A new empirical model for estimation of crude oil/brine interfacial tension using genetic programming approach

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@Article{ABOOALI:2019:JPSE,

• author = "Danial Abooali and Mohammad Amin Sobati and Shahrokh Shahhosseini and Mehdi Assareh",
• title = "A new empirical model for estimation of crude oil/brine interfacial tension using genetic programming approach",
• journal = "Journal of Petroleum Science and Engineering",
• volume = "173",
• pages = "187--196",
• year = "2019",
• keywords = "genetic algorithms, genetic programming, Interfacial tension, Correlation, Crude oil, Brine, Genetic programming (GP)",
• ISSN = "0920-4105",
• DOI = "doi:10.1016/j.petrol.2018.09.073",
• URL = "http://www.sciencedirect.com/science/article/pii/S0920410518308283",
• abstract = "Detailed understanding of the behavior of crude oils and their interactions with reservoir formations and other in-situ fluids can help the engineers to make better decisions about the future of oil reservoirs. As an important property, interfacial tension (IFT) between crude oil and brine has great impacts on the oil production efficiency in different recovery stages due to its effects on the capillary number and residual oil saturation. In the present work, a new mathematical model has been developed to estimate IFT between crude oil and brine on the basis of a number of physical properties of crude oil (i.e., specific gravity, and total acid number) and the brine (i.e., pH, NaCl equivalent salinity), temperature, and pressure. Genetic programming (GP) methodology has been implemented on a data set including 560 experimental data to develop the IFT correlation. The correlation coefficient (R2a =a 0.9745), root mean square deviation (RMSDa =a 1.8606a mN/m), and average absolute relative deviation (AARDa =a 3.3932percent) confirm the acceptable accuracy of the developed correlation for the prediction of IFT",

}

Genetic Programming entries for Danial Abooali Mohammad Amin Sobati Shahrokh Shahhosseini Mehdi Assareh

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