# Intelligent Stuck Pipe Type Recognition Using Digital Twins and Knowledge Graph Model
###### Abstract  
During drilling operations, stuck pipe occurs from time to time due to various reasons such as continuous changes of the formation lithology, failure to return the drill cuttings in time, shrinkage or collapse caused by soaking the formation with drilling fluid, and steps in the well wall caused by the drill-down. After the stuck pipe, the identification of the stuck pipe type can only be guessed by manual experience due to the jamming of the drill stem downhole, which lacks a scientific basis. Moreover, there is a lack of studies on the stuck pipe type. Therefore, scientific and accurate identification of the stuck pipe type is of great significance for timely unsticking and resuming drilling. In this paper, based on the friction torque rigid rod model of a3D well track, we obtained the degree of deviation of measured parameters from the normal trend, which can scientifically evaluate the degree of stuck pipe. Based on the SAX morphological symbolic aggregation approximation method, we obtained the changing trend of measured parameters during the stuck pipe, which can accurately describe the change laws of characteristic parameters during the stuck pipe. Based on the statistical characterization laws of different stuck pipe types in Sichuan and Chongqing, we established the knowledge graph of stuck pipe types, which can correlate with the complex knowledge of different stuck pipe types. The stuck pipe type can be identified according to the degree of stuck pipe, the changing trend of the characteristic parameters of stuck pipe, and the knowledge graph of stuck pipe types. The results show that the method can combine digital twins and the knowledge graph to accurately identify the stuck pipe type and provide a basis for taking targeted deconstruction measures. identification of stuck pipe types by combining a physical model and data analysis, which not only helps to take correct unblocking measures after the stuck pipe on the spot but also helps to study the early warning of different stuck pipe types. The physical model for the identification of sticking began to develop from the soft string model put forward by [PERSON] [1] for calculation of frictional torque. The model was a simple solution and could meet the calculation requirements under general conditions. The rigid rod model [2; 3] put forward by [PERSON] improved the calculation precision and was capable of coupling with the curved interval with a large curvature change and drill collar and heavy weight drill pipe with a large rigidity. Most of the subsequent calculation models of frictional torque develop and improve based on these two models and provide frictional torque board chart in the site to assist the monitoring of sticking [4; 5]. As the monitoring technology of sticking gradually becomes automatic and intelligent, SVM, LSTM, clustering analysis, Bayesian network and particle swarm are used to analyze the data trend of sticking in a supervised or unsupervised way in some studies [6; 7], improving the accuracy of complex accident identification, while the analysis on sticking only in a data-driven way is not comprehensive and the normal trend of change of frictional torque presented by the model is ignored. The misdeclaration of sticking occurs very easily, so the research about early warning of sticking gradually develops to digital twin, namely taking full advantage of the 3D well track, well cleanliness, drilling tool assembly, real-time logging data and derived data of frictional torque model calculation model, and hydraulic calculation model for automatic real-time monitoring of well drilling [8; 9; 10], improvement of safety and reduction of costs via digital twin technology to accurately analyze the probability of sticking from the angle of mechanics, and data change. For example, the frictional torque roadmap simulated is used to monitor the hanging load for pulling out of the hole, the hanging load when running in hole, and the idling hookload [11] subject to real-time measurement to identify the sticking.