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Characteristic curves correlations to predict SARA composition and gas yields during heavy crude oil non-catalytic aquathermolysis

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dc.contributor Казанский федеральный университет
dc.contributor.author Felix Lugo Jose Guillermo
dc.contributor.author Tirado Kota Aleksis
dc.contributor.author Varfolomeev Mikhail Alekseevich
dc.contributor.author Yuan Chengdong
dc.contributor.author Ancheyta Juarez Jorge
dc.date.accessioned 2026-09-04T06:51:23Z
dc.date.available 2026-09-04T06:51:23Z
dc.date.issued 2023
dc.identifier.citation Characteristic curves correlations to predict SARA composition and gas yields during heavy crude oil non-catalytic aquathermolysis / G. Felix, A. Tirado, M.A. Varfolomeev, C. Yuan, J. Ancheyta // Chem Eng Commun. - 2023. - 210. - P. 1865-1872.
dc.identifier.uri https://dspace.kpfu.ru/xmlui/handle/net/186120
dc.description.abstract Characteristic curve equations are used to calculate the products distribution during the non-catalytic aquathermolysis of Ashal'cha heavy crude oil at different reaction times (12, 24, 48, and 72 h) and temperatures (250 and 300 ?C) with a 3:7 water-to-oil weight ratio. The developed equations accurately estimate the experimental information at different time of reaction and temperature with average absolute error lower than 5%. Unstable primary and secondary (UPS) products equations predicted the experimental results for aromatics and saturate fractions better than stable primary and secondary (SPS) products equations. On the contrary, the gas fraction was well calculated with the SPS products correlation.
dc.language.iso en
dc.relation.ispartofseries Chem Eng Commun.
dc.rights только для КФУ
dc.subject Characteristic curves correlations
dc.subject Aquathermolysis
dc.subject SARA fractions
dc.subject.other Химия
dc.title Characteristic curves correlations to predict SARA composition and gas yields during heavy crude oil non-catalytic aquathermolysis
dc.type Article
dc.contributor.org Институт геологии и нефтегазовых технологий
dc.description.pages 1865-1872
dc.relation.ispartofseries-volume 210
dc.pub-id 329250
dc.identifier.doi 10.1080/00986445.2022.2150617


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