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pro vyhledávání: '"Lis, M. A."'
Akademický článek
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Autor:
Supriyono, Chen, Tzu-Chia, Yapanto, Lis M., Latipov, Zagir Azgarovich, Zekiy, Angelina Olegovna, Melnikova, Lyubov A., Thangavelu, Lakshmi, Surendar, A., Repnikov, Nikolay I., Arzehgar, Zeinab
Publikováno v:
Soldering & Surface Mount Technology, 2021, Vol. 34, Issue 1, pp. 58-65.
Externí odkaz:
http://www.emeraldinsight.com/doi/10.1108/SSMT-04-2021-0014
Autor:
Wolok, Eduart1, Yapanto, Lis M.2, Lapian, Agnes Lutherani. Ch. P.3, Wolok, Tineke4, Aneta, Asna5
Publikováno v:
International Journal of Professional Business Review (JPBReview). 2023, Vol. 8 Issue 4, p1-10. 10p.
Publikováno v:
Energy Reports, Vol 7, Iss , Pp 2452-2459 (2021)
Tight reservoirs are considered one of the unconventional hydrocarbon reservoirs with low permeability and porosity, directly affecting the oil production rate rather than conventional reservoirs. Thereby, optimum enhanced oil recovery methods would
Externí odkaz:
https://doaj.org/article/2dad9ae28deb47ea823accce8c24b9d4
Publikováno v:
Energy Reports, Vol 7, Iss , Pp 2751-2758 (2021)
To select the optimum methods, such criteria like reservoir heterogeneity, reservoir pressure, reservoir temperature, crude oil type API (American Petroleum Institute), and brine salinity. EOR methods contained water flooding, chemical flooding, nano
Externí odkaz:
https://doaj.org/article/d30c4315b2d14087b497d0b9776c0509
Application of artificial neural networks and fuzzy logics to estimate porosity for Asmari formation
Publikováno v:
Energy Reports, Vol 7, Iss , Pp 3090-3098 (2021)
Porosity estimation is one of the essential issues in petroleum industries to distinguish the reservoir characteristics properly. Therefore, it is of importance to predict porosity with the optimum way to reduce the logging tests. In this study, arti
Externí odkaz:
https://doaj.org/article/146565e229c4411883357984333b1030
Application of artificial neural networks and fuzzy logics to estimate porosity for Asmari formation
Publikováno v:
In Energy Reports November 2021 7:3090-3098
Autor:
Wei, Zhenzhen, Zhu, Shanyu, Dai, Xiaodong, Wang, Xuewu, Yapanto, Lis M., Raupov, Inzir Ramilevich
Publikováno v:
In Energy Reports November 2021 7:2751-2758
Publikováno v:
In Energy Reports November 2021 7:2452-2459
Autor:
Pernille B. Udesen, Anja E. Sørensen, Rikke Svendsen, Nanna L. S. Frisk, Anne L. Hess, Mubeena Aziz, Marie Louise M. Wissing, Anne Lis M. Englund, Louise T. Dalgaard
Publikováno v:
Cells, Vol 12, Iss 7, p 983 (2023)
Background: Women with polycystic ovary syndrome (PCOS) often change their metabolic profile over time to decrease levels of androgens while often gaining a propensity for the development of the metabolic syndrome. Recent discoveries indicate that mi
Externí odkaz:
https://doaj.org/article/f56ac2d15f0843078d8c208e1acad3e4