Research on Recognition Method for Typical Drilling Conditions Based on Drill Bit Motion Characteristics

Drilling conditions serve as reliable indicators for evaluating drilling system performance. The recognition methodology forms a critical foundation for intelligent drilling and plays a significant role in preventing borehole accidents such as pipe sticking. This study proposes a recognition method for typical drilling conditions based on bit motion characteristics, developing intelligent algorithms that utilize fundamental motion parameters and drive signals as inputs to achieve accurate condition identification. Firstly, micro-drilling experiments were conducted under various pressure and rotational speed combinations. The variation characteristics of drilling speed, rotational speed, torque, and drilling pressure during the process were analyzed. Based on rotational and feed motion features at different stages, four typical drilling conditions were defined: stable drilling, slowing-down drilling, intermittent jamming, and pipe sticking. Subsequently, using actual coal mine drilling data, feature indicators for recognition models were selected through collective statistical characteristics and rolling statistical properties. Recognition models employing support vector machine (SVM), convolutional neural network (CNN), and long short-term memory (LSTM) networks were constructed and trained. Test results demonstrate that the LSTM-based model achieved the highest recognition accuracy (84% overall). It attained 100% accuracy in identifying slowing-down drilling and intermittent jamming conditions, though recognition accuracy for stable drilling remained relatively low. This research contributes to enhanced intelligent recognition of drilling conditions and provides theoretical support for advancing intelligent drilling technologies.