Unsupervised Learning–Based Kick Risk Warning with Dynamic Thresholds and Knowledge Embedding

Summary Kick risk is one of the most common and safety-critical issues in drilling operations, posing substantial threats to operational safety. Therefore, accurate kick risk warning is essential for ensuring drilling safety and operational efficiency. However, traditional data-driven models are often affected by the quality and variability of real-time logging data and lack integration with formation characteristics and domain knowledge, leading to high false alarm rates, instability, and limited effectiveness in kick risk warning. To address these challenges, we introduce two key innovations—a dynamically adjusted threshold mechanism, informed by predrilling kick risk assessment, and knowledge embedding to enhance the performance of data-driven models. The dynamic threshold mechanism enables the model to adaptively adjust its warning criteria based on predrilling assessments, reducing the false alarm rate. Knowledge embedding further optimizes the model by incorporating domain knowledge, improving the model’s accuracy and reliability. In this study, we propose an unsupervised temporal kick risk warning method based on bidirectional gated recurrent unit (BiGRU) autoencoder (AE)(BiGRU-AE), which leverages the temporal modeling capabilities of BiGRU to extract key features from the logging data. The proposed method is validated through a comprehensive evaluation using field drilling data sets, comparing temporal vs. nontemporal models, as well as supervised vs. unsupervised approaches, and assessing model performance before and after dynamic threshold optimization. The unsupervised model is trained using risk-free data, eliminating the need for labeled kick risk samples and effectively addressing the challenge of limited risk sample availability. The results show that BiGRU-AE outperforms other models, with the combined optimization of dynamic threshold and knowledge embedding reducing the false alarm rate by 14.67%, significantly enhancing stability and reliability. The proposed method organically integrates domain knowledge with intelligent technology, greatly improving the accuracy and practicality of kick risk warning. This provides strong technical support for ensuring drilling safety and demonstrates broad engineering application potential in the field of intelligent drilling.