Enhanced oil recovery by nanoparticles flooding: From numerical modeling improvement to machine learning prediction
Abstract view|1375|times PDF download|198|times
Abstract
Nowadays, enhanced oil recovery using nanoparticles is considered an innovative approach to increase oil production. This paper focuses on predicting nanoparticles transport in porous media using machine learning techniques including random forest, gradient boosting regression, decision tree, and artificial neural networks. Due to the lack of data on nanoparticles transport in porous media, this work generates artificial datasets using a numerical model that are validated against experimental data from the literature. Six experiments with different nanoparticles types with various physical features are selected to validate the numerical model. Therefore, the researchers produce six datasets from the experiments and create an additional dataset by combining all other datasets. Also, data preprocessing, correlation, and features importance methods are investigated using the Scikit-learn library. Moreover, hyperparameters tuning are optimized using the GridSearchCV algorithm. The performance of predictive models is evaluated using the mean absolute error, the R-squared correlation, the mean squared error, and the root mean squared error. The results show that the decision tree model has the best performance and highest accuracy in one of the datasets. On the other hand, the random forest model has the lowest root mean squared error and highest R-squared values in the rest of the datasets, including the combined dataset.
Cited as: Alwated, B., El-Amin, M.F. Enhanced oil recovery by nanoparticles flooding: From numerical modeling improvement to machine learning prediction. Advances in Geo-Energy Research, 2021, 5(3): 297-317, doi: 10.46690/ager.2021.03.06
Keywords
Full Text:
PDFReferences
Abdelfatah, E., Pournik, M., Shiau, B. J. B., et al. Mathematical modeling and simulation of nanoparticles transport in heterogeneous porous media. Journal of Natural Gas Science and Engineering, 2017, 40: 1-16.
Alvarado, V., Manrique, E. Enhanced oil ecovery concepts, in Enhanced Oil Recovery, edited by V. Alvarado and E. Manrique, Boston, Gulf Professional Publishing, pp. 7-16, 2010.
Alvarado, V., Manrique, E. Enhanced oil recovery: An update review. Energies, 2010b, 3: 1529-1575.
Benamar, A., Ahfir, N. D., Wang, H., et al. Particle transport in a saturated porous medium: Pore structure effects. Comptes Rendus Geoscience, 2007, 339(10): 674-681.
Bradford, S. A., Yates, S. R., Bettahar, M., et al. Physical factors affecting the transport and fate of colloids in saturated porous media. Water Resources Research, 2002, 38(12): 63-1-63-12.
Breiman, L. Random forests. Machine Learning, 2001, 45: 5-32.
Changdar, S., Saha, S., De, S. A smart model for prediction of viscosity of nanofluids using deep learning. Smart Science, 2020, 8(4): 242-256.
Cushing, R. S., Lawler, D. F. Depth filtration: Fundamental investigation through three-dimensional trajectory analysis. Environmental Science and Technology, 1998, 32(23): 3793-3801.
Daribayev, B., Akhmed-Zaki, D., Imankulov, T., et al. Using machine learning methods for oil recovery prediction. ECMOR XVII, European Association of Geoscientists and Engineers, 2020, 2020(1): 1-13.
Álvarez del Castillo, A., Santoyo, E., García-Valladares, O. A new void fraction correlation inferred from artificial neural networks for modeling two-phase flows in geothermal wells. Computers and Geosciences, 2012, 41: 25-39.
El-Amin, M. F., Salama, A., Sun, S. Numerical and dimensional analysis of nanoparticles transport with two-phase flow in porous media. Journal of Petroleum Science and Engineering, 2015, 128: 53-64.
El-Amin, M. F., Salama, A., Sun, S. Modeling and simulation of nanoparticles transport in a two-phase flow in porous media. Paper SPE 154972 Presented at the International Oilfield Nanotechnology Conference and Exhibition, Noordwijk, The Netherlands, 12-14 June, 2012a.
El-Amin, M. F., Subasi, A., Developing a generalized scaling-law for oil recovery using machine learning techniques. Procedia Computer Science, 2019, 163: 237-247.
El-Amin, M. F., Subasi, A. Forecasting a small-scale hydrogen leakage in air using machine learning techniques. Paper IEEE 9257718 Presented at 2020 2nd International Conference on Computer and Information Sciences (ICCIS), Aljouf, Saudi Arabia, 13-15 October, 2020a.
El-Amin, M. F., Subasi, A. Predicting turbulent buoyant jet using machine learning techniques. Paper IEEE 9257628 Presented at 2020 2nd International Conference on Computer and Information Sciences (ICCIS), Aljouf, Saudi Arabia, 13-15 October, 2020b.
El-Amin, M. F., Sun, S., Salama, A. Modeling and simulation of nanoparticle transport in multiphase flows in porous media: CO2 sequestration. Paper SPE 163089 Presented at the Mathematical Methods in Fluid Dynamics and Simulation of Giant Oil and Gas Reservoirs, Istanbul, Turkey, 3-5 September, 2012b.
El-Amin, M. F., Sun, S., Salama, A. Enhanced oil recovery by nanoparticles injection: Modeling and simulation. Paper SPE 164333 Presented at the SPE Middle East Oil and Gas Show and Conference, Manama, Bahrain, 10-13 March, 2013.
Esfe, M. H., Bahiraei, M., Mahian, O. Experimental study for developing an accurate model to predict viscosity of CuO–ethylene glycol nanofluid using genetic algorithm based neural network. Powder Technology, 2018, 338: 383-390.
Fan, J. Numerical study of particle transport and deposition in porous media. Bretagne, INSA de Rennes, 2018.
Hansen, L. K., Salamon, P. Neural network ensembles. IEEE transactions on pattern analysis and machine intelligence, 1990, 12(10): 993-1001.
Irfan, S. A., Shafie, A. Artificial neural network modeling of nanoparticles assisted enhanced oil recovery, in Advanced Methods for Processing and Visualizing the Renewable Energy, edited by S. A. Irfan and A. Shafie, Springer, Singapore, pp. 59-75, 2021.
Jeong, S. W., Kim, S. D. Aggregation and transport of copper oxide nanoparticles in porous media. Journal of Environmental Monitoring, 2009, 11(9): 1595-1600.
Ju, B., Fan, T. Experimental study and mathematical model of nanoparticle transport in porous media. Powder Technology, 2009, 192(2): 195-202.
Kazemzadeh, Y., Shojaei, S., Riazi, M., et al. Review on application of nanoparticles for EOR purposes: A critical review of the opportunities and challenges. Chinese Journal of Chemical Engineering, 2019, 27(2): 237-246.
Khalilinezhad, S. S., Cheraghian, G., Karambeigi, M. S., et al. Characterizing the role of clay and silica nanoparticles in enhanced heavy oil recovery during polymer flooding. Arabian Journal for Science and Engineering, 2016, 41(7): 2731-2750.
Khalilinezhad, S. S., Cheraghian, G., Roayaei, E., et al. Improving heavy oil recovery in the polymer flooding process by utilizing hydrophilic silica nanoparticles. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 2017, 1–10.
Kong, X., Ohadi, M. M. Applications of micro and nano technologies in the oil and gas industry-an overview of the recent progress. Paper SPE 138241 Presented at the Abu Dhabi International Petroleum Exhibition and Conference, Abu Dhabi, UAE, 1-4 November, 2010.
Lashari, N., Ganat, T. Emerging applications of NANOMATERIALS in chemical enhanced oil recovery: Progress and perspective. Chinese Journal of Chemical Engineering, 2020, 28(8): 1995-2009.
Li, S. An experimental investigation of enhanced oil recovery mechanisms in nanofluid injection process. Norway, Norwegian University of Science and Technology, 2016.
Lippmann, R. An introduction to computing with neural nets. IEEE Assp magazine, 1987, 4(2): 4-22.
Liu, X., O’carroll, D. M., Petersen, E. J., et al. Mobility of multiwalled carbon nanotubes in porous media. Environmental science and technology, 2009, 43(21): 8153-8158.
Maghzi, A., Kharrat, R., Mohebbi, A., et al. The impact of silica nanoparticles on the performance of polymer solution in presence of salts in polymer flooding for heavy oil recovery. Fuel, 2014, 123: 123-132.
Manning, R. K. A technical survey of polymer flooding projects. The University of Texas at Austin, Austin, 1983.
Mcdowell-Boyer, L. M., Hunt, J. R., Sitar, N. Particle transport through porous media. Water Resources Research, 1986, 22(13): 1901-1921.
Mendez, K. M., Pritchard, L., Reinke, S. N., et al. Toward collaborative open data science in metabolomics using Jupyter Notebooks and cloud computing. Metabolomics, 2019, 15(10): 1-16.
Mohaghegh, S., Ameri, S. Artificial neural network as a valuable tool for petroleum engineers. Paper SPE 29220 Presented at SPE Internatlonal Soclety of Petroleum Engheers, Texas, USA, January, 1995.
Murphy, M. J. Experimental analysis of electrostatic and hydrodynamic forces affecting nanoparticle retention in porous media. Austin, University of Texas at Austin, 2012.
Ogolo, N. A., Olafuyi, O. A., Onyekonwu, M. O. Enhanced oil recovery using nanoparticles. Paper SPE 160847 Presented at the SPE Saudi Arabia Section Technical Symposium and Exhibition, Al-Khobar, Saudi Arabia, 8-11 April, 2012.
Park, J. J., Lacerda, S. H., Stanley, S. K., et al. Langmuir adsorption study of the interaction of CdSe/ZnS quantum dots with model substrates: Influence of substrate surface chemistry and pH. Langmuir, 2009, 25(1): 443-450.
Pirizadeh, M., Alemohammad, N., Manthouri, M., et al. A new machine learning ensemble model for class imbalance problem of screening enhanced oil recovery methods. Journal of Petroleum Science and Engineering, 2021, 198: 108214.
Shaniv, D., Dror, I., Berkowitz, B. Effects of particle size and surface chemistry on plastic nanoparticle transport in saturated natural porous media. Chemosphere, 2021, 262: 127854.
Subasi, A., El-Amin, M. F., Darwich, T., et al. Permeability prediction of petroleum reservoirs using stochastic gradient boosting regression. Journal of Ambient Intelligence and Humanized Computing, 2020.
Van Den Doel, K., Robinson, M., Stove, C., et al. Subsurface temperature measurement using electromagnetic waves and machine learning for enhanced oil recovery. Paper Presented at 82nd EAGE Annual Conference and Exhibition, Amsterdam, Netherlands, 8-11 December, 2020.
Van Poollen, H. K. Fundamentals of Enhanced Oil Recovery. New York, USA, Elsevier, 1980.
Wang, Y., Li, Y., Pennell, K. D. Influence of electrolyte species and concentration on the aggregation and transport of fullerene nanoparticles in quartz sands. Environmental Toxicology and Chemistry: An International Journal, 2008, 27(9): 1860-1867.
Wasan, D. T., Nikolov, A. D. Spreading of nanofluids on solids. Nature, 2003, 423(6936): 156-159.
You, J., Ampomah, W., Sun, Q. Development and application of a machine learning based multi-objective optimization workflow for CO2 -EOR projects. Fuel, 2020, 264: 116758.
Yousef, A. M., Kavousi, G. P., Alnuaimi, M., at al. Predictive data analytics application for enhanced oil recovery in a mature field in the Middle East. Petroleum Exploration and Development, 2020, 47(2): 393-399.
Youssif, M. I., El-Maghraby, R. M., Saleh, S. M., et al. Silica nanofluid flooding for enhanced oil recovery in sandstone rocks. Egyptian Journal of Petroleum, 2018, 27(1): 105-110.
Zhang, T. Modeling of nanoparticle transport in porous media. Austin, The University of Texas at Austin, 2012.
Zhang, X. D. A Matrix Algebra Approach to Artificial Intelligence. Singapore, Springer, 2020.
Zhang, Y., Haghani, A. A gradient boosting method to improve travel time prediction. Transportation Research Part C: Emerging Technologies, 2015, 58: 308-324.
Zhou, K., Li, S., Zhou, X., et al. Data-driven prediction and analysis method for nanoparticle transport behavior in porous media. Measurement, 2021, 172: 108869.
Refbacks
- There are currently no refbacks.
Copyright (c) 2021 The Author(s)
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.