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Application of MES System in Offshore Oil and Gas Field Production Management

Yong Chen1,*, Weihua Lan1, Chong Wang2

China National Offshore Oil Corporation China Limited, Shenzhen Branch, Shenzhen, 518000, China.
Department of Engineering Mechanics, State Marine Technical University of St. Petersburg, St. Petersburg, 190008, Russia.

*Corresponding Author: Yong Chen. Email: .

Journal on Big Data 2019, 1(2), 47-54.


In order to solve information island problem of offshore oil and gas field production-related information system, including repetitive reporting and input of data, data isolation of central control system, inadequate follow-up analysis and development to support oil and gas field production management, and so on. Therefore, the introduction of MES (Manufacturing Execution System) production execution system in the manufacturing industry and downstream production of offshore oil has become an inevitable choice. This system utilizes the real-time database combined with relational database to collect the scattered structured data, such as the production process real-time data, production management documents and statistical tables of offshore oil and gas production facilities. It establishes a unified data center platform for each operation area and production site, so as to centralize the related production management data at production sites. This system realizes many functions, including the production management support like production report generation as well as presentation and POB, equipment management support like PM optimization, remote configuration and monitoring of production operation, status trend analysis, and production event prediction. The implementation of MES system in offshore oil and gas field production management perfects the overall information system of China National Offshore Oil Corporation more and gradually enhances its comprehensive benefits.


Cite This Article

Y. Chen, W. Lan and C. Wang, "Application of mes system in offshore oil and gas field production management," Journal on Big Data, vol. 1, no.2, pp. 47–54, 2019.

This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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