Proceedings of the 2024 6th International Conference on Civil Architecture and Urban Engineering (ICCAUE 2024)

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Fast Drilling Technology Application of Artificial Intelligence Algorithm in Shunbei Area

Authors
Faqiang Luo1, 2, *, Xiuping Chen2, Yajun Zhang3, Guangming Qin3, Shaokun Luo3
1Petroleum Engineering Technology Research Institute, Sinopec Northwest Oilfield Company, Urumqi, Xinjiang Province, 830011, China
2Sinopec Key Laboratory of Enhanced Oil Recovery for Fractured Vuggy Reservoirs, Urumqi, Xinjiang Province, 830011, China
3HYBRIT ENERGY, Shenzhen, Guangdong Province, 518100, China
*Corresponding author. Email: luofq.xbsj@sinopec.com
Corresponding Author
Faqiang Luo
Available Online 30 April 2025.
DOI
10.2991/978-94-6463-688-8_21How to use a DOI?
Keywords
artificial intelligence; drilling acceleration; machine learning; data analysis; real-time monitoring system
Abstract

This study investigates the utilization of artificial intelligence (AI) algorithms to enhance drilling efficiency in the Shunbei oil field. By integrating machine learning and data analysis technologies, we can optimize the drilling process, minimize non-productive time, and improve overall productivity. Various algorithms such as neural networks, decision trees, and support vector machines were employed to analyze a substantial volume of historical drilling data in this research. The results demonstrate that AI algorithms accurately predict potential issues during drilling operations and offer optimization solutions. Furthermore, a real-time monitoring system has been developed capable of dynamically adjusting parameters during drilling operations, thereby reducing human intervention and increasing automation levels. Experimental findings indicate that the implementation of AI technology significantly reduces drilling costs while enhancing speed compared to conventional methods. These research outcomes provide a novel technical approach for oil field drilling engineering with promising prospects for broader future applications.

Copyright
© 2025 The Author(s)
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 2024 6th International Conference on Civil Architecture and Urban Engineering (ICCAUE 2024)
Series
Advances in Engineering Research
Publication Date
30 April 2025
ISBN
978-94-6463-688-8
ISSN
2352-5401
DOI
10.2991/978-94-6463-688-8_21How to use a DOI?
Copyright
© 2025 The Author(s)
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

risenwbib
TY  - CONF
AU  - Faqiang Luo
AU  - Xiuping Chen
AU  - Yajun Zhang
AU  - Guangming Qin
AU  - Shaokun Luo
PY  - 2025
DA  - 2025/04/30
TI  - Fast Drilling Technology Application of Artificial Intelligence Algorithm in Shunbei Area
BT  - Proceedings of the 2024 6th International Conference on Civil Architecture and Urban Engineering (ICCAUE 2024)
PB  - Atlantis Press
SP  - 192
EP  - 203
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-688-8_21
DO  - 10.2991/978-94-6463-688-8_21
ID  - Luo2025
ER  -