Drilling Conditions Classification Based on Improved Stacking Ensemble Learning

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