Methodological Theory | Views : 0 下载量: 121 CSCD: 0 CNKI被引量: 0 Scopus: 0 更多指标
  • Export

  • Share

  • Collection

  • Album

    • Research progress on machine learning in CO2 enhanced oil and gas recovery and geological storage

    • In the field of carbon capture, utilization, and storage, machine learning technology has shown significant advantages in optimizing operating parameters, improving computational efficiency, and providing solutions for achieving carbon neutrality.
    • Vol. 16, Issue 1, Pages: 84-95(2026)   

      Received:09 June 2025

      Published:26 January 2026

    • DOI: 10.13809/j.cnki.cn32-1825/te.2025268     

    移动端阅览

  • YE Hongying, CAO Cheng, ZHAO Yulong, et al. Research progress on machine learning in CO2 enhanced oil and gas recovery and geological storage[J]. Petroleum Reservoir Evaluation and Development, 2026, 16(1): 84-95. DOI: 10.13809/j.cnki.cn32-1825/te.2025268.
  •  
  •  
Alert me when the article has been cited
提交

相关作者

ZHAO SONG 中国石油长庆油田
CAO CHENG 西南石油大学油气藏地质及开发工程全国重点实验室;怀柔实验室;重庆大学产业技术研究院
YE HONGYING 西南石油大学油气藏地质及开发工程全国重点实验室
WANG YUANYUAN 中国石化胜利油田分公司纯梁采油厂, 山东 博兴
GAO TONG 中国石化胜利油田分公司纯梁采油厂, 山东 博兴
YANG ZHIKAI 中国石化胜利油田分公司纯梁采油厂, 山东 博兴
LIU SAIJUN 中国石化胜利油田分公司纯梁采油厂, 山东 博兴
FAN CHAO 中国石化胜利油田分公司纯梁采油厂, 山东 博兴

相关机构

Changqing Oilfield
Chunliang Oil Production Plant, Sinopec Shengli Oilfield Company, Boxing
Gas Storage Construction Project Department, PetroChina Jidong Oilfield Company
PetroChina Hangzhou Research Institute of Geology
Exploration and Development Research Institute of China Petroleum Changqing Oilfield Branch
0