Kick Risk Diagnosis Method Based on Ensemble Learning Models

As oil and gas exploration and development gradually advance into deeper and offshore fields, the geological environment and formation pressure conditions become increasingly complex, leading to a higher risk of drilling incidents such as kicks. Timely diagnosis of kick risk is crucial for ensuring safety and efficiency. This study proposes a kick risk diagnosis method based on ensemble learning models, which integrates various time-series analysis algorithms to construct and optimize multiple kick diagnosis models, accurately fitting the relationship between integrated logging parameters and kick events. By incorporating high-performance ensemble models such as Stacking and Bagging, the accuracy and F1 score of the models were significantly improved. Furthermore, the application of the Synthetic Minority Over-sampling Technique and Tomek Links (SMOTE-Tomek) data balancing technique effectively addressed the issue of data imbalance, contributing to a more robust and balanced model performance. The results demonstrate that integrating time-series analysis with ensemble learning methods significantly enhances the predictive reliability and stability of kick monitoring models. This approach provides a dependable solution for addressing complex kick monitoring tasks in offshore and deepwater drilling operations, ensuring greater safety and efficiency. The findings offer valuable insights that can guide future research and practical implementation in kick risk diagnosis.