooth wear grade is established, facilitating real-time monitoring of drill bit tooth wear grade. Test results on ten drill bits demonstrate that the DANN ideal ROP prediction model, leveraging data from new drill bits, enhances the model's adaptability to varying working conditions for new drill bits, thereby improving the accuracy of ideal ROP predictions. We establish relationship between calculated wear coefficients and tooth wear grades. The results indicate that the average error in inner wear grade is 0.6 (7.5%) and the average error in outer wear grade is 1.3 (16%) on an 8-point scale which demonstrates the model's effectiveness in monitoring drill bit tooth wear.