Predictive modeling for coiled tubing fatigue using advanced machine learning techniques

Abstract

Coiled tubing CT plays Pivotal role in oil and gas well intervention operations due to its advantages such as flexibility, fast mobilization, safe, low cost and wide range of applications, including: well intervention, cleaning, stimulation, fluid displacement, cementing, and drilling. However, CT is subject of fatigue and mechanical damage caused by repeated bending cycles, internal pressure and environmental factors which can lead to a premature failure, high operational cost and production downtime. With the development of CT proprieties and application traditional fatigue life prediction methods based on analytical models integrated in tracking process showed in some cases an underestimate or overestimate of the actual fatigue life of CT particularly when complex factor like welding type, corrosive environment, and high-pressure variation are involved. This study addresses this limitation by introducing a comprehensive machine learning-based approach to improve the accuracy of CT fatigue life prediction, using a data set derived from over 350 tests both lab scale and full-scale fatigue test. The study has incorporated the impact of different parameters such as CT, grades, wall, thickness, CT diameter, internal pressure and welding types. By using advanced machine learning techniques such as artificial network (ANN), Gradient boosting regressor we got a more precise estimation of the number of cycles to failure than traditional models. The results of this study shown the importance of integration of machine learning for CT fatigue life analysis and demonstrating its capacity to enhance the prediction accuracy and reduce the uncertainty. A detailed machine Learning models is presented, emphasizing their capability to handle complex data and improve prediction under diverse operational conditions. This study contributes to more reliable, CT management and safer more cost-efficient well intervention operations.