es and advantages/disadvantages of different models in terms of early warning capabilities. It is found that in scenarios where the demand for characteristic parameters is low and the data sources are stable，machine learning algorithm models exhibit superior accuracy and timeliness in the early warning of drilling complex accidents. However，when faced with challenges such as high computational complexity，poor data quality，and unstable data sources，especially in predicting lost circulation during drilling and early-warning complex well kick accidents，the models suffer from high rates of false alarms and frequent missed alarms，and few field application cases are reported. To promote the field application of drilling complex accident early warning techniques and drilling digitalization，it is necessary to strengthen research on data quality improvement and algorithm optimization. Despite the numerous challenges，big data and artificial intelligence still open up broad prospects for early warning technologies.

In drilling engineering，stuck pipe，as one of the common downhole complex issues，seriously affects drilling efficiency. Stuck pipe monitoring is crucial for ensuring the safety and efficiency of drilling operations. With the rapid development of artificial intelligence technology in recent years，new approaches have emerged for stuck pipe monitoring. However，the existing research on intelligent stuck pipe monitoring mainly focuses on the optimization and application of single unsupervised or supervised algorithm，leaving a gap of systematic comparative studies on these two types of algorithms with respect to stuck pipe monitoring. This study selects multi-dimensional drilling parameters such as bit position，hook height，and torque of ratary table as the study targets，according to distance correlation coefficients，constructs a comparative evaluation system involving classic unsupervised algorithms （AE，K-means，DBSCAN） and supervised algorithms （SVM，RF，LSTM），and analyzes the performance of these algorithms in estimating stuck pipe trends. The results show that compared with supervised algorithm，unsupervised algorithm delivers a increase of 12.5% in monitoring average accuracy and reductions of 37.1% and 27.6% in average false and missed alarm rates respectively. Unsupervised algorithm demonstrates greater advantages in cases of small sample sizes and absence of mechanistic constraints. The findings of this research provide references for model selection and optimization in intelligent stuck pipe monitoring for drilling engineering and promote the practical application of unsupervised algorithm in stuck pipe risk monitoring.

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.

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.

The emergence of large language models（LLM） with characteristics of general artificial intelligence has ushered in a milestone technological revolution across industries，offering new opportunities for the intelligent transformation of petroleum engineering. This paper explores the application prospect，challenges，and development recommendations for LLM，represented by DeepSeek，in petroleum engineering. First，the fundamental concepts and technical features of LLM are introduced. Subsequently，potential application scenarios in petroleum engineering are examined，including user interaction and Q&A systems，data governance and information integration，data analysis and decision support，information parsing and intelligent assistance，and environmental monitoring and safety management. Concurrently，limitations and challenges in applying LLM to petroleum engineering are identified，such as insufficient knowledge updating capabilities，difficulties in comprehending domain-specific expertise，limited innovation in scientific research，and high training costs. Finally，recommendations and future directions for leveraging LLM in petroleum engineering are proposed，including developing specialized LLMs tailored for petroleum engineering，constructing petroleum-domain databases and information extraction frameworks，integrating internet-enabled search and real-time updating functionalities，and advancing image processing and video generation technologies. This study systematically outlines an implementation framework for LLM in petroleum engineering，providing theoretical guidance and practical references for the industry’s intelligent evolution.

In cable logging operations，accurately regulating the cable tension is crucial to ensure the safe arrival of the logging instruments at the wellbore bottom and accurate data acquisition. In order to precisely predict the change of cable tension during well logging，a well logging cable tension prediction system based on the hybrid model was designed，which combines the physical model with in-depth understanding of the mechanical nature of the logging process and the data-driven model SC-PBiGRU with advantages in data processing and analysis. The system was validated using a large number of well logging data. The system can keep the maximum error of the prediction results within 5% and deliver stable prediction accuracy under complex geological conditions and equipment status. It is a powerful tool to provide technical support for tension regulation in cable logging operations.

The downhole monitoring system，integrated into the conventional electric control downhole flow control valve for intelligent wells in China，cannot monitor the two key parameters，namely the flow rate and water cut. Moreover，once the driving mechanism fails，the transmission screw automatically locks the sliding sleeve at the current position，making it impossible to close or fully open the sliding sleeve in a timely manner and resulting in the inability to regulate the production of the target layer or branch. In this research，a unique sliding sleeve mechanical structure with an integrated downhole parameter measurement system was designed using the selected sensors of pressure ，temperature and water-cut，flow meter，and downhole sliding sleeve displacement sensor，to solve the incapabilities of existing electrical-control downhole flow control valves in China to monitor fluid flow and water cut. A unique mechanical transmission system was designed using the scalable coupling to serve as the driving mechanism for the concentric sliding sleeve. The designed sliding sleeve integrates a force-open force-close mechanical structure，which can be moved to the fully open or fully closed position by the sliding sleeve switch tool. This eliminates the shortcomings of the mechanical structure of conventional electric control downhole flow control valves in China and provides key support for the development of the intelligent well technology in China.

The investment estimation of a systematic drilling engineering project is an important step for oilfield enterprises to strengthen investment control and enhance operation management. The quality of investment estimation does not only decide the feasibility and profitability of the development plan，but also has an important instruction influence on the implementation and operation performance of the approved engineering plan. This paper proposed a method for extracting engineering parameters of drilling investment estimation based on the natural language processing algorithm and the Monte Carlo simulation investment prediction model. These two techniques were introduced into the petroleum engineering estimation，and it was demonstrated via modelling and case studies that all selected control factors were significant and thus effective. Based on the above，the investment estimation was carried out. Natural language processing algorithms were required for parameter extraction and processing，with an accuracy of over 90%. Meanwhile，the Monte Carlo simulation investment prediction model was used for calculation to ensure that the error between the extreme investment and the existing economic evaluation results was less than 5%. This developed method has been successfully applied to 29 production capacity building projects in 2024，identifying and warning 8 projects with excessive investment. It improves the accuracy and efficiency of engineering parameter extraction，enhances the percent of pass for the internal rate of return of petroleum drilling engineering investment estimation，and is of great help in improving the digitalization level of the petroleum engineering estimation.

Given that the conventional five-arc trajectory design of the double-step horizontal wellbore may have no feasible solution in the case of limited horizontal section spacing，this study proposed a wellbore trajectory design method based on constrained optimization. By establishing a constrained optimization model to minimize the horizontal section length，an efficient solving algorithm was constructed，which combines the quasi-analytic solution and the bisection method. Given the failure problem that the singularity polynomial of the proposed quasi-analytic solution is always zero when the inclination and azimuth angles of the two horizontal sections are the same，a modified analytical calculation formula was proposed to deal with this theoretical defect of the traditional method. Compared with the traditional iterative method，the proposed method reduces the computational complexity to the order of solving quadratic equations via the characteristic polynomial dimension reduction strategy. Examples showed that this method can converge rapidly in cases of three-dimensional stepped wells，two-dimensional wells and simplified cases. By optimizing the adjustment of the horizontal section through the bisection method，the control of the minimum trajectory length under the constraint of wellbore curvature was achieved. This method can effectively deal with the trajectory design in cases of narrow spacing，like fault-separated reservoirs，and provide theoretical support for the development of drilling software. The research findings significantly improve the reliability and engineering applicability of the trajectory design for stepped horizontal wells.