DATA-DRIVEN ANALYSES OF LOW SALINITY WATERFLOODING IN CARBONATES

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dc.contributor.authorSalimova, Rashida
dc.contributor.authorPourafshary, Peyman
dc.contributor.authorWang, Lei
dc.date.accessioned2022-11-23T11:03:22Z
dc.date.available2022-11-23T11:03:22Z
dc.date.issued2021-07
dc.description.abstractLow 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 dropen_US
dc.identifier.citationSalimova, 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/app11146651en_US
dc.identifier.urihttp://nur.nu.edu.kz/handle/123456789/6824
dc.language.isoenen_US
dc.publisherApplied Sciencesen_US
dc.rightsAttribution-NonCommercial-ShareAlike 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/3.0/us/*
dc.subjectType of access: Open Accessen_US
dc.subjectlow salinity waterfloodingen_US
dc.subjectcarbonatesen_US
dc.subjectdata-driven analysisen_US
dc.subjectmachine learningen_US
dc.subjectSVMen_US
dc.subjectANNen_US
dc.subjectDTen_US
dc.titleDATA-DRIVEN ANALYSES OF LOW SALINITY WATERFLOODING IN CARBONATESen_US
dc.typeArticleen_US
workflow.import.sourcescience

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