基于人工神经网络的连续管疲劳分析软件开发

打开文本图片集

文章编号：1001-3482（2025）05-0001-06

关键词：连续管；疲劳；人工神经网络；软件开发；混合编程中图分类号：TE933.8 文献标志码：A doi：10.3969/j.issn.1001-3482.2025.05.001

Abstract： To address the issue of fatigue damage caused by pressure and bending during the operation of coiled tubing，a multi-layer backpropagation （BP） artificial neural network was constructed in Matlab by considering five key factors influencing the fatigue lifeof coiled tubing，namely yield strength，outer diameter，wall thickness，bending radius，and internal pressure.This neural network was trained through massive data to achieve a highly accurate fatigue life prediction model for coiled tubing.The model was encapsulated intoa dynamic link library file available forC# invocation.Based on theVS platform and the C#.NET framework，a coiled tubing fatigue analysis software was successully developed.The software had a user-friendly interface，enabling full-size fatigue damage calculation of coiled tubing，visualization of fatigue distribution，and management of historicaldata.Practical applications demonstrate that the results computed by the independently developed software are comparable to those of authoritative industry tools and closely align with field evaluations.This achievement provides a robust tool for the safety management and preventive maintenance of coiled tubing， ofering significant practical value in enhancing safe operations in oil and gas fields.

Key words ：coiledtubing; fatigue;artificial neural network; softwaredevelopment; hybrid programming

连续管是一种能够缠绕在大直径滚筒上进行工作的无缝钢管，凭借其优越的柔韧性和强度，能够适应复杂的井下环境，不仅可以显著提高作业效率，还能有效降低成本[1]。（剩余5114字）