# Innovative Approach Integrating Machine Learning Models for Coiled Tubing Fatigue Modeling
## 1 Introduction  
Coiled tubing (CT) is considered an important well intervention service due to its advantages such as flexibility compared to threaded joints for reaching downhole bottoms during various operations [1]. Since its introduction to the oil and gas industry, coiled tubing has been the subject of extensive research due to its reliability and performance in operations and service life, as well as its potential to prevent failures. Coil tubing consists of a long tube spooled onto a reel, which is set on a CT unit. This tubing can be stripped from the reel and smoothly run in and out of the hole through an arch guide and injector, enabling a wide range of applications, including well kick-off, hole cleaning, production stimulation, cementing, fishing, and coiled tubing drilling. Throughout its service life, CT encounters several challenges, including rupture, mechanical damage, and fatigue [2]. This can lead to failures and expensive costs related to string loss or recovery operations, and production cuts [3; 4]. While running, CT is subject to three cycles of bending and straightening combined with other factors such as fluid type, pumping pressures, CT unit design, and material properties. Over time, this contributes to the fatigue of CT [5]. Since 1980, many research studies have been conducted to understand the fatigue behavior and mechanisms of CT by developing predictive models based on full-scale and lab-scale fatigue tests. It takes into account various data, including material properties, applied forces, bending stresses, internal pressure, environmental conditions, and metal defects [6; 7; 8]. The modeling approach relies on principles of accounting for cumulative fatigue damage through Miner's rule, power-law damage, and the double linear damage rule [4]. Generally, the strain life method and the Manson-Coffin equation are the most useful equations for modeling low cycle fatigue, as the CT is subject to plastic deformation [9; 10; 11]. Fatigue damage refers to the amount of CT life consumed by each event. Based on the plasticity theory and the full-scale fatigue testing data, [PERSON] presented the Achilles CT fatigue model, which depends on the strain-life method and the Von Mises equation to calculate fatigue damage using the effective amplitude strain in strain-life curves. Additionally, it calculates diametrical changes in the CT's diameter [12]. The other well-known model is [PERSON]'s model, which calculates fatigue damage using the equivalent uniaxial alternating strain equation and strain-life method in strain-life curves [13]. The authors of [14] presented a model of CT fatigue life based on the tri-axial strain state of the strain-life approach. An experimental test was conducted in parallel with FEA analysis to predict the fatigue life and integration with developed software [14]. Other CT fatigue modeling efforts were based on metal defects and crack propagation, and a good correlation and strong results were found [5; 15; 16]. The most popular computational software recognized in the oil and gas industry includes Halliburton, CTES, Cycle, COILLIFE, Halliburton, and MEDCO, among others [12; 17]. Despite the implementation of these processes that have served well in this industry, CT has frequently experienced fatigue failures prior to achieving the fatigue life yield predicted by those softwares. These occurrences have prompted ongoing investigations by researchers into the accuracy of different factors, addressing the complexity of data and integration, such as corrosive environments [18], surface anomalies [15; 19], and slip damage [20]. Consequently, to address this issue with the advancements in the development of new CT grades and the challenges posed by well geometry, numerous experimental lab-scale and full-scale fatigue assessments have been conducted, updating the methodology and tracking process to improve CT fatigue life estimation [11; 21; 22; 23; 24; 25].