Coiled tubing (CT) integrity is critical for well intervention operations in the field. To monitor and manage tubing integrity, the industry has developed a number of computer models over the past decades. Among them, low-cycle fatigue (LCF) modeling plays a paramount role in safeguarding tubing integrity. LCF modeling of CT strings dates back to the 1980s. Recently, novel algorithms have contributed to developments in physics-based modeling of tubing fatigue and plasticity. As CT trips into and out of the well, it goes through bending-straightening cycles under high differential pressure. Such tough conditions lead to low- or ultralow-cycle fatigue, limiting CT useful life. The model proposed in this study is derived from a previous one and based on rigorously derived material parameters to compute the evolution of state variables from a wide range of loading conditions. Through newly formulated plasticity and strain parameters, a physics-based damage model predicts CT fatigue life, along with diametral growth and wall thinning. The revised modeling approach gives results for CT damage accumulation, diametral growth, and wall thinning under realistic field conditions, with experimental validation. For 20 different coiled tubing alloys, it was observed that the model improved in accuracy overall by about 18.8% and consistency by 14.0%, for constant pressure data sets of more than 4,500 data points. The modeling results provide insights into the nonlinear nature of fatigue damage accumulation. This study allowed developing recommendations to guide future analytical modeling and experimental investigations, to summarize theoretical findings in physics-based LCF modeling, and to provide practical guidelines for CT string management in the field. The study provides a fundamental understanding of CT LCF and introduces novel algorithms in plasticity and damage.
{"title":"Latest Development in Physics-Based Modeling of Coiled Tubing Plasticity and Fatigue","authors":"Zhanke Liu, S. Tipton, D. Sukumar","doi":"10.2118/204421-MS","DOIUrl":"https://doi.org/10.2118/204421-MS","url":null,"abstract":"\u0000 Coiled tubing (CT) integrity is critical for well intervention operations in the field. To monitor and manage tubing integrity, the industry has developed a number of computer models over the past decades. Among them, low-cycle fatigue (LCF) modeling plays a paramount role in safeguarding tubing integrity. LCF modeling of CT strings dates back to the 1980s. Recently, novel algorithms have contributed to developments in physics-based modeling of tubing fatigue and plasticity. As CT trips into and out of the well, it goes through bending-straightening cycles under high differential pressure. Such tough conditions lead to low- or ultralow-cycle fatigue, limiting CT useful life. The model proposed in this study is derived from a previous one and based on rigorously derived material parameters to compute the evolution of state variables from a wide range of loading conditions. Through newly formulated plasticity and strain parameters, a physics-based damage model predicts CT fatigue life, along with diametral growth and wall thinning. The revised modeling approach gives results for CT damage accumulation, diametral growth, and wall thinning under realistic field conditions, with experimental validation. For 20 different coiled tubing alloys, it was observed that the model improved in accuracy overall by about 18.8% and consistency by 14.0%, for constant pressure data sets of more than 4,500 data points. The modeling results provide insights into the nonlinear nature of fatigue damage accumulation. This study allowed developing recommendations to guide future analytical modeling and experimental investigations, to summarize theoretical findings in physics-based LCF modeling, and to provide practical guidelines for CT string management in the field. The study provides a fundamental understanding of CT LCF and introduces novel algorithms in plasticity and damage.","PeriodicalId":178780,"journal":{"name":"Day 1 Mon, March 22, 2021","volume":"82 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133694023","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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