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dc.contributor.author Salimova, Rashida
dc.contributor.author Pourafshary, Peyman
dc.contributor.author Wang, Lei
dc.date.accessioned 2022-11-23T11:03:22Z
dc.date.available 2022-11-23T11:03:22Z
dc.date.issued 2021-07
dc.identifier.citation Salimova, R., Pourafshary, P., & Wang, L. (2021). Data-Driven Analyses of Low Salinity Waterflooding in Carbonates. Applied Sciences, 11(14), 6651. https://doi.org/10.3390/app11146651 en_US
dc.identifier.uri http://nur.nu.edu.kz/handle/123456789/6824
dc.description.abstract Low salinity water (LSW) injection is a promising Enhanced Oil Recovery (EOR) techniquethat has the potential to improve oil recovery and has been studied by many researchers. LSWflooding in carbonates has been widely evaluated by coreflooding tests in prior studies. A closer lookat the literature on LSW in carbonates indicates a number of gaps and shortcomings. It is difficult tounderstand the exact relationship between different controlling parameters and the LSW effect incarbonates. The active mechanisms involved in oil recovery improvement are still uncertain and moreanalyses are required. To predict LSW performance and study the mechanisms of oil displacement,data collected from available experimental studies on LSW injection in carbonates were analyzedusing data analysis approaches. We used linear regression to study the linear relationships betweensingle parameters and the incremental recovery factor (RF). Correlations between rock, oil, andbrine properties and tertiary RF were weak and negligible. Subsequently, we analyzed the effect ofoil/brine parameters on LSW performance using multivariable linear regression. Relatively stronglinear correlations were found for a combination of oil/brine parameters and RF. We also studied thenonlinear relationships between parameters by applying machine learning (ML) nonlinear models,such as artificial neural network (ANN), support vector machine (SVM), and decision tree (DT).These models showed better data fitting results compared to linear regression. Among the appliedML models, DT provided the best correlation for oil/brine parameters, as ANN and SVM overfittedthe testing data. Finally, different mechanisms involved in the LSW effect were analyzed based on thechanges in the effluent PDIs concentration, interfacial tension, pH, zeta potential, and pressure drop en_US
dc.language.iso en en_US
dc.publisher Applied Sciences en_US
dc.rights Attribution-NonCommercial-ShareAlike 3.0 United States *
dc.rights.uri http://creativecommons.org/licenses/by-nc-sa/3.0/us/ *
dc.subject Type of access: Open Access en_US
dc.subject low salinity waterflooding en_US
dc.subject carbonates en_US
dc.subject data-driven analysis en_US
dc.subject machine learning en_US
dc.subject SVM en_US
dc.subject ANN en_US
dc.subject DT en_US
dc.type Article en_US
workflow.import.source science

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