Drilling Conditions Classification Based on Improved Stacking Ensemble Learning

The classification of drilling conditions is a crucial task in the drilling process, playing a vital role in improving drilling efficiency and reducing costs. In this study, we propose an improved stacking ensemble learning algorithm with the objective of enhancing the performance of drilling conditions classification. Additionally, this algorithm aims to have a positive impact on automated drilling time estimation and the continuous improvement of efficiency. In our experimental setup, we employed various base learners, such as random forests, support vector machine, and the K-nearest neighbors algorithm, as initial models for the task of drilling conditions classification. To improve the model's expressive power and feature relevance specifically for this task, we enhanced the meta-model component of the stacking algorithm by incorporating feature engineering techniques. The experimental results show that the improved ensemble learning algorithm achieves an accuracy and recall rate of 97% and 98%, respectively. Through continuous improvement in drilling operations, the average sliding time is reduced by 21.1%, and the average Rate of Penetration (ROP) is increased by 15.65%. This research holds significant importance for engineering practice in the drilling industry, providing robust support for optimizing and enhancing the drilling process.