Prediction by machine learning in nanoparticles-based enhanced oil recovery
Abstract
Nanotechnology is on the brink of transforming numerous industrial sectors, and the petroleum industry stands as a front-runner in embracing these revolutionary advancements. In recent years, a growing interest has occurred in leveraging nanotechnology within the petroleum industry%, particularly to enhance oil recovery.
Extensive research studies on nano-enhanced oil recovery (nano-EOR) have consistently delivered promising outcomes, underscoring its potential to elevate oil production substantially. However, a notable challenge persists within this domain due to the limited data availability concerning nanoparticle transport in porous media. This paper uses machine learning techniques to predict nanoparticle transport in porous media. This study uses the finite difference method to generate simulated datasets from a modified linear adsorption model. These simulated datasets are used to train machine learning models for prediction by considering artificial neural network (ANNs), decision tree (DT), and random forest (RF). We achieve mean squared values for ANN as 0.0478 (training), 0.0496 (testing), 0.0509 (validation), and R-squared values as 0.9798 (training), 0.9780 (testing), 0.9773 (validation), and for DT and RF mean squared values are 0.014683, 0.009807, and R squared values are 0.928775, 0.952425.
Keywords
Enhanced oil recovery, Nanoparticles, Machine learning, Fluid flow
References
- [1] Bera, A. and Belhaj, H. Application of nanotechnology by means of nanoparticles and nanodispersions in oil recovery-A comprehensive review. Journal of Natural Gas Science and Engineering, 34, 1284-1309, (2016).
- [2] Negin, C., Ali, S. and Xie, Q. Application of nanotechnology for enhancing oil recovery–A review. Petroleum, 2(4), 324-333, (2016).
- [3] Metin, C.O., Baran, J.R. and Nguyen, Q.P. Adsorption of surface functionalized silica nanoparticles onto mineral surfaces and decane/water interface. Journal of Nanoparticle Research, 14, 1246, (2012).
- [4] Miranda, C.R., De Lara, L.S. and Tonetto, B.C. Stability and mobility of functionalized silica nanoparticles for enhanced oil recovery applications. In Proceedings, SPE International Oilfield Nanotechnology Conference and Exhibition, pp. 157033, Noordwijk, The Netherlands, (2012, June).
- [5] Nath, N., Chakroborty, S., Panda, P. and Pal, K. High yield silica-based emerging nanoparticles activities for hybrid catalyst applications. Topics in Catalysis, 65(19), 1706-1718, (2022).
- [6] Bentley, R.W., Mannan, S.A. and Wheeler, S.J. Assessing the date of the global oil peak: the need to use 2P reserves. Energy policy, 35(12), 6364-6382, (2007).
- [7] Katende, A. and Sagala, F. A critical review of low salinity water flooding: Mechanism, laboratory and field application. Journal of Molecular Liquids, 278, 627-649, (2019).
- [8] Kong, X. and Ohadi, M.M. Applications of micro and nano technologies in the oil and gas industry - an overview of the recent progress. In Proceedings, Abu Dhabi International Petroleum Exhibition and Conference, pp. 138241, Abu Dhabi, UAE, (2010, November).
- [9] Karimi, A., Fakhroueian, Z., Bahramian, A., Pour Khiabani, N., Darabad, J.B., Azin, R. and Arya, S. Wettability alteration in carbonates using zirconium oxide nanofluids: EOR implications. Energy and Fuels, 26(2), 1028-1036, (2012).
- [10] Ehtesabi, H., Ahadian, M.M., Taghikhani, V. and Ghazanfari, M.H. Enhanced heavy oil recovery in sandstone cores using TiO2 nanofluids. Energy and Fuels, 28(1), 423-430, (2014).