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Your search keyword '"Elraies, Khaled A."' showing total 234 results
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2. Enhancing MSc by Coursework Offering Through the Industry Driven Program

Author

Mohshim, Dzeti Farhah, Elraies, Khaled Abdalla, Razman, Nini Nabila, Huang, Ronghuai, Series Editor, Kinshuk, Series Editor, Jemni, Mohamed, Series Editor, Chen, Nian-Shing, Series Editor, Spector, J. Michael, Series Editor, Romlie, Mohd Fakhizan bin, editor, Shaikh Ali, Siti Haryani, editor, Hari, Zolman Bin, editor, and Leow, Meng Chew, editor

Published
2024
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19. Comparison of the effect of organic surfactant and non-ionic surfactant on interfacial tension reduction during enhanced oil recovery.

Author

Basruddin, Muhammad Safri Bin, Suhaimi, Fatin Ayuni Mohd, Fan, Wong Siew, Shuhili, Juhairi Aris Bin Muhamad, Elraies, Khaled Abdalla, Hyun, Lee Jang, Saaid, Ismail Mohd, Gogoi, Subrata Borgohain, Ibrahim, Mohamad Nasir Mohamad, and Husna, Ully Zakyatul

Subjects
NONIONIC surfactants, ENHANCED oil recovery, ANIONIC surfactants, INTERFACIAL tension, AMPHIPHILES, LIGNINS
Abstract

Surfactants are one of the essential components for chemical flooding during enhanced oil recovery (EOR). Surfactants are amphipathic compounds that consist of hydrophilic head and lipophilic tail that could be used to reduce the interfacial tension (IFT) between the injected solution and the crude oil. Lignin-based surfactants are gaining traction among researchers as the alternative to commercial surfactants due to low cost for production, abundant in nature, biodegradable and eco-friendly properties. However, the study on the comparison between natural surfactants derived from lignin and commercial non-ionic surfactants are still yet to be known. The research aims to compare the performance of lignin-based surfactant and anionic commercial surfactant in terms of IFT reduction and wettability alteration. The objectives to be achieved in this study are to measure the IFT between crude oil and surfactant solution at different concentration for both organic and non-ionic surfactant. Lignin-based surfactant was used as the organic surfactant whereas Triton X-405 as the non-ionic surfactant. Different concentrations of surfactant were used in NaCl brine with salinity of 30,000 ppm with decane as the crude oil. All the experiments were conducted in ambient temperature. The experiment was started by measuring the density of prepared surfactant solutions with decane using a densitometer. Next, the measurement of IFT between crude oil and surfactant solution using pendant drop method and rising drop method would be performed using IFT 700 equipment. From the result, the IFT performance of lignin-based surfactant in terms of IFT reduction was found to be lower than conventional non-ionic surfactant. [ABSTRACT FROM AUTHOR]

Published
2024
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22. A REVIEW OF CHAN PLOT APPLICATION AND RECENT ADVANCED MODELS FOR DIAGNOSING EXCESSIVE WATER PRODUCTION

Author

Hamdoon, Ahmed, Mohammed, Mohammed, Elraies, Khaled, Hamdoon, Ahmed, Mohammed, Mohammed, and Elraies, Khaled

Abstract

Water is one of the major fluids associated with the operational cycle of the oil industry that must be carefully considered due to its environmental, treatment facility, and economic impacts. Over the years, various methods have been developed to identify excessive water production. These methods range from reliable and expensive ones, such as well-logging records, to less accurate methods that utilize available production and water-oil ratio data, such as the Chan plot. The Chan plot emphasizes that well production can exhibit various patterns of excessive water production, including constant water-oil ratios, normal displacement, channeling, and coning. However, manual interpretation of these plots is often confusing due to the noise present in the actual data. Machine learning models have improved interpretation accuracy, but limitations remain in detecting evolving water production patterns. This paper reviews the application of Chan plots and their integration with existing diagnostic tools for diagnosing excessive water production. It then focuses on a recent advanced model that leverages machine learning specifically designed to improve the interpretation of Chan plots. The review highlights the limitations of traditional interpretation techniques and explores how the recent advanced model can address these limitations. Additionally, the paper briefly discusses the potential of an interactive model for the continuous monitoring of water production patterns. Finally, the paper offers recommendations for future research directions., Slojna voda jedan je od glavnih fluida povezanih s proizvodnim ciklusom u naftnoj industriji, koji je potrebno pažljivo razmatrati zbog utjecaja na okoliš, izgradnje postrojenja za obradu fluida i ekonomskoga utjecaja. Tijekom godina razvijene su različite metode za otkrivanje uzroka prekomjerne proizvodnje slojne vode. Te metode obuhvaćaju cijeli niz metoda, od pouzdanih i skupih, kao što su karotažni podatci, do manje točnih metoda, koje se koriste dostupnim podatcima o proizvodnji te podatcima o vodo-naftnom faktoru (eng. Water-Oil Ratio, WOR), kao što je Chanov dijagram. Iz Chanova dijagrama vidljivo je da tijekom proizvodnje ugljikovodika do prekomjerne proizvodnje slojne vode dolazi zbog različitih uzročnika uključujući konstantan WOR, istiskivanje fluida, kanalno strujanje (strujanje istiskujućega fluida) i konusiranje. Međutim, ručna interpretacija ovih dijagrama često dovodi do zbunjujućih rezultata zbog pogrešaka (šumova) prisutnih u stvarnim podatcima. Modeli strojnoga učenja poboljšali su točnost tumačenja, no još uvijek ostaju ograničenja vezana uz otkrivanje obrazaca proizvodnje vode koji se pritom razvijaju. Ovaj rad daje pregled primjene Chanovih dijagrama i njihove integracije s postojećim dijagnostičkim alatima za otkrivanje uzročnika prekomjerne proizvodnje vode. Rad je fokusiran na nedavno razvijen napredni model koji se koristi strojnim učenjem posebno osmišljenim za poboljšanje interpretacije Chanovih dijagrama. U ovome su preglednom radu istaknuta ograničenja tradicionalnih metoda tumačenja Chanovih dijagrama i istraženo je kako nedavno razvijen napredni model može riješiti ta ograničenja. Dodatno, u radu se ukratko raspravlja o potencijalu interaktivnoga modela za kontinuirano praćenje uzroka prekomjerne proizvodnje slojne vode. Na kraju, rad daje preporuke za buduće smjerove istraživanja.

Published
2024

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