Stuck pipe，a common risk during drilling operations，severely restricts safe and efficient drilling. Traditional methods for diagnosing and handling stuck pipe incidents mostly rely on expert experience，mechanistic models，or supervised machine learning algorithms and suffer from issues such as diagnostic lag，high false alarms due to overfitting，strong subjectivity，and low adaptability. This paper proposes a method for diagnosing stuck pipe risks and recommending cases of countermeasures based on unsupervised learning and K-Nearest Neighbors （KNN），which has been implemented as a software system. The system comprises two core modules：stuck pipe risk diagnosis and stuck pipe incident handling. The stuck pipe risk diagnosis module employs the Isolation Forest algorithm to monitor and diagnose abnormal trends in key real-time downhole engineering parameters. The stuck pipe incident handling module utilizes a Case-Based Reasoning （CBR） algorithm based on KD-Tree and KNN to retrieve cases from a stuck pipe decision case library，returning the data of three most similar cases to the client-side to assist engineers in handling stuck pipe incidents. Based on the aforementioned，an intelligent downhole sticking diagnosis system based on case-based reasoning was designed and implemented. Tests show that the system successfully issues warnings approximately 5 minutes before an incident occurs，quickly retrieves three most similar historical handling cases，and pushes them to the interface. This developed system provides direct decision support for engineers.

In recent years， the large-scale development of unconventional oil and gas resources in China has led to a sharp increase in the number of horizontal wells. Due to the lack of scientific and systematic understanding on horizontal well bore cleaning， drilling process in horizontal section often appears stuck， completion ahead of schedule， disability of running casing to the predetermined well depth. In addition， poor wellbore cleaning can also cause very high implicit costs， including ROP controlling， increasing time consumption of reaming， circulation， and tripping. In this paper， by clarifying the mechanism of cutting migration in different sections of horizontal wells， analyzing the key elements of wellbore cleaning and proposing a method to evaluate wellbore cleaning by monitoring actual drilling friction， a set of efficient horizontal wellbore cleaning technology has been formed. Field practice in Mahu and Jimsar shale oil has verified the technical theory based on field data， which can guarantee the cleanness of horizontal section under the conditions of existing domestic equipments and instruments， and greatly ruduce inefficient production time consumption such as mud circulation and wiper tripping to increase speed and efficiency.