Machine learning prediction and optimization of CO2 foam performance for enhanced oil recovery and carbon sequestration: Effect of surfactant type and operating conditions-Reference-Cited by-同舟云学术

Machine learning prediction and optimization of CO2 foam performance for enhanced oil recovery and carbon sequestration: Effect of surfactant type and operating conditions

Published:2024-09 Issue: Volume:240 Page:213064
ISSN:2949-8910
Container-title:Geoenergy Science and Engineering
language:en
Short-container-title:Geoenergy Science and Engineering

Author:

Iskandarov Javad^ORCID,Fanourgakis George S.^ORCID,Ahmed Shehzad,Alameri Waleed^ORCID,Froudakis George E.,Karanikolos Georgios N.^ORCID

Funder

Khalifa University

Publisher

Elsevier BV

Reference60 articles.

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3. Foam generation and stability: role of the surfactant structure and Asphaltene aggregates;Alexander;Ind. Eng. Chem. Res.,2022

4. Gas/water foams stabilized with a newly developed anionic surfactant for gas mobility control applications;Almobarky;Pet Sci,2020

5. Laboratory study of CO 2 foam flooding in high temperature, high salinity carbonate reservoirs using Co-injection technique;AlSumaiti;Energy & Fuels,2018

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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