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.

Stick-slip vibrations in drilling reduce efficiency and increase tool failure risks. Current identification methods, relying on surface data or lab models, poor real-time adaptability. This study proposes a novel method combining wavelet de¬composition (Morlet wavelet) with multi-parameter logical discrimination detect vibrations. Four drilling efficiency indicators, bit aggressiveness, cutting depth, torque variation index, and weight on bit variation index, are analyzed from surface data. Wavelet decomposition extracts high frequency components to quantify vibration intensity, with adaptive decomposition levels (1-3) balancing sensitivity and computational efficiency. Field-calibrated thresholds classify minor and severe vibrations. Validation using downhole acceleration data shows torque signals detect minor vibrations effectively (87%-94% accuracy), while bit aggressiveness excels in severe stages. The method reduces reliance on downhole instruments, improves timeliness, and enhances recognition stability by 30%-45% over conventional approaches, offering a cost-effective solution for real-time monitoring and risk mitigation.