Accurately identifying the damage failure modes and causes of PDC cutters and PDC drill bits is a key step in the drilling tool selection and the drill bit iterative optimization. To improve the accuracy and objectivity of drill bit damage identification，failure analysis was conducted on hundreds of PDC bits tripped out of wells，and the main damage failure modes and causes of PDC cutters (including various shaped cutters) were summarized，leading to a dataset containing over ten thousand images of PDC cutter damage morphologies. Then，based on the convolutional neural network-based YOLOv7 image recognition algorithm，an intelligent recognition model for PDC cutter damage failure was established. This model can effectively infer damage types from PDC cutter images and automatically annotate the images with corresponding damage failure modes. The model was evaluated using multiple performance metrics，showing an identification accuracy of over 80%. Furthermore，this model was combined with the PDC drill bit design theory and damage failure mechanisms to develop a new intelligent recognition method for PDC drill bit damage failure，using statistical methods like causal inference. This method can automatically evaluate the damage failure modes of PDC cutters in different regions of the bit crown using only photos of bits tripped out of wells and determine the primary cause of PDC drill bit failure. The research findings provide references for intelligent drill bit damage identification and innovation in intelligent drilling technology.

Stuck pipe incidents significantly disrupt drilling operations and cause major economic losses. Traditional physics-based models for stuck pipe prediction and analysis are subjective with large errors，while intelligent models suffer from high false alarm rates and low interpretability. To address these issues，an unsupervised stuck pipe risk evaluation method based on fuzzy mathematics was proposed. This approach involves：1） establishing a tubular mechanics model to quantify wellbore friction characteristics；2） constructing a deep autoencoder to detect abnormal parameters through reconstruction error analysis；and 3） developing a dual-factor membership function for a comprehensive fuzzy evaluation of friction coefficient trends and reconstruction errors. This method avoids the dependence of the conventional supervised learning on labeled data，integrates the interpretability of mechanistic models with the generalization ability of data-driven models，and creates a physics-constrained intelligent risk assessment framework. Tests using real drilling data show that this model effectively identifies early signs of stuck pipe. It improves warning accuracy by 7.1%，compared to the single-parameter methods，reduces false alarms，and delivers a 30-minute-earlier alert. The proposed method provides a promising new technique for predicting downhole complex issues and possesses significant application potential.