Coiled Tubing Fatigue Research Articles

Predictive modeling for coiled tubing fatigue using advanced machine learning techniques

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.

A modified rule for estimating notch root strains in ball defects existing in coiled tubing

Coiled tubing life is predominantly limited by fatigue as it undergoes cyclic bending strains well into the plastic regime leading to cyclic ratchetting due to plastic strain accumulation. Furthermore, small defects occurring in the oil field environment are capable of reducing coiled tubing fatigue life significantly. Small defects are macroscopic flaws that have small dimensions relative to the tubing wall thickness. Very shallow flaws at depths of only a few percent of the wall thickness can have a substantial, detrimental influence on fatigue life. Accordingly, the influence of surface ball defects on the magnitude of the fluctuating strains at the root of these defects was the subject of this investigation. Accurate cyclic strain magnitude predictions are essential to ultimately quantify the fatigue life of coiled tubing. The effects of defect radius and depth were both studied, and the strains at the notch root were evaluated for coiled tubing having a diameter of 2.375 in. and a wall thickness of 0.156 in..A matrix of ball defects with various depths and radii were examined. The defects were introduced at the surface of a model having the same thickness as the actual coiled tubing. The FEA results provided detailed information about the magnitude of cyclic strains at the root of a defect in coiled tubing that were previously unavailable. The notch root strain results were used to bring about a modified Neuber’s rule since conventional notch strain analysis approaches did not lead to accurate notch root strain range predictions. A new modified rule was therefore proposed and validated based on a detailed statistical analysis. This modified rule led to a substantial reduction in the percentage difference between the numerical notch root strain range and the estimated analytical one to within 8%.

Improvement and determination of the influencing factors of coiled tubing fatigue life prediction

The fatigue life prediction model cannot accurately predict the service life of coiled tubing operations, resulting in waste and accidental fracture of coiled tubing. This article introduces the fatigue life prediction model of coiled tubing and a detection system developed using weak magnetic detection method and eddy current detection method. The detection device is used to detect accurate defect dimension, wall thickness reduction, and diameter deformation to improve the accuracy of fatigue life prediction. Furthermore, the actual defect dimension is included in the fatigue life assessment calculation to eliminate the influence of the existing model on the immobilization of defects and corrosion influence factors. By analyzing field operation cases and measured values, the effects of defects, wall thickness reduction, and diameter growth on fatigue life are studied to improve the accuracy of parameters and the integrity of basic data in the calculation process. The influence of reverse bending and weld on fatigue life is discussed. The residual life can be predicted more accurately by combining field testing technology and fatigue life evaluation method. Field measurement input of fatigue life prediction model is the main means to improve its accuracy and practicability.

Prebending Coiled Tubing and Its Fatigue Life Prediction

Coiled tubing is widely used in oil drilling and production operations. However, extreme high stress variation of coiled tubing during the processes of pulling out, rolling in the reel, and passing through the gooseneck makes coiled tubing fatigue failure easily. Thus, it is of great importance to increase coiled tubing fatigue life. This paper introduces the new technology to improve the fatigue life of coiled tubing—the prebending coiled tubing technology; proceeds mechanical analysis and strength check of the prebending coiled tubing; analyzes stress cycling characteristics of the prebending coiled tubing in field operations; establishes the fatigue life prediction model of prebending coiled tubing under arbitrary cycle, on the basis of fatigue experimental data under the symmetric cycle and the pulsating cycle, with fitting and interpolation method; makes simple comparison of the fatigue life of the prebending coiled tubing with that of the straight coiled tubing. Preliminary calculations show that the prebending coiled tubing technology may improve the fatigue life of coiled tubing multiple times.

Research on the Analysis of Coiled Tubing Fatigue Life

Coiled tubing (CT) and coiled tubing units (CTU) technology has been widely used in oil and gas field since World War II. Coiled tubing fatigue failure is one of the constraints on promoting used. CT bears complex loads, leading to multiple plastic deformations when operating. Then the fatigue damage cannot be ignored. Through the mechanics analysis of CT with finite element method (FEM) in working state at the wellhead, the results show that high stress and high strain are the main factors which cause CT failure. Based on the analysis, the fatigue life is predicted for CT without defects by using Manson-Coffin formula. The results of CT with different inner pressure show that simulations result is closer to the actual working conditions.

コイルドチュービング作業におけるパイプ管理について パーソナルコンピューターベースの記録・表示システムの紹介

Pipe management is one of the key elements of coiled tubing operation. Durability of the coiled tubing was simply evaluated by running feet for a long period of time which resulted in tubing failure. After extensive testing at the research facility, we obtained a reliable model to estimate coiled tubing fatigue. Now we have introduced a PC-based pipe management system called “CoilCAT” which eliminates extensive manual recording during operation. It also improves safety, economics and reliability. CoilCAT consists of a sensor network and a PC which acquires and analyzes they data. During operation, circulating pressure, well head pressure, coiled tubing weight, running speed, depth, coiled tubing dimension, and other data are monitored and collected with the history of the tubing already in the PC. At the same time, it will display pressure and tension limits of that particular condition. The system can alert the operator if any of the monitored and calculated values exceed the predetermined limits.