基于悬点载荷的抽油杆断脱原因诊断研究  

Cause Analysis of Rod Parting Based on Polished Rod Load

作　　者：吕萌萌 陈彧 向华[3] 马卫国[4] 刘少胡[4] 冉小丰[4] 曲宝龙[1] LüMengmeng;Chen Yu;Xiang Hua;Ma Weiguo;Liu Shaohu;Ran Xiaofeng;Qu Baolong(College of Chemistry&Environment Engineering,Yangtze University;Sinopec Jiangsu Oilfield Company;College of Computer Science,Yangtze University;School of Mechanical Engineering,Yangtze University)

机构地区：[1]长江大学化学与环境工程学院,湖北省荆州市434020 [2]中国石化江苏油田 [3]长江大学计算机科学学院 [4]长江大学机械工程学院

出　　处：《石油机械》2024年第12期110-117,共8页China Petroleum Machinery

基　　金：湖北省高等学校优秀中青年科技创新团队计划项目“连续管智能控制与安全评定”(T2021035);中国石化江苏油田分公司科研项目“江苏油田抽油杆断脱机理研究及防治技术”(31450001-19-FW2099-0029)。

摘　　要：目前抽油杆断脱研究集中于断脱后的机理分析,对断脱预防较少涉及;井下工况故障诊断也大多依赖于示功图,无法诊断井筒摩阻影响。为此,以抽油杆力学模型为基础,分析故障井悬点载荷理论计算结果与现场值差异,提出修正力学模型,采用BP神经网络方法对影响因子进行预测、回归和验证,实现修正结果与现场结果匹配,进而对井下异常工况进行诊断。研究结果表明:训练后的BP神经网络模型结构简单,易于操作,诊断效率高,预测精度高,拟合优度高达88.96%以上,修正力学模型计算结果与现场实测值符合良好。针对14口故障井的计算,结果偏差均在10.31%以内。研究结论可为抽油杆井下断脱故障诊断提供理论支撑。At present,the rod parting research mainly focuses on the mechanism analysis after parting,with less involvement in parting prevention.The fault diagnosis of downhole working conditions mostly relies on indicator diagrams,which cannot diagnose the influence of wellbore friction.To solve these problems,based on the mechanical model of the sucker rod,the differences between the theoretical calculation results and the field values of the polished rod loads of the fault wells were analyzed,and a modified mechanical model was proposed.The BP neural network method was used to conduct prediction,regression and verification on the influential factors,and the matching of the modified results with the field results was achieved.Finally,a diagnosis was conducted on the abnormal downhole working conditions.The research results show that the trained BP neural network model has a simple structure,is easy to operate,and has a diagnostic efficiency and high prediction accuracy,with a goodness of fit of over 88.96%.The calculated results of the modified mechanical model are in good agreement with the field measured values.The bias between the calculated results of 14 fault wells and the field measured results is all within 0.31%.The research conclusions provide theoretical support for the diagnosis of downhole rod parting faults.

关 键 词：抽油杆断脱 悬点载荷 力学模型 BP神经网络 故障诊断 影响因子 

分 类 号：TE933[石油与天然气工程—石油机械设备]