# Intelligent Prediction of Stuck Pipe Using Combined Data-Driven and Knowledge-Driven Model
## 4 Results and Discussion  
The paper analyzes the stuck pipe case of well A in Tarim Oilfield, China. The stuck pipe occurred at 6110.58 m. After various rescue measures, the accident was not successfully resolved, so it was finally decided to sidetrack again. It took about 3 months from the occurrence of the stuck pipe to the continuation of drilling, which seriously affected the drilling efficiency. Figure 10 shows the stuck pipe index 1 (friction coefficient), which is calculated by the real-time inversion method of friction coefficient established in Section 3.1, and its change can characterize the stuck pipe trend to a certain extent. Figure 11 shows the stuck pipe index 2 (stuck pipe probability), which is obtained from the intelligent prediction model of stuck pipe probability based on the LSTM network in Section 3.2, which can provide early warning of stuck pipe through the changes of mudlog data. The red straight line indicates the predicted result of stuck (stuck/non stucking). Blue scatter indicates the probability of stuck pipe. The blue straight line represents the real sample of the site (0: no stuck, 1: stuck). Figure 12 shows the comprehensive prediction of stuck pipe by using the comprehensive evaluation index established in Section 3.3. Figure 10: Variation trend of friction coefficient. As shown in Figure 10, it is found that the friction coefficient increases gradually from 6000 m to 6110 m. The friction coefficient increases sharply from 0.35 to 0.75 around 6110 m, which indicates the risk of stuck pipe. However, Real-time inversion of friction coefficient can only achieve qualitative evaluation, and it is difficult to accurately predict stuck pipe. It can be seen from Figure 11 that the evaluation indexes predicted by the intelligent prediction model are: FAR: 0.3, MAR: 0, F1: 0.82. It can be found that although the MAR is 0, the FAR is relatively high, reaching 30%. Although a certain FAR is allowed for the prediction of stuck pipe, the FAR is too high, which will affect the drilling efficiency to a certain extent. It can be seen from Figure 12 that the comprehensive evaluation indexes for stuck pipe are: FAR: 0.01, MAR: 0.04, F1: 0.98. Compared with the intelligent prediction model, the FAR is lower, only 1%. However, there is a certain MAR, which is 4%. Although for the prediction of stuck pipe, the existence of MAR will cause a certain risk to the site, this paper has marked some normal samples as stuck samples when dividing stuck samples, and it can be found from the figure that the 4% missed alarm rate is only the samples that is artificially marked as stuck samples are missed, and the real stuck samples have not been missed. As shown in Table 3, the three prediction methods are compared and analyzed. First of all, trend analysis method of friction coefficient has a long history. In this paper, the neural network model is introduced to realize the real-time calculation of the WOB and TOB, and further realize the real-time monitoring and trend analysis of the friction coefficient during drilling, but it is still qualitative analysis, and it is difficult to give the accurate quantitative index. The intelligent prediction of stuck pipe based on LSTM network can give accurate quantitative index of stuck pipe. However, through the analysis of FAR, MAR and F1, it is found that the prediction of stuck pipe based on data-driven method has a high FAR of 30%, which will have a certain impact on the site. The comprehensive evaluation index for stuck pipe is obtained by combining two stuck sub-indexes through fuzzy mathematics. It realizes the prediction of stuck pipe based on data-driven and knowledge-driven model. Through the analysis of evaluation indexes  
Figure 12: Prediction of stuck by comprehensive evaluation index.