大数据分析方法在机采井评价中的应用  

Application of Big Data Analysis Method in Evaluation of Mechanical Recovery Well

作　　者：刘洋[1] 宋彦生 LIU Yang;SONG Yan-sheng(China Northwest Oilfield Company of China Petroleum and Chemical Corporation,Urumqi 830011,China;College of Pipeline and Civil Engineering,China University of Petroleum(East China),Qingdao 266580,China)

机构地区：[1]中国石油化工股份有限公司西北油田分公司,乌鲁木齐830011 [2]中国石油大学(华东)储运与建筑工程学院,青岛266580

出　　处：《价值工程》2020年第23期246-248,共3页Value Engineering

摘　　要：由于影响机采井系统效率的因素很多,主要因素并不十分明确,在评价油井的生产和工作过程中,大部分石油公司没有一个系统、全面的评价体系。论文基于塔河油田机采井生产数据库的海量数据,开发了大数据分析软件,通过灰色关联分析理论,确定了影响塔河油田机采井系统效率的关联指标,并给出了沉没度、泵效、回压等指标的控制范围,在指标控制范围内可提高机采井的系统效率,对机采井的生产控制有着重要意义,也可为石油其它领域数据分析提供借鉴。Because there are many factors that affect the efficiency of a mechanical recovery well system,the main factors are not very clear.In the process of evaluating the production and work of oil wells,most oil companies do not have a systematic and comprehensive evaluation system.Based on the massive data of the mechanical recovery well production database in Tahe Oilfield,this paper develops big data analysis software.Through the grey correlation analysis theory,the relevant indicators affecting the efficiency of the mechanical recovery well system in Tahe Oilfield are determined,and gives the control range of the index of submergence depth,pump efficiency,back pressure,etc.Within the control range of this index,the system efficiency of the mechanical recovery well can be improved,which is of great significance to the production control of the mechanical recovery well,and can also provide reference for data analysis in other fields of petroleum.

关 键 词：机采井 效率 评价指标 大数据 

分 类 号：TE355[石油与天然气工程—油气田开发工程]