Deep and offshore drilling operations face complex geological formations, uncertain formation pressures, and narrow safety density windows, making them susceptible to lost circulation risks. To address these challenges, this paper introduces an innovative, intelligent lost circulation monitoring model that incorporates geological lithology information. This model not only utilizes real-time drilling parameters, but also encodes geological information such as rock type as inputs to the model. By combining these key lithological features, the model can comprehensively assess wellbore stability and reduce the lost circulation risks. In this paper, the Conditional Tabular Generative adversarial network (CTGAN) model is used to enhance the data of small-sample risk data, which can effectively expand the data distribution space and improve the performance of the model. This paper conducts a comparative analysis of intelligent monitoring results using artificial neural networks (ANNs), long short-term memory (LSTM), and temporal convolutional networks (TCNs). The results show that the TCN achieves an identification accuracy of 93.7%. Furthermore, the analysis reveals that the inclusion of lithology information significantly enhances the model’s performance, resulting in a 7.1% increase in accuracy. The false alarm rate of the model can be reduced by 10.2%, considering the fluctuation of the logging curve caused by the on/off condition of the pump. This indicates that the introduction of lithology information and the condition of the pump on−off provide advantages in monitoring and identifying lost circulation risks, enabling a more precise assessment of wellbore stability and a reduction in lost circulation incidents. The method of lost circulation monitoring proposed in this paper provides an important safety guarantee for the oil drilling industry.

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.

Gas kick events in drilling operations are characterized by strong coupling dynamics, subtle early-stage evolution, and severe class imbalance, which limit the effectiveness of conventional feature-independent monitoring methods. To address these challenges, this paper proposes a structure-aware intelligent monitoring framework for early gas kick detection. First, multivariate drilling parameters are modeled as an interacting graph, and a graph neural network (GNN) is introduced to capture relational dependencies and anomaly propagation behaviors at the structural level. Second, to mitigate abnormal sample scarcity and enhance temporal discriminability, a representation enhancement strategy integrating conditional tabular generative adversarial networks (CTGAN) and shapelet-based temporal patterns is developed. Finally, a multi-level interpretability mechanism combining graph attention analysis and SHAP attribution is constructed to provide transparent insights into both structural interactions and feature contributions. Experiments conducted on real drilling datasets demonstrate that the proposed GNN baseline achieves the highest accuracy (0.7302) among various machine learning and deep learning models. With representation enhancement, the GNN+CTGAN+Shapelet model further improves accuracy to 0.7507 and F1-score to 0.7347, validating the effectiveness of the enhancement strategy. Interpretability results reveal that the model decisions are primarily driven by flow-rate and standpipe-pressure-related temporal evolution patterns, which are consistent with drilling engineering knowledge. Overall, the proposed framework provides a structurally consistent, robust, and interpretable solution for intelligent gas kick monitoring in modern drilling operations.

To accurately quantify pore pressure uncertainty and associated kick risk, this paper proposes a dual-phase pre-drilling risk assessment framework based on Bayesian Long Short-Term Memory (BLSTM) networks, integrating formation pressure prediction with distribution interference analysis. First, the effects of two Bayesian layer optimization methods—Monte Carlo dropout and Bayes-by-Backprop—on deep learning networks were systematically evaluated. The optimized Bayes-by-Backprop-LSTM model was subsequently selected for uncertainty prediction of formation pore pressure. Finally, kick risk was quantified by analyzing the interference between predicted pressure distributions and the safety margin of designed drilling mud density. The BLSTM models uncertainty regression between well-log parameters and formation pore pressure labels. Using the Bayes-by-Backprop strategy, it generates probabilistic pressure predictions. By incorporating the designed drilling mud density of target wells, kick risk probability is calculated through distribution interference criteria, where the overlapping area between pore pressure distributions and mud density safety boundaries is mapped to risk probability. Validation experiments utilized five types of well-log parameters from three wells in EAST CHINA. Key results demonstrate: (1) The BLSTM regression model achieved a mean absolute error (MAE) of 0.037 on test wells, representing a 26.7% reduction compared to conventional LSTM, with the 95% confidence interval coverage reaching 69.6%. (2) In the 3893–4048 m interval of a test well, interference areas exceeding thresholds indicated 60% kick risk probability. Spatial correlation with actual kick events revealed risk points undetectable by conventional pore pressure prediction methods. This study establishes a comprehensive risk assessment paradigm encompassing pore pressure uncertainty regression prediction and probabilistic risk calculation, providing drilling engineering with a framework that combines physical interpretability and statistical reliability.