Research Method and Practice of Artificial Intelligence Drilling Technology

YANG Chuanshu; LI Changsheng; SUN Xudong; HUANG Liming; ZHANG Haolin

doi:10.11911/syztjs.2020136

Volume 49 Issue 5

Oct. 2021

Turn off MathJax

Article Contents

Abstract

References

Petroleum Drilling Techniques > 2021 > 49(5): 7-13. > DOI: 10.11911/syztjs.2020136

YANG Chuanshu, LI Changsheng, SUN Xudong, HUANG Liming, ZHANG Haolin. Research Method and Practice of Artificial Intelligence Drilling Technology[J]. Petroleum Drilling Techniques, 2021, 49(5): 7-13. DOI: 10.11911/syztjs.2020136

Citation:

PDF (1789 KB)

Research Method and Practice of Artificial Intelligence Drilling Technology

Sinopec Research Institute of Petroleum Engineering, Beijing, 102206, China

More Information

Received Date: October 14, 2020
Revised Date: March 09, 2021
Available Online: March 22, 2021

Graphical Abstract

Abstract

Abstract

With the rapid development of artificial intelligence (AI) technology, it has made remarkable breakthroughs in many fields. However, the application of AI in drilling engineering is still in the primary stage. In order to promote the application of AI technology in drilling, based on a brief description of the research situation of its application in drilling engineering, a “three-wheels drive” methodology for the specific application of AI technology in drilling area was proposed. Then, business application scenarios and AI technology tools suitable for the research of AI in drilling engineering were analyzed. After putting forward a method of evaluating and optimizing projects based on the methodology with examples, the research process of AI application in drilling was illustrated by the real-time diagnosis of complex downhole failures. Finally, the shortcomings were identified and suggestions were given for the application of AI in drilling engineering, so as to promote the development of AI drilling technology.
- drilling,
- artificial intelligence,
- big data,
- the “three-wheels drive” methodology,
- downhole failure

FullText(HTML)

References (13)

References

[1]	李开复, 王咏刚．人工智能[M]．北京: 文化发展出版社, 2017: 188–290． LEE Kaifu, WANG Yonggang. Artificial intelligence[M]. Beijing: Cultural Development Press, 2017: 188-290.
[2]	ALOUHALI R, ALJUBRAN M, GHARBI S, et al. Drilling through data: automated kick detection using data mining[R]. SPE 193687, 2018.
[3]	POLLOCK J, STOECKER-SYLVIA Z, VEEDU V, et al. Machine learning for improved directional drilling[R]. OTC 28633, 2018.
[4]	PHAN D T, LIU Chao, ALTAMMAR M J, et al. Application of artificial intelligence to predict time-Ydependent safe mud weight windows for inclined wellbores[R]. IPTC 19900, 2020.
[5]	管志川,胜亚楠,许玉强,等. 基于PSO优化BP神经网络的钻井动态风险评估方法[J]. 中国安全生产科学技术,2017,13(8):5–11. GUAN Zhichuan, SHENG Ya’nan, XU Yuqiang, et al. Dynamic risk assessment method of drilling based on PSO optimized BP neural network[J]. Journal of Safety Science and Technology, 2017, 13(8): 5–11.
[6]	殷志明,刘书杰,谭扬,等. 基于机器学习的深水钻井大数据处理方法研究[J]. 海洋工程装备与技术,2019,6(11):446–453. YIN Zhiming, LIU Shujie, TAN Yang, et al. Research on outlier marking method of deepwater drilling big data in machine learning[J]. Ocean Engineering Equipment and Technology, 2019, 6(11): 446–453.
[7]	YIN Qishuai, YANG Jin, ZHOU Bo. et al. Improve the drilling operations efficiency by the big data mining of real-time logging[R]. SPE/IADC 189330, 2018.
[8]	林伯韬,郭建成. 人工智能在石油工业中的应用现状探讨[J]. 石油科学通报,2019,4(4):403–413. LIN Botao, GUO Jiancheng. Discussion on current application of artificial intelligence in petroleum industry[J]. Petroleum Science Bulletin, 2019, 4(4): 403–413.
[9]	李杰．工业人工智能[M]．刘宗长, 高虹安, 贾晓东, 译．上海: 上海交通大学出版社, 2019: 29-113． LEE Jay. Industrial artificial intelligence[M]. LIU Zongchang, GAO Hongan, JIA Xiaodong, translated. Shanghai: Shanghai Jiao Tong University Press, 2019.
[10]	李杰, 倪军, 王安正．从大数据到智能制造[M]．上海: 上海交通大学出版社, 2016． LEE Jay, NI Jun, WANG Anzheng. From bigdata to intelligent manufacturing[M]. Shanghai: Shanghai Jiao Tong University Press, 2016.
[11]	CSDN．人工智能算法总结[EB/OL]．[2019-12-26]．https://blog.csdn.net/JimBraddock/article/details/100376290.
[12]	CSDN．机器学习（三）: 时间序列法[EB/OL]．[2019-12-26]．https://blog.csdn.net/livan1234/article/details/80863021．
[13]	CSDN．10大热门人工智能开源工具（框架）[EB/OL]．[2019-12-26]．https://blog.csdn.net/zhangbijun1230/article/details/80714032．

Cited By

Cited by

Periodical cited type(32)

1.	孙虎，郭阳，张媛，喻建胜，陈思锦. 智能化钻井完井技术研究与工程实践. 钻采工艺. 2025(01): 46-54 .
2.	杨传书，王敏生，李昌盛，张洪宝，吴建军，付宣，袁多. 中国石化钻井工程决策支持系统进展及展望. 钻采工艺. 2025(01): 55-62 .
3.	李胜忠. 贝叶斯网络在钻井设备系统故障诊断中的应用. 兵工自动化. 2025(05): 85-89 .
4.	聂臻，夏朝辉，吴波鸿，黄雪琴. 中东地区碳酸盐岩油藏钻井工程技术现状及发展趋势. 石油钻探技术. 2024(01): 8-16 . 本站查看
5.	孙伟峰，冯剑寒，张德志，李威桦，刘凯，戴永寿. 结合LSTM自编码器与集成学习的井漏智能识别方法. 石油钻探技术. 2024(03): 61-67 . 本站查看
6.	王贺强，郭海涛，马翠岩，王子毓，陈友军，李斌，梁毅. 智能钻井系统在赵东油田的应用. 世界石油工业. 2024(03): 59-67 .
7.	刘明，许鹏，陈述，夏林，边建杰，张华. 四川盆地致密气水平井钻井关键技术. 非常规油气. 2024(04): 152-159 .
8.	杜松涛，杨晓峰，刘克强. 人工智能技术在钻井工程的应用与发展. 石油化工应用. 2024(06): 1-5+10 .
9.	卢聪，罗扬，郭建春，曾凡辉. 融合物理约束的压裂水平井产能智能预测框架构建与应用. 天然气工业. 2024(09): 99-107 .
10.	裴学良，黄哲. 胜利工程智能钻井关键技术探索与建议. 石油钻探技术. 2024(05): 62-68 . 本站查看
11.	王建龙，王越支，邱卫红，于琛，张菲菲，王学迎. 基于大数据与融合模型的钻井智能辅助决策系统. 石油钻探技术. 2024(05): 105-116 . 本站查看
12.	曾义金，王敏生，光新军，王果，张洪宝，陈曾伟，段继男. 中国石化智能钻井技术进展与展望. 石油钻探技术. 2024(05): 1-9+171 . 本站查看
13.	景明，王增祥. 数字孪生技术在实物地质资料工作中的应用场景探索. 中国矿业. 2024(S2): 145-148 .
14.	赵汩凡，李婧，光新军，马广军. 基于专利的钻井参数优化技术发展态势分析. 石油化工应用. 2024(12): 1-7 .
15.	付长民，王啸天，底青云. 智能导向钻井一体化软件系统平台研发. 地球物理学报. 2023(01): 139-152 .
16.	朱日祥，金之钧，底青云，杨长春，陈文轩，田飞，张文秀. 智能导钻技术体系与相关理论研发进展. 地球物理学报. 2023(01): 1-15 .
17.	廖璐璐，李根生，宋先知，冯连勇，高启超，程世忠. 我国脱碳路径与油公司能源转型策略研究. 石油钻探技术. 2023(01): 115-122 . 本站查看
18.	汝渴. 面向全智能石油钻机发展路径探讨. 化工管理. 2023(12): 136-139 .
19.	孙伟峰，刘凯，张德志，李威桦，徐黎明，戴永寿. 结合钻井工况与Bi-GRU的溢流与井漏监测方法. 石油钻探技术. 2023(03): 37-44 . 本站查看
20.	张好林，杨传书，李昌盛，王果，段继男. 钻井数字孪生系统设计与研发实践. 石油钻探技术. 2023(03): 58-65 . 本站查看
21.	李昌华，张学龄，杜小芸，郭志阳. 基于卷积神经网络的井漏预测. 实验室研究与探索. 2023(05): 102-106+142 .
22.	李根生，宋先知，祝兆鹏，田守嶒，盛茂. 智能钻完井技术研究进展与前景展望. 石油钻探技术. 2023(04): 35-47 . 本站查看
23.	李华洋，邓金根，谭强，冯永存，董保宏，曹志鹏，严科. 智能钻井技术应用体系构建及研究进展. 现代化工. 2023(10): 41-45+51 .
24.	郭建春，任文希，曾凡辉，罗扬，李宇麟，杜肖泱. 非常规油气井压裂参数智能优化研究进展与发展展望. 石油钻探技术. 2023(05): 1-7+179 . 本站查看
25.	孙金声，刘凡，程荣超，冯杰，郝惠军，王韧，白英睿，刘钦政. 机器学习在防漏堵漏中研究进展与展望. 石油学报. 2022(01): 91-100 .
26.	薛亮，戴城，韩江峡，杨明瑾，刘月田. 油藏渗流物理和数据联合驱动的深度神经网络模型. 油气地质与采收率. 2022(01): 145-151 .
27.	李中. 中国海油油气井工程数字化和智能化新进展与展望. 石油钻探技术. 2022(02): 1-8 . 本站查看
28.	陶宇龙. 智能钻井技术研究现状及发展趋势探究. 石油化工建设. 2022(02): 151-153 .
29.	杨传书. 数字孪生技术在钻井领域的应用探索. 石油钻探技术. 2022(03): 10-16 . 本站查看
30.	蒋廷学，周珺，廖璐璐. 国内外智能压裂技术现状及发展趋势. 石油钻探技术. 2022(03): 1-9 . 本站查看
31.	蒋海军，耿黎东，王晓慧，光新军. 国外石油工程碳减排技术与作业管理发展现状及启示. 石油钻探技术. 2022(05): 125-134 . 本站查看
32.	陈应显，周萌. 基于智能岩性识别的炮孔装药量计算. 爆破. 2022(04): 92-99 .

Other cited types(13)

Get Citation

PDF

XML

Article Metrics

Article views (1595) PDF downloads (559) Cited by(45)

Research Method and Practice of Artificial Intelligence Drilling Technology

Abstract

References

Cited by

Periodical cited type(32)

Other cited types(13)

Catalog

Article Metrics

Related

Research Method and Practice of Artificial Intelligence Drilling Technology

Abstract

References

Cited by

Periodical cited type(32)

Other cited types(13)

Catalog

Article Metrics

Related

Export File

Citation

Format

Content