基于大数据的智慧油田装备健康管理方法

宋士吉; 杨乐; 潘旭冉; 韩益增; 黄众

doi:10.12405/j.issn.2097-1486.2022.02.005

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基于大数据的智慧油田装备健康管理方法

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- 基于大数据的智慧油田装备健康管理方法
- Smart oilfield equipment health management method based on big data
- 新兴科学和技术趋势 2022年1卷第2期页码：178-192
- 作者机构：
  
  1.清华大学自动化系，北京　100084
  2.西安交通大学信息与通信工程学院，陕西　西安　710049
  3.重载快捷大功率电力机车全国重点实验室，中车株洲电力机车有限公司，湖南　株洲　412001
- 作者简介：
  
  [ "宋士吉，清华大学自动化系教授、博士生导师，教育部长江学者特聘教授，自动化系工业智能与系统研究所所长。兼任中国指挥与控制学会无人系统专委会副主任、中国运筹学会智能工业数据解析与优化专委会常务理事、中国海洋学会深海技术分会副理事长等职务。长期从事复杂系统建模优化、复杂生产线优化调度、供应网络库存管理与配送路径优化、机器学习与强化学习理论方法及其应用研究。在随机鲁棒建模与优化、深度学习/迁移学习/强化学习理论与方法等研究领域有不俗表现。已在国内外重要学术期刊及学术会议发表论文240余篇，其中IEEE Transactions系列期刊长文、AUTOMATICA、EUROPEAN Journal of Operational Research国内外著名期刊SCI检索论文150余篇。先后主持了国家自然科学基金重大科学仪器研制项目“深海可控式可视采样器关键技术研究与样机研制”、科技部新一代人工智能重大基础前沿项目“复杂制造环境下的协同控制与决策理论方法”；主持完成国家自然科学基金重点项目2项、中国大洋协会国际海域调查与开发重点项目等累计50余项。研究成果获得中国自动化学会自然科学一等奖、中国人工智能学会自然科学一等奖、教育部自然科学二等奖、中国自动化学会教学成果一等奖、英国皇家工程院授予杰出访问会士奖等。E-mail:shijis@mail.tsinghua.edu.cn" ]
- 基金信息：
  
  中国工程院重点咨询项目(2019-XZ-17)
- DOI：10.12405/j.issn.2097-1486.2022.02.005
  中图分类号： TP311.13
- 纸质出版日期：2022-12，
  
  收稿日期：2022-08-10，
  
  修回日期：2022-09-22，
- 稿件说明：
移动端阅览
宋士吉, 杨乐, 潘旭冉, 等. 基于大数据的智慧油田装备健康管理方法[J]. 新兴科学和技术趋势, 2022,1(2):178-192.

SONG Shiji, YANG Le, PAN Xuran, et al. Smart oilfield equipment health management method based on big data. [J]. Emerging Science and Technology, 2022,1(2):178-192.
宋士吉, 杨乐, 潘旭冉, 等. 基于大数据的智慧油田装备健康管理方法[J]. 新兴科学和技术趋势, 2022,1(2):178-192. DOI： 10.12405/j.issn.2097-1486.2022.02.005.

SONG Shiji, YANG Le, PAN Xuran, et al. Smart oilfield equipment health management method based on big data. [J]. Emerging Science and Technology, 2022,1(2):178-192. DOI： 10.12405/j.issn.2097-1486.2022.02.005.

摘要

油田资源勘探过程是一个复杂的大系统，包括油气生产全过程的数据采集、处理、分析、存储、应用等多个环节，生产过程数据复杂、异构，具有数据不完备和时空分布等特征。本文探讨冗余数据处理技术、缺失数据处理技术、分布式数据融合技术的特点，结合油气生产过程的典型案例给出的实际数据，给出复杂、异构且不完备数据处理方法在油田生产设备状态监测、诊断及预测中的应用论证评估。进而利用复杂异构数据的分布式数据库管理技术，建立生产设备状态监测、诊断及预测的数据资源管理框架、数据处理流程及相关数据技术规范。

Abstract

The process of oil field resource exploration is a complex large-scale system

including multiple links of data acquisition

processing

analysis

storage and application in the whole process of oil and gas production. The data in the production process is complex and heterogeneous

with the characteristics of incomplete data and temporal and spatial distribution. The paper discusses the characteristics of redundant data processing technology

missing data processing technology and distributed data fusion technology. Combined with the actual data given by typical cases of oil and gas production process

the paper gives the application demonstration and evaluation of complex

heterogeneous and incomplete data processing methods in condition monitoring

diagnosis and prediction of oilfield production equipment. Then

using the distributed database management technology of complex heterogeneous data

the data resource management framework

data processing flow and relevant data technical specifications for production equipment condition monitoring

diagnosis and prediction are established.

关键词

油田资源勘探数据融合分布式数据库管理

Keywords

oil field resource explorationdata fusiondistributed database management

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