超深井钻机绳轮疲劳寿命预测

doi:10.3969/j.issn.1006-6535.2025.03.016

特种油气藏 ›› 2025, Vol. 32 ›› Issue (3): 133-141.DOI: 10.3969/j.issn.1006-6535.2025.03.016

超深井钻机绳轮疲劳寿命预测

易先中^1,2, 秦赛博¹, 刘航铭³, 乐容昌¹, 许志新¹, 皮运松⁴, 蔡星星⁴, 冀玉松⁴

1.长江大学机械工程学院,湖北荆州 434023;
2.湖北省智能油气钻釆装备企校联合创新中心,湖北荆州 434000;
3.湖北省地质局第七地质大队,湖北宜昌 443000;
4.湖北江汉石油仪器仪表股份有限公司,湖北武汉 430205

收稿日期:2024-09-22 修回日期:2025-02-05 出版日期:2025-06-25 发布日期:2025-07-08
通讯作者: 刘航铭(1991—),男,工程师,2015年毕业于长江大学机械制造及其自动化专业,2023年毕业于该校石油矿场机械专业,获博士学位,现从事油气钻采机械设计及钻完井技术研究。
作者简介:易先中(1963—),男,教授,博士生导师,1983年毕业于大庆石油学院石油矿场机械专业,2004年毕业于石油大学(北京)油气井工程专业,获博士学位,现从事油气钻采机械及其智能化研究工作。
基金资助:
国家自然科学基金“地面旋转导向钻井新方法及其控制机理”(51974035)

Fatigue life prediction of sheave wheels for ultra-deep well drilling rigs

YI Xianzhong^1,2, QIN Saibo¹, LIU Hangming³, YUE Rongchang¹, XU Zhixin¹, PI Yunsong⁴, CAI Xingxing⁴, JI Yusong⁴

1. School of Mechanical Engineering, Yangtze University, Jingzhou, Hubei 434023, China;
2. Hubei Provincial Enterprise and University Joint Innovation Center for Intelligent Oil and Gas Drilling and Production Equipment, Jingzhou, Hubei 434000, China;
3. The Seventh Geological Brigade of Hubei Geological Bureau, Yichang, Hubei 443000, China;
4. Hubei Jianghan Petroleum Instrument & Meter Co., Ltd., Wuhan, Hubei 430205, China

Received:2024-09-22 Revised:2025-02-05 Online:2025-06-25 Published:2025-07-08

摘要/Abstract

摘要： 针对钻机起升系统重要部件死绳固定器疲劳寿命认识不清的问题,以ZJ150钻机用JZG97型死绳固定器为例,使用有限元法进行了疲劳寿命分析,通过正交实验对死绳固定器薄弱环节(绳轮)处的结构参数进行优选,并建立BP神经网络疲劳寿命预测模型,预测薄弱部位的疲劳寿命。结果表明:肋板宽度为90 mm、传感器支臂过渡圆角半径为270 mm、肋板厚度为230 mm为最优参数组合,优化后绳轮的疲劳安全系数为1.78,与初始结构相比提高了19.46%。BP神经网络模型整体拟合准确率为97.84%,预测值与模拟值最大误差小于4.870%,BP神经网络具有准确率高、收敛速度快、泛化能力强的特点,可以在不构建复杂函数回归关系的情况下,准确预测死绳固定器的疲劳寿命。该研究对超深井钻井设备死绳固定器的结构优化设计及寿命预测具有一定的指导意义。

关键词: 死绳固定器, 绳轮, 疲劳寿命, 正交实验, BP神经网络

Abstract: To address the issue of unclear understanding of the fatigue life of critical components in the drilling rig hoisting system, specifically the deadline anchor, the fatigue life analysis was conducted using the finite element method, taking the JZG97 type deadline anchor used in the ZJ150 drilling rig as an example. Through orthogonal experiments, the structural parameters of the weak link (sheave wheel) of the deadline anchor were optimized. Furthermore, a BP neural network fatigue life prediction model was established to predict the fatigue life of the vulnerable parts. The results show that the optimal parameters are a rib width of 90 mm, a sensor arm fillet radius of 270 mm, and a rib thickness of 230 mm. This increases the sheave wheel′s fatigue safety factor to 1.78, a 19.46% improvement over the initial design. The BP neural network model has a fitting accuracy of 97.84% and a maximum error of less than 4.870% between the predicted value and the simulated value. It proves to be accurate, fast-converging, and generalizable, enabling reliable fatigue life prediction of the deadline anchor without constructing complex functional regression relationships. This research offers guidance for structural optimization and life prediction of deadline anchors in ultra-deep well drilling equipment.

Key words: deadline anchor, sheave wheel, fatigue life, orthogonal experiment, BP neural network

中图分类号:

TE922

易先中, 秦赛博, 刘航铭, 乐容昌, 许志新, 皮运松, 蔡星星, 冀玉松. 超深井钻机绳轮疲劳寿命预测[J]. 特种油气藏, 2025, 32(3): 133-141.

YI Xianzhong, QIN Saibo, LIU Hangming, YUE Rongchang, XU Zhixin, PI Yunsong, CAI Xingxing, JI Yusong. Fatigue life prediction of sheave wheels for ultra-deep well drilling rigs[J]. Special Oil & Gas Reservoirs, 2025, 32(3): 133-141.

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超深井钻机绳轮疲劳寿命预测

Fatigue life prediction of sheave wheels for ultra-deep well drilling rigs

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