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Prediction Model between Emulsified Water Fractions and Physicochemical Properties of Crude Oil Based on the Exergy Loss Rate

[Image: see text] The quantitative calculation of emulsified water fractions of crude oil–water systems is of great significance for the study of flow characteristics of multiphase flow pipelines. For a crude oil–water system with a high water fraction, the emulsified water fraction under different...

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Detalles Bibliográficos
Autores principales: Wen, Jiangbo, Luo, Haijun, Ai, Gang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Chemical Society 2021
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8388094/
https://www.ncbi.nlm.nih.gov/pubmed/34471762
http://dx.doi.org/10.1021/acsomega.1c02819
Descripción
Sumario:[Image: see text] The quantitative calculation of emulsified water fractions of crude oil–water systems is of great significance for the study of flow characteristics of multiphase flow pipelines. For a crude oil–water system with a high water fraction, the emulsified water fraction under different influencing factors was determined by emulsification experiments. It was found that the emulsified water fraction under different shearing conditions correlated well with the exergy loss rate and could be described by a power-law equation, in which two undetermined parameters relate to the crude oil physicochemical properties. Six representative parameters were selected to describe the crude oil physicochemical properties, i.e., the sum of asphaltene and resin contents, wax content, mechanical impurity content, crude oil acid number, crude oil average carbon number, and crude oil viscosity. Further, the correlations between the two undetermined parameters and the crude oil physicochemical properties were derived by regression analysis. Thus, the prediction model of emulsified water fraction was determined, which could be conveniently adopted to predict the emulsified water fraction with different crude oils and shearing conditions. The validation results showed that the mean relative deviation of the model prediction is 4.5%.