Application of Big Data Analysis for Energy Consumption Standards Establishment of Oil Wells

Yun Liu; Tingting Liu; Xingping Cheng; Weiyi Xie; Runfang Miao; Shan Gao; Fengjiao Qu; Zhiyong Liu; Fei Zhao; Lihong Du

doi:10.2991/978-94-6463-064-0_90

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Application of Big Data Analysis for Energy Consumption Standards Establishment of Oil Wells

Authors

Yun Liu¹^{, *}, Tingting Liu¹, Xingping Cheng², Weiyi Xie¹, Runfang Miao³, Shan Gao¹, Fengjiao Qu¹, Zhiyong Liu⁴, Fei Zhao¹, Lihong Du¹

¹Engineering Technology Research Institute of Huabei Oilfield Company, Huaibei, China

²Economic and Technical Research Institute of Huabei Oilfield Company, Huaibei, China

³Sales Department of Huabei Oilfield Company, Huaibei, China

⁴The Fifth Factory of Huabei Oilfield Company, Huaibei, China

^*Corresponding author. Email: yjy_liuyun@petrochina.com.cn

Corresponding Author

Yun Liu

Available Online 27 December 2022.

DOI: 10.2991/978-94-6463-064-0_90 How to use a DOI?
Keywords: Big data analysis; Gray correlation analysis; Energy consumption
Abstract: The big data analysis of oil wells uses the historical production data of the oilfield to find out the internal relationship between the data, to guide on-site production and to realize energy consumption reduction. Due to the huge of wells’ historical data the big data analysis methods such as descriptive analysis, matrix correlation, grouping analysis and gray correlation analysis have been used to analyse influencing factors and get the key indicators affecting energy consumption. The key influence factors of energy consumption have been determined by the big data analysis, which provides scientific data support for measure wells selection and effect prediction. At the same time, according to the threshold and energy consumption conditions, the pumping parameters and working conditions have been optimized. At present, this method has been applied to 340 oil wells in Huabei oilfield and achieved a remarkable energy conservation effect.
Open Access: Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title: Proceedings of the 2022 3rd International Conference on Big Data and Social Sciences (ICBDSS 2022)
Series: Atlantis Highlights in Computer Sciences
Publication Date: 27 December 2022
ISBN: 10.2991/978-94-6463-064-0_90
ISSN: 2589-4900
DOI: 10.2991/978-94-6463-064-0_90 How to use a DOI?
Open Access: Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

ris enw bib

TY  - CONF
AU  - Yun Liu
AU  - Tingting Liu
AU  - Xingping Cheng
AU  - Weiyi Xie
AU  - Runfang Miao
AU  - Shan Gao
AU  - Fengjiao Qu
AU  - Zhiyong Liu
AU  - Fei Zhao
AU  - Lihong Du
PY  - 2022
DA  - 2022/12/27
TI  - Application of Big Data Analysis for Energy Consumption Standards Establishment of Oil Wells
BT  - Proceedings of the 2022 3rd International Conference on Big Data and Social Sciences (ICBDSS 2022)
PB  - Atlantis Press
SP  - 883
EP  - 890
SN  - 2589-4900
UR  - https://doi.org/10.2991/978-94-6463-064-0_90
DO  - 10.2991/978-94-6463-064-0_90
ID  - Liu2022
ER  -

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