# Intelligent Prediction of Stuck Pipe Using Combined Data-Driven and Knowledge-Driven Model
## 1 Introduction  
Drilling is a key process to oil and gas exploration and development which is full of randomness, uncertainty and concealment. It is necessary to place several thousand meters of drill pipe in a narrow space with a diameter of only 200-500 mm. Unreasonable design of engineering parameters and unclear understanding of the formation often lead to complex downhole accidents (lost circulation, well collapse, stuck pipe, etc.), among which stuck pipe is one of the most common accidents.  
According to the statistics of complex accidents in the South China Sea during 2009-2018, about 45% of them were stuck pipe [1]. Mudlog data from Tarim Oilfield in China shows that it takes about 3 months to deal with a serious stuck pipe accident which increases non-productive times and seriously affects drilling efficiency. Therefore, the timely prediction of stuck pipe events is considered a primary necessity to assist the drilling team in the decision-making process, so that appropriate countermeasures can be put in effect before the situation slips out of hand.  
Mudlog data shows that the stuck pipe accidents occur in the process of tripping and drilling with varying degrees of severity. For example, in some highly deviated wells and horizontal wells, the stuck pipe accident is often accompanied in the process of tripping, but it is usually not serious. On the contrary, more attention is paid to the stuck pipe during drilling. Once the drill string is completely stuck, it can only be lifted by explosion and a new side drilling scheme need to be redesigned, which seriously restricts the safety and efficiency of drilling. Therefore, we focus on the real-time prediction of stuck pipe in the process of drilling.  
Many approaches have been developed to predict and reduce the risk of stuck pipe. In summary, the analysis and prediction of stuck pipe accidents can be divided into two parts: data-driven and knowledge-driven.  
For the knowledge-driven model, cuttings, wellbore tortuosity and other factors will eventually lead to the increase of torque and drag which can lead to the occurrence of stuck pipe accident. Therefore, researchers use the torque and drag model to invert the friction coefficient to realize the qualitative analysis of stuck pipe trend in the view of mechanics. However, this method is mainly used to analyze and deal with the stuck pipe after drilling considering that the WOB (weight on bit) and TOB (torque on bit) cannot be transmitted in real time.  
For the data-driven model, researchers applied support vector machine, random forest, neural network and other algorithms to the prediction of stuck pipe. However, the generalization ability and reliability of the intelligent model need to be further improved due to the quantity and quality of samples and the differences between blocks.  
Based on previous research, we establish a comprehensive indicators based on the knowledge-driven and data-driven model using fuzzy mathematics, which is expected to realize accurate prediction of stuck pipe and ensure the safety and efficiency of drilling  
The remaining of this paper is organized as follows: Section 2 discusses the literature review of the prediction methods of stuck pipe. Section 3 introduces our proposed method. Section 4 explains the experimental results and discusses the main findings and finally Section 5 concludes this research and provides some plans for future work.