Volume 14 Number 4 (Apr. 2019)
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JCP 2019 Vol.14(4): 268-282 ISSN: 1796-203X
doi: 10.17706/jcp.14.4.268-282

Cloud Computing and Big Data for Oil and Gas Industry Application, China

Yang Zhifeng1,2,3, Han Fei4, Feng Xuehui3, Yuan Qi3, Cao Zhen3, Zhang Yidan5
1Postdoctoral Workstation, Xinjiang Oilfield Company, Petrochina, Karamay, Xinjiang, China.
2State Key Laboratory of Petroleum Resource and Prospecting, China University of Petroleum, Beijing, China.
3Sugon Information Industry Co., Ltd., Beijing, China.
4Lenovo (Beijing) Co., Ltd., Beijing, China.
5Xinjiang Oilfield Company, petroChina, Karamay, Xinjiang, China.

Abstract—The oil and gas industry is a complex data-driven industry with compute-intensive, data-intensive and business-intensive features. Cloud computing and big data have a broad application prospect in the oil and gas industry. This research aims to highlight the cloud computing and big data issues and challenges from the informatization in oil and gas industry. In this paper, the distributed cloud storage architecture and its applications for seismic data of oil and gas industry are focused on first. Then,cloud desktop for oil and gas industry applications are also introduced in terms of efficiency, security and usability. Finally, big data architecture and security issues of oil and gas industry are analyzed. Cloud computing and big data architectures have advantages in many aspects, such as system scalability, reliability, and serviceability. This paper also provides a brief description for the future development of Cloud computing and big data in oil and gas industry. Cloud computing and big data can provide convenient information sharing and high quality service for oil and gas industry.

Index Terms—Big data, cloud computing, cloud desktop, oil and gas industry, china.

[PDF]

Cite: Yang Zhifeng, Han Fei, Feng Xuehui, Yuan Qi, Cao Zhen, Zhang Yidan, "Cloud Computing and Big Data for Oil and Gas Industry Application, China," Journal of Computers vol. 14, no. 4, pp. 268-282, 2019.

General Information

ISSN: 1796-203X
Abbreviated Title: J.Comput.
Frequency: Monthly
Editor-in-Chief: Prof. Liansheng Tan
Executive Editor: Ms. Nina Lee
Abstracting/ Indexing: DBLP, EBSCO,  ProQuest, INSPEC, ULRICH's Periodicals Directory, WorldCat, CNKI,etc
E-mail: jcp@iap.org
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