Steps and Challenges in Empirical Foam Modeling for Enhanced Oil Recovery
Author:
Publisher
Springer Science and Business Media LLC
Subject
General Environmental Science
Link
http://link.springer.com/content/pdf/10.1007/s11053-020-09624-4.pdf
Reference139 articles.
1. Aarra, M. G., Ormehaug, P. A., Skauge, A., & Masalmeh, S. K. (2011). Experimental study of CO2- and methane-foam using carbonate core material at reservoir conditions. In SPE middle east oil and gas show and conference. Society of Petroleum Engineers.
2. Abbaszadeh, M., Kazemi Nia Korrani, A., Lopez-Salinas, J. L., Rodriguez-de La Garza, F., Villavicencio Pino, A., & Hirasaki, G. (2014). Experimentally-based empirical foam modeling. In SPE improved oil recovery symposium. Society of Petroleum Engineers.
3. Abbaszadeh, M., & Ren, G. (2013). Simulation techniques for surfactant phase behavior and foam flow modeling in fractured reservoirs. In SPE reservoir characterization and simulation conference and exhibition. Society of Petroleum Engineers.
4. Abbaszadeh, M., Rodriguez De La Garza, F., Yuan, C., & Pope, G. A. (2010). Single-well simulation study of foam EOR in gas-cap oil of the naturally-fractured cantarell field. In SPE improved oil recovery symposium. Society of Petroleum Engineers.
5. Abbaszadeh, M., Rodriguez De-La Garza, F., & Villavicencio, A. (2016). Methodology of foam-surfactant eor for pilot design studies in naturally fractured reservoirs. In SPE EOR conference at oil and gas West Asia. Society of Petroleum Engineers.
Cited by 5 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. Predicting and optimizing CO2 foam performance for enhanced oil recovery: A machine learning approach to foam formulation focusing on apparent viscosity and interfacial tension;Marine and Petroleum Geology;2024-12
2. Machine learning prediction and optimization of CO2 foam performance for enhanced oil recovery and carbon sequestration: Effect of surfactant type and operating conditions;Geoenergy Science and Engineering;2024-09
3. Experimental Characterization to Support a FAWAG Project in an Offshore Pre-Salt Field;Day 2 Tue, April 23, 2024;2024-04-22
4. Data-driven prediction of in situ CO2 foam strength for enhanced oil recovery and carbon sequestration;RSC Advances;2022
5. Study on the regional characteristics during foam flooding by population balance model;Energy Exploration & Exploitation;2020-12-29
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