Application of Artificial Intelligence to Predict Enhanced Oil Recovery Using Silica Nanofluids
Author:
Funder
Hakim Sabzevari University
Iran Nanotechnology Initiative Council
Publisher
Springer Science and Business Media LLC
Subject
General Environmental Science
Link
https://link.springer.com/content/pdf/10.1007/s11053-021-09829-1.pdf
Reference51 articles.
1. Abhishek, R., Hamouda, A. A., & Abdulhameed, F. M. (2019). Adsorption kinetics and enhanced oil recovery by silica nanoparticles in sandstone. Petroleum Science and Technology, 37(12), 1363–1369. https://doi.org/10.1080/10916466.2019.1587455.
2. AfzaliTabar, M., Alaei, M., Khojasteh, R. R., Motiee, F., & Rashidi, A. (2017). Preference of multi-walled carbon nanotube (MWCNT) to single-walled carbon nanotube (SWCNT) and activated carbon for preparing silica nanohybrid pickering emulsion for chemical enhanced oil recovery (C-EOR). Journal of Solid State Chemistry, 245, 164–173. https://doi.org/10.1016/j.jssc.2016.10.017.
3. Agista, M. N., Guo, K., & Yu, Z. (2018). A state-of-the-art review of nanoparticles application in petroleum with a focus on enhanced oil recovery. Applied Sciences, 8(6), 871. https://doi.org/10.3390/app8060871.
4. Agwu, O. E., Akpabio, J. U., & Dosunmu, A. (2020). Artificial neural network model for predicting the density of oil-based muds in high-temperature, high-pressure wells. Journal of Petroleum Exploration and Production Technology, 10(3), 1081–1095. https://doi.org/10.1007/s13202-019-00802-6.
5. Ali, J. A., Kolo, K., Manshad, A. K., & Stephen, K. D. (2019). Potential application of low-salinity polymeric-nanofluid in carbonate oil reservoirs: IFT reduction, wettability alteration, rheology and emulsification characteristics. Journal of Molecular Liquids, 284, 735–747. https://doi.org/10.1016/j.molliq.2019.04.053.
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