Prediction of Rheological and Filtration Loss Properties of Nano-Zirconium-Dioxide Drilling Fluids via Machine Learning Techniques for Energy Exploration

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Prediction of Rheological and Filtration Loss Properties of Nano-Zirconium-Dioxide Drilling Fluids via Machine Learning Techniques for Energy Exploration. / Jason, Cheah ; Umer Ilyas, Suhaib ; Ridha, Syahrir et al.
Prediction of Rheological and Filtration Loss Properties of Nano-Zirconium-Dioxide Drilling Fluids via Machine Learning Techniques for Energy Exploration. 2024. S. 469-477.

Publikationen: Beitrag in Buch/Bericht/Konferenzband › Beitrag in Konferenzband

Harvard

Jason, C, Umer Ilyas, S, Ridha, S, Sehar, U, Alsaady, M & Krishna, S 2024, Prediction of Rheological and Filtration Loss Properties of Nano-Zirconium-Dioxide Drilling Fluids via Machine Learning Techniques for Energy Exploration. in Prediction of Rheological and Filtration Loss Properties of Nano-Zirconium-Dioxide Drilling Fluids via Machine Learning Techniques for Energy Exploration. S. 469-477. https://doi.org/10.1007/978-981-97-8345-8_57

APA

Jason, C., Umer Ilyas, S., Ridha, S., Sehar, U., Alsaady, M., & Krishna, S. (2024). Prediction of Rheological and Filtration Loss Properties of Nano-Zirconium-Dioxide Drilling Fluids via Machine Learning Techniques for Energy Exploration. In Prediction of Rheological and Filtration Loss Properties of Nano-Zirconium-Dioxide Drilling Fluids via Machine Learning Techniques for Energy Exploration (S. 469-477) https://doi.org/10.1007/978-981-97-8345-8_57

Vancouver

Jason C, Umer Ilyas S, Ridha S, Sehar U, Alsaady M, Krishna S. Prediction of Rheological and Filtration Loss Properties of Nano-Zirconium-Dioxide Drilling Fluids via Machine Learning Techniques for Energy Exploration. in Prediction of Rheological and Filtration Loss Properties of Nano-Zirconium-Dioxide Drilling Fluids via Machine Learning Techniques for Energy Exploration. 2024. S. 469-477 doi: 10.1007/978-981-97-8345-8_57

Author

Jason, Cheah ; Umer Ilyas, Suhaib ; Ridha, Syahrir et al. / Prediction of Rheological and Filtration Loss Properties of Nano-Zirconium-Dioxide Drilling Fluids via Machine Learning Techniques for Energy Exploration. Prediction of Rheological and Filtration Loss Properties of Nano-Zirconium-Dioxide Drilling Fluids via Machine Learning Techniques for Energy Exploration. 2024. S. 469-477

Bibtex - Download

@inproceedings{96c3e934d98f437eb0bed3cf36e9a6f8,
title = "Prediction of Rheological and Filtration Loss Properties of Nano-Zirconium-Dioxide Drilling Fluids via Machine Learning Techniques for Energy Exploration",
author = "Cheah Jason and {Umer Ilyas}, Suhaib and Syahrir Ridha and Umara Sehar and Mustafa Alsaady and Shwetank Krishna",
year = "2024",
doi = "10.1007/978-981-97-8345-8_57",
language = "English",
pages = "469--477",
booktitle = "Prediction of Rheological and Filtration Loss Properties of Nano-Zirconium-Dioxide Drilling Fluids via Machine Learning Techniques for Energy Exploration",

}

RIS (suitable for import to EndNote) - Download

TY - GEN

T1 - Prediction of Rheological and Filtration Loss Properties of Nano-Zirconium-Dioxide Drilling Fluids via Machine Learning Techniques for Energy Exploration

AU - Jason, Cheah

AU - Umer Ilyas, Suhaib

AU - Ridha, Syahrir

AU - Sehar, Umara

AU - Alsaady, Mustafa

AU - Krishna, Shwetank

PY - 2024

Y1 - 2024

U2 - 10.1007/978-981-97-8345-8_57

DO - 10.1007/978-981-97-8345-8_57

M3 - Conference contribution

SP - 469

EP - 477

BT - Prediction of Rheological and Filtration Loss Properties of Nano-Zirconium-Dioxide Drilling Fluids via Machine Learning Techniques for Energy Exploration

ER -

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