A model designed for HSE big data analysis in petroleum industry
Title
A model designed for HSE big data analysis in petroleum industry
Subject
Petroleum industry
Gasoline
Big data
Accidents
Digital storage
Accident prevention
Data acquisition
Data handling
Data mining
Data Analytics
Data warehouses
Description
HSE (Health, Safety, and Environment) management is one of the most concerned matters of every business, especially in petroleum Industry. Currently, analyzing the origin of accident and tracing the responsibility of accident commonly happened after the accident due to the lack of analytical theories, methods and models. This paper presents a HSE big data analysis framework which is capable of analyzing historical data of HSE management to promote the practicality and scientificity of HSE management. This paper has done much research of HSE data analytics. Based on the features of HSE management in petroleum Industry, it elaborate the open source projects and its applicable scenes in data analytics. Then, it gives suggestions of choosing open source projects to establish data analytics platform under given conditions. Last, by using data warehouse, data mining, machine learning and pattern identification technology, a HSE big data analytics framework was presented in this paper. This framework includes the level of data acquisition, data storage, data processing, data analysis, and data application. The efficient use of this model can help to untangle doubts of HSE big data analytics, discover the regularity and characteristics of accidents, and enhance supervision and warning of safety production. 2019, International Petroleum Technology Conference
Creator
Wu, Tongxin
Mao, Yaming
Zhao, Gang
Publisher
International Petroleum Technology Conference 2019, IPTC 2019, March 26, 2019 - March 28, 2019
Date
2019
Type
conferencePaper
Identifier
10.2523/19508-ms
Citation
Wu, Tongxin, Mao, Yaming, and Zhao, Gang, “A model designed for HSE big data analysis in petroleum industry,” Lamar University Midstream Center Research, accessed May 18, 2024, https://lumc.omeka.net/items/show/29354.