石油学报 ›› 2003, Vol. 24 ›› Issue (4): 103-107.DOI: 10.7623/syxb200304024

• 石油工程 • 上一篇    下一篇

潜油电泵机组可靠性研究

张玉斌, 于海春   

  1. 大庆石油学院机械系, 黑龙江大庆, 163318
  • 收稿日期:2002-05-20 修回日期:2002-08-26 出版日期:2003-07-25 发布日期:2010-05-21
  • 作者简介:张玉斌,男,1945年9月生,1969年毕业于东北石油学院石油矿场机械专业,现为大庆石油学院机械系教授,长期从事石油天然气机械装备可靠性领域科研和教学工作.

Reliability of electrical submersible pumping unit

ZHANG Yu-bin, YU Hai-chun   

  1. Department of Mechanical Engineering, Daqing Petroleum Institute, Daqing 163318, China
  • Received:2002-05-20 Revised:2002-08-26 Online:2003-07-25 Published:2010-05-21

摘要: 对电泵井故障模式的调查和统计表明,潜油电泵机组使用寿命是影响电泵井检修周期的主要因素.根据机组中各部件逻辑功能关系,构建了其可靠性模型为串联系统.采用常规假设检验和模糊识别相结合的方法,对子系统故障数据统计分布的多个备择假设分布进行统计推断,使子系统寿命统计模型具有较高的可信度.运用Monte Carlo统计模拟法,对系统可靠性进行了数字仿真,得到潜油电泵机组系统寿命威布尔分布统计模型,实现了对系统使用可靠性的定量评价.通过多项式拟合建立起产液量、含水率及泵挂深度与潜油电泵机组检泵周期之间的函数表达式,能定量描述各影响因素与检泵周期的相关性.

关键词: 潜油电泵, 使用寿命, 故障, 统计模型, 可靠性, 定量评价, 数字仿真

Abstract: The lifetime of the electrical submersible pumping unit is the main factor affecting the overhaul period of electrical pumping unit.On the basis of the logic relation of all members of a pumping unit,a reliability model for electrical submersible pumping unit was developed.Adopting ordinary suppose test and faintness identification,the multi-selected suppose distribution in the digital failure simulation of the subsystem was counted up and concluded,and a statistical model with high reliability of the subsystem was obtained.The Monte-Carlo statistical model was used to do digital simulation to the system reliability,and the statistical lifetime Weibull distribution model for the electrical submersible pumping unit was obtained.The system reliability assessment may be realized using the statistical model.The function expression of the liquid production,water cut or moisture rate and the pump depth with the overhaul period of the electrical pumping unit was developed and applied to describe the relativity of each affecting factor with the overhaul period of electrical pumping unit.

Key words: electrical submersible pumping unit, lifetime, failure, statistical model, reliability, quantitative assessment, digital simulation

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