连续油管塑性与疲劳物理建模的最新进展

Zhanke Liu, S. Tipton, D. Sukumar
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摘要

连续油管(CT)的完整性对于现场修井作业至关重要。为了监测和管理油管的完整性,在过去的几十年里,该行业开发了许多计算机模型。其中,低周疲劳(LCF)建模对于保证油管的完整性起着至关重要的作用。连续油管串的LCF建模可以追溯到20世纪80年代。最近,新的算法促进了油管疲劳和塑性物理建模的发展。连续油管进出井时,会在高压差下进行弯曲-矫直循环。这种恶劣的条件会导致低循环或超低循环疲劳,限制了连续油管的使用寿命。本研究提出的模型是在前人模型的基础上,基于严格推导的材料参数来计算大范围加载条件下状态变量的演化。通过新制定的塑性和应变参数,基于物理的损伤模型可以预测连续油管的疲劳寿命、直径增长和管壁变薄。修正后的建模方法给出了实际现场条件下CT损伤累积、直径增长和管壁减薄的结果,并得到了实验验证。对于20种不同的连续油管合金,在超过4500个数据点的恒压数据集上,该模型的总体精度提高了18.8%,一致性提高了14.0%。建模结果提供了对疲劳损伤积累的非线性本质的见解。该研究为指导未来的分析建模和实验研究提供了建议,总结了基于物理的LCF建模的理论发现,并为现场的连续油管管柱管理提供了实用指南。该研究提供了对CT LCF的基本理解,并引入了塑性和损伤方面的新算法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Latest Development in Physics-Based Modeling of Coiled Tubing Plasticity and Fatigue
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.
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