J
Jun Lu
Researcher at University of Tulsa
Publications - 95
Citations - 2936
Jun Lu is an academic researcher from University of Tulsa. The author has contributed to research in topics: Enhanced oil recovery & Chemistry. The author has an hindex of 26, co-authored 82 publications receiving 2022 citations. Previous affiliations of Jun Lu include Xi'an Shiyou University & University of Texas at Austin.
Papers
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A realistic and integrated model for evaluating oil sands development with Steam Assisted Gravity Drainage technology in Canada
TL;DR: In this paper, the authors developed an integrated evaluation model through the analyses of a significant amount of actual historical data, which includes six subcomponent models, ranging from the subsurface reservoir to infield flowline.
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A 2.5-D glass micromodel for investigation of multi-phase flow in porous media
TL;DR: The 2.5-D micromodel can better represent the 3-D features of multi-phase flow in real porous media, as demonstrated in this paper with three different examples.
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Enhanced oil recovery from high-temperature, high-salinity naturally fractured carbonate reservoirs by surfactant flood
Jun Lu,Ali Goudarzi,Peila Chen,Do Hoon Kim,Mojdeh Delshad,Kishore K. Mohanty,Kamy Sepehrnoori,Upali P. Weerasooriya,Gary A. Pope +8 more
TL;DR: In this article, a carboxylate surfactant was used in a hard brine at a high reservoir temperature of 100 ÂC. The results showed that both the mechanisms of IFT reduction and wettability alteration were important for oil recovery.
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New surfactant developments for chemical enhanced oil recovery
Jun Lu,Pathma Jith Liyanage,Sriram Solairaj,Stephanie Adkins,Gayani W.P. Pinnawala Arachchilage,Do Hoon Kim,Christopher Britton,Upali P. Weerasooriya,Gary A. Pope +8 more
TL;DR: In this paper, a new correlation has been developed using an extensive data set taking into account the effects of PO number, EO number, temperature, brine salinity and the EACN of the oil on the optimum hydrophobe size.
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A quantitative oil and gas reservoir evaluation system for development
TL;DR: In this paper, a quantitative evaluation system was developed to measure oil and gas reservoir readiness for development, considering the reservoir and input data quality, using principal component analysis (PCA).