Using Unsupervised Machine Learning to Characterize Capillary Flow and Residual Trapping
Author:
Affiliation:
1. Department of Energy Resources Engineering Stanford University Stanford CA USA
Publisher
American Geophysical Union (AGU)
Subject
Water Science and Technology
Link
https://onlinelibrary.wiley.com/doi/pdf/10.1029/2020WR027473
Reference81 articles.
1. Time-series clustering – A decade review
2. Relative permeability hysteresis and capillary trapping characteristics of supercritical CO2/brine systems: An experimental study at reservoir conditions
3. Co-sequestration of SO2 with supercritical CO2 in carbonates: An experimental study of capillary trapping, relative permeability, and capillary pressure
4. Computed tomography in petroleum engineering research
5. Semi-Supervised and Unsupervised Machine Learning
Cited by 14 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献
1. Graph theory based estimation of probable CO2 plume spreading in siliciclastic reservoirs with lithological heterogeneity;Advances in Water Resources;2024-07
2. Machine learning applications in vadose zone hydrology: A review;Vadose Zone Journal;2024-06-14
3. Physics-informed neural networks for modeling two-phase steady state flow with capillary heterogeneity at varying flow conditions;Advances in Water Resources;2024-03
4. Carbon capture, utilization and sequestration systems design and operation optimization: Assessment and perspectives of artificial intelligence opportunities;Science of The Total Environment;2024-03
5. Quantifying the effects of pressure management for the Williston basin Brine Extraction and Storage Test (BEST) site using machine learning;International Journal of Greenhouse Gas Control;2024-01
1.学者识别学者识别
2.学术分析学术分析
3.人才评估人才评估
"同舟云学术"是以全球学者为主线，采集、加工和组织学术论文而形成的新型学术文献查询和分析系统，可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容，当前同舟云学术共收录了国内外主流学术期刊6万余种，收集的期刊论文及会议论文总量共计约1.5亿篇，并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询！咨询电话：010-8811{复制后删除}0370
www.globalauthorid.com
TOP
Copyright © 2019-2024 北京同舟云网络信息技术有限公司 京公网安备11010802033243号 京ICP备18003416号-3