# Innovative Approach Integrating Machine Learning Models for Coiled Tubing Fatigue Modeling
## 9 General Conclusions  
With the revolution in AI and machine learning methods, predicting material fatigue behavior in terms of fatigue life has become easier to track and more accurate. This study takes a step toward tackling this phenomenon by gathering a large amount of fatigue testing data, which represents the first condition for effective machine learning.  
The techniques applied have demonstrated strong performance in predicting CT fatigue life, forming the foundation for a proper modeling and tracking process to be implemented in field applications.  
This study demonstrated the effectiveness of using machine learning to handle complicated problems that are difficult to model analytically. Due to its ability to manage a large, complex, and non-linear dataset (analyzing, training, and testing), machine learning aids in effectively studying material fatigue behavior, enabling us to apply this beneficial and powerful tool to successfully study the fatigue of coiled tubing in comparison to traditional methods.  
Figure 15: Prediction plot for the best models with test data vs. actual data (Random Forest, Decision Tree, XGBoost and CatBoost).  
This study explored the estimation of coiled tubing fatigue life by analyzing the performance of different machine learning models. The outcomes revealed that gradient boosting regression algorithms, namely CatBoost and XGBoost, consistently surpassed other models in terms of accuracy, as indicated by the R\({}^{2}\), MAE, and MSE metrics. These results emphasize the ability of advanced machine learning approaches to enhance fatigue life predictions, contributing to the improved dependability and efficiency of coiled tubing operations in well interventions. Proper modeling enhances the prediction of CT fatigue evolution. This modeling step forms the basis of a monitoring and tracking process that can help to prevent CT failures in advance and optimize CT's service safely and successfully.  
Overall, this research highlights the importance of using and choosing the most optimal machine learning models for predicting coiled tubing fatigue, such as Gradient Boosting models (CatBoost and XG Boost), making them valuable tools for engineers in predictive maintenance and structural reliability.  
Conceptualization, K.M.B., A.H., A.A.S., E.A.S. and H.H.; Data curation, K.M.B.; Formal analysis, A.A.S. and E.A.S.; Methodology, K.M.B., A.A.S. and H.H.; Resources, K.M.B. and E.A.S.; Supervision, A.A.S. and A.H.; Validation, AH.; Visualization, K.M.B., A.A.S. and H.H.; Writing--original draft, K.M.B., E.A.S.; Writing--review and editing, K.M.B. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original contributions presented in the study are included in the article and further inquiries can be directed to the corresponding author.  
Author [PERSON] was employed by the company Baker Hughes. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.