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.