A review on smart platform and application analytics managing big data of oil and gas. (9th March 2023)

• Record Type: Journal Article
• Authors: Zeng, Ying; Wang, Lijun; Shi, Guowei; Zhi, Zhiying
• Is Part Of: International journal of mining and mineral engineering. Volume 13:Number 3(2022)
• Page Start: 205
• Page End: 230
• Publication Date: 2023-03-09
• Subjects: artificial intelligence -- big data -- data mining -- exploring and monitoring -- oil and gas; Mining engineering -- Periodicals; Mines and mineral resources -- Periodicals; 622.05
• Languages: English
• ISSNs: 1754-890X

Abstract

In exploring and monitoring of the oil and gas industry data, extensive analysis of artificial intelligence (AI) methods can play a vital role in enhancing and optimising the application of the 'smart oil and gas field (SOGF)' framework. The paper presents an intelligent platform for managing foreign and domestic oil and gas big data, along with a general big data '6V' application and features. An idealistic comprehensive framework and fundamental process of oil and gas big data platform are presented. Supporting this platform, an intelligent hybrid cloud storage computing architecture integrating Hadoop, Storm, and Spark for the SOGF is also presented. Multiple data cleaning and fusion techniques integrating oil company databases are also presented to enhance the data mining process. Finally, a review of existing AI approaches is summarised with consistent diagrammatic approaches. Future work includes joint complex virtual service functioning approaches with deep learning techniques to enhance SOGF.