Machine Learning Application to CO2 Foam Rheology

Javad Iskandarov, George Fanourgakis, Waleed Alameri, George Froudakis, Georgios Karanikolos

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

Conventional foam modelling techniques require tuning of too many parameters and long computational time in order to provide accurate predictions. Therefore, there is a need for alternative methodologies for the efficient and reliable prediction of the foams' performance. Foams are susceptible to various operational conditions and reservoir parameters. This research aims to apply machine learning (ML) algorithms to experimental data in order to correlate important affecting parameters to foam rheology. In this way, optimum operational conditions for CO2 foam enhanced oil recovery (EOR) can be determined. In order to achieve that, five different ML algorithms were applied to experimental rheology data from various experimental studies. It was concluded that the Gradient Boosting (GB) algorithm could successfully fit the training data and give the most accurate predictions for unknown cases.

Original languageBritish English
Title of host publicationSociety of Petroleum Engineers - Abu Dhabi International Petroleum Exhibition and Conference, ADIP 2021
ISBN (Electronic)9781613998342
DOIs
StatePublished - 2021
Event2021 Abu Dhabi International Petroleum Exhibition and Conference, ADIP 2021 - Abu Dhabi, United Arab Emirates
Duration: 15 Nov 202118 Nov 2021

Publication series

NameSociety of Petroleum Engineers - Abu Dhabi International Petroleum Exhibition and Conference, ADIP 2021

Conference

Conference2021 Abu Dhabi International Petroleum Exhibition and Conference, ADIP 2021
Country/TerritoryUnited Arab Emirates
CityAbu Dhabi
Period15/11/2118/11/21

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