Home Journals Articles Updates About
CN
image
收藏切换
Research on segmented modelling and prediction of shale gas production based on time series analysis
收藏切换
PDF
Feifei Fanga, b, Hongjian Chena, *, Sijie Heb, Jie Zhangb, c, Wei Guod, Weixiang Jinb, Yue Gongb, ChuXiang Xiab
Petroleum Research | 2026, 11(2) : 399 - 414
Less
收藏切换
Petroleum Research | 2026, 11(2): 399-414
• Full Length Article •
Research on segmented modelling and prediction of shale gas production based on time series analysis
Full
Feifei Fanga, b, Hongjian Chena, *, Sijie Heb, Jie Zhangb, c, Wei Guod, Weixiang Jinb, Yue Gongb, ChuXiang Xiab
Affiliations
  • aSchool of Mathematical and Physical Sciences, Chongqing University of Science and Technology, Chongqing 401331, China
  • bSchool of Petroleum Engineering, Chongqing University of Science and Technology, Chongqing 401331, China
  • cChongqing Unconventional Oil & Gas Development Research Institute, Chongqing University of Science and Technology, Chongqing 401331, China
  • dResearch Institute of Petroleum Exploration and Development, PetroChina, Beijing 100083, China
Published: 2026-06-10 doi: 10.1016/j.ptlrs.2025.09.009
Outline
收藏切换

At present, the world is experiencing an unprecedented period of change, and the third wave of energy conversion is also emerging. Natural gas is widely regarded as an ideal bridge between traditional fossil fuels and new energy transformations due to its significant low-carbon advantages. Forecasting shale gas output each day is key for secure natural gas provision. However, the high dimension and nonlinear characteristics of shale gas production data present significant challenges for prediction. Therefore, this study proposes a segmented modeling method that divides the production cycle into unstable and stable periods and combines the IPSO–CNN-BiGRU-Attention hybrid model to model these two cycles respectively. Comparative results indicate segmented modeling offers superior predictive performance compared to whole production cycle modeling. Furthermore, when modeling different periods, the IPSO–CNN-BiGRU-Attention hybrid model demonstrates a superior predictive effect compared to traditional single time series models.

Shale gas  /  Segmented modeling  /  Full-cycle modeling  /  IPSO–CNN-BiGRU-attention  /  Production prediction
Feifei Fang, Hongjian Chen, Sijie He, Jie Zhang, Wei Guo, Weixiang Jin, Yue Gong, ChuXiang Xia. Research on segmented modelling and prediction of shale gas production based on time series analysis[J]. Petroleum Research, 2026 , 11 (2) : 399 -414 . DOI: 10.1016/j.ptlrs.2025.09.009
Full Text
Less
Appendix
Less
Year 2026 volume 11 Issue 2
PDF
10
1
Cite this Article
BibTeX
Article Info
doi: 10.1016/j.ptlrs.2025.09.009
  • Receive Date：2025-05-08
  • Online Date：2026-07-29
  • Published：2026-06-10
Article Data
Affiliations
History
  • Received：2025-05-08
  • Revised：2025-09-16
  • Accepted：2025-09-28
Affiliations
    aSchool of Mathematical and Physical Sciences, Chongqing University of Science and Technology, Chongqing 401331, China
    bSchool of Petroleum Engineering, Chongqing University of Science and Technology, Chongqing 401331, China
    cChongqing Unconventional Oil & Gas Development Research Institute, Chongqing University of Science and Technology, Chongqing 401331, China
    dResearch Institute of Petroleum Exploration and Development, PetroChina, Beijing 100083, China

Corresponding:

*

E-mail address: (H. Chen).
References
Share
https://castjournals.cast.org.cn/joweb/pr/EN/10.1016/j.ptlrs.2025.09.009
Share to
QR

Scan QR to access full text

Cite this article
BibTeX
Citations
表12种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
关闭全屏
  • BibTeX
  • EndNote
  • RefWorks
  • TxT
Links
Articles
Latest Articles
Most Read
Collections
Updates
Events
News
Multimedia
About
About Us
Contact
No. 86 Xueyuan South Road, Haidian District, Beijing
100081
010-62199257
qkjq@cast.org.cn

Copyright © 2025 China Association for Science and Technology. All rights reserved. For all open access content, the relevant licensing terms apply.
Sponsored by the Office of the Leading Group for Cybersecurity and Informatization of CAST, and supported by Science and Technology Review Publishing House