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

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

doi:10.1016/j.geoen.2024.213064

Geoenergy Science and Engineering

2024

DOI: 10.1016/j.geoen.2024.213064

|View full text |Cite

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

Javad Iskandarov,

George S. Fanourgakis,

Shehzad Ahmed

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article2

Relationship

Self Cite0

Independent2

Authors

Journals

Cited by 2 publications

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Predicting and optimizing CO2 foam performance for enhanced oil recovery: A machine learning approach to foam formulation focusing on apparent viscosity and interfacial tension

Iskandarov,

Ahmed,

Fanourgakis

et al. 2024

Marine and Petroleum Geology

View full text Add to dashboard Cite

Predicting and optimizing CO2 foam performance for enhanced oil recovery: A machine learning approach to foam formulation focusing on apparent viscosity and interfacial tension

Iskandarov,

Ahmed,

Fanourgakis

et al. 2024

Marine and Petroleum Geology

View full text Add to dashboard Cite

Carbon Capture and Storage Optimization with Machine Learning using an ANN model

Kotov,

Sravanthi,

Logabiraman

et al. 2024

E3S Web Conf.

View full text Add to dashboard Cite

The purpose of this study is to evaluate the accuracy of predictions regarding the work capacity of CO2 and the selectivity of MOF, using machine learning methodologies in relation to CO2/N2. A dataset was used that includes numerous characteristics of MOFs for the development of a neural network model. The factors that determined the operational capacity of CO2 and the CO2/N2 selectivity included pore size, surface area, chemical composition, among others. The model demonstrated its work capacity by evaluating the selectivity of CO2; the mean absolute errors for the CO2/N2 selectivity were 25 and 0.8 mmol/g, respectively. The correlation Analysis showed a fairly negative correlation (-0.014) between the operational capacity of CO2 and its chemical makeup and a very positive correlation ( 0.029) between the surface area and amount of pore size. Thus, the gas absorbability is not top-dependent exclusively; pore size and surface area of a material contribute to the capacity as well. More research should be carried out to evaluate a machine learning capability on predicting the nature of different Flow Object Models (MOFs) with an aim of increasing efficiency, precision and dependability of the models.

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

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

Cited by 2 publications

References 39 publications

Predicting and optimizing CO2 foam performance for enhanced oil recovery: A machine learning approach to foam formulation focusing on apparent viscosity and interfacial tension

Predicting and optimizing CO2 foam performance for enhanced oil recovery: A machine learning approach to foam formulation focusing on apparent viscosity and interfacial tension

Carbon Capture and Storage Optimization with Machine Learning using an ANN model

Contact Info

Product

Resources

About