Title: A review on smart platform and application analytics managing big data of oil and gas

Authors: Ying Zeng; Lijun Wang; Guowei Shi; Zhiying Zhi

Addresses: Xinjiang Oil Field Company, PetroChina, Karamay 830023, Xinjiang, China ' Xinjiang Oil Field Company, PetroChina, Karamay 830023, Xinjiang, China ' Xinjiang Oil Field Company, PetroChina, Karamay 830023, Xinjiang, China ' Xinjiang Oil Field Company, PetroChina, Karamay 830023, Xinjiang, China

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.

Keywords: artificial intelligence; big data; data mining; exploring and monitoring; oil and gas.

DOI: 10.1504/IJMME.2022.129528

International Journal of Mining and Mineral Engineering, 2022 Vol.13 No.3, pp.205 - 230

Received: 12 Apr 2022
Accepted: 27 Sep 2022
Published online: 13 Mar 2023 *

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