# Intelligent Stuck Pipe Type Recognition Using Digital Twins and Knowledge Graph Model
###### Abstract
With the development of digital twin technology, the combination of physical model and intelligent algorithm has become the trend of current engineering technology. As for the specific engineering problem, using the physical model, basic data, and intelligence algorithm at the same time has become the main technology for monitoring and identifying complex faults. The PAA is a classical similarity analysis and pattern recognition algorithm in which the dimensionality reduction technology is used to create a simplified signal representation. Firstly, as for the PAA proposed by [PERSON] et al. [12], the dimensions of the data were reduced by taking an average on the equally spaced frame. The SAX was first put forward by [PERSON] and [PERSON] [13], with such advantages as quick dimensionality reduction, high recognition precision, and high robustness. The SAX can be used to aggregate the time series segmentations into symbol, with similarity between the whole curve and the contrast curve obtained by calculating the distance between symbols and was developed by extension based on PAA. The good results were obtained by it in terms of fault identification [14], ECG signal recognition [15], and anomaly detection [16], and it has been widely used for the digital twin domain in all industries. The knowledge graph is an emerging knowledge representation method. Through the expression of the triad \"entity-relationship-entity\", a knowledge graph containing documents and expert experience on stuck drill type identification can be constructed to correlate the complex knowledge of different stuck pipe types. The unstandardized manual identification method can be replaced by compiling the knowledge graph into software to identify stuck pipe types, thus improving the accuracy of stuck pipe type identification. The knowledge graph is a representation of concepts and relationships in the form of nodes and edges, which can be constructed in various ways using resources such as pre-existing databases or text. It can represent a huge number of subject-object relationships, thus forming a knowledge graph with searchable and mineable effective information. For example, the knowledge graph is used to construct the specific knowledge graph in the oil exploration and development field. The intelligent search and Q&A system for petroleum exploration and development is formed by the natural language processing based on assembly of multi-source heterogeneous data and construction of one million nodes [17]. A good effect is embodied in terms of knowledge service. The large-scale integration and intelligent application of big data in the petroleum exploration and development field is accelerated. The knowledge graph has been widely used in various fields. For example, a good effect is obtained in the medical aspect [18; 19; 20], semantic recognition [21; 22], railway flow optimization [23], smart city [24], natural language processing [25; 26], and digital twin technology in the manufacturing industry [27]. On the basis of previous studies, the measured data are compared with the simulation results based on the rigid rod model for frictional torque of 3D well track to obtain the deviation degree of the measured parameter from the normal trend, the intelligence algorithm is used to analyze the trend of measured data changes in the process of sticking, the knowledge graph for the type of sticking is established based on the law of representation of different types of sticking, and the diagnosis on the type of sticking is realized according to physical model, intelligent method, and knowledge graph in this paper. The method proposed in this paper can be used to quickly identify the type of sticking in the site based on the digital twin and knowledge graph, providing the basis for scientific and accurate formulation of free stick measures.