ANN-Based Prediction of Laboratory-Scale Performance of CO2-Foam Flooding for Improving Oil Recovery
- 64 Downloads
Improving oil recovery by CO2 injection continues to gain momentum in mature oil fields due to its favorable industrial and environmental benefits. One remediation for the poor sweep efficiency of CO2 is co-injection of surfactants to generate CO2-foams in reservoirs. However, it is essential to minimize the expensive and time-consuming experiments required during the laboratory screening of this EOR process for a given reservoir. In this regard, methods to predict RF and Q from reservoir characteristics based on existing laboratory test data are worthwhile. In this paper, we develop the RF and Q prediction models involving optimized multi-layer perceptron (MLP) and radial basis function (RBF) neural networks. These models are applied to a compiled dataset of 214 data records of published CO2-foam injection tests into oil-reservoir cores. The RF and Q prediction derived applying these two models to the compiled dataset are compared. Statistical accuracy measures of the predictions achieved for an independent testing subset (20% of the data records) indicate for RF (MLP: RMSE = 0.0236, R2 = 0.9988; for RBF: RMSE = 0.0197, R2 = 0.9991) and for Q (MLP: RMSE = 0.0283, R2 = 0.9971; for RBF: RMSE = 0.0092, R2 = 0.9991) the excellent prediction performance of the developed networks.
KeywordsCO2-foam subsurface injection Enhanced oil recovery CO2 underground sequestration Multi-layer perceptron Radial basis function CO2 performance
- Awad, M. (2010). Optimization RBFNNs parameters using genetic algorithms: Applied on function approximation. International Journal of Computer Science and Security (IJCSS), 4(3), 295.Google Scholar
- Barati-Harooni, A., Najafi-Marghmaleki, A., Tatar, A., & Mohammadi, A. H. (2016). Experimental and modeling studies on adsorption of a nonionic surfactant on sandstone minerals in enhanced oil recovery process with surfactant flooding. Journal of Molecular Liquids, 220, 1022–1032.CrossRefGoogle Scholar
- Berger, P. D., Berger, C. H., & Hsu, I. K. (2000). Anionic surfactants based on alkene sulfonic acid. U.S. Patent 6,043,391.Google Scholar
- Broomhead, D. S., & Lowe, D. (1988). Radial basis functions, multi-variable functional interpolation and adaptive networks (No. RSRE-MEMO-4148). Royal Signals and Radar Establishment Malvern (United Kingdom). http://www.dtic.mil/dtic/tr/fulltext/u2/a196234.pdf.
- Du, K. L., & Swamy, M. N. S. (2014). Radial basis function networks. In Neural networks and statistical learning. London: Springer. https://doi.org/10.1007/978-1-4471-5571-3_10.
- Goodall, C. R. (1993) Computation using the QR decomposition. In Handbook in statistics (Vol. 9). Amsterdam: Elsevier/North-Holland. https://www.mathworks.com/help/stats/leverage.html.
- Kalyanaraman, N., Arnold, C., Gupta, A., Tsau, J. S., & Ghahfarokhi, R. B. (2017). Stability improvement of CO2 foam for enhanced oil-recovery applications using polyelectrolytes and polyelectrolyte complex nanoparticles. Journal of Applied Polymer Science. https://doi.org/10.1002/app.44491.Google Scholar
- Sajic, B., Dong, X., Matache, C., & Gariepy, C. (2006). Low solids, high viscosity fabric softener compositions and process for making the same. U.S. Patent Application 11/436,924.Google Scholar
- Shen, C., Nguyen, Q.P., Huh, C., & Rossen, W. R. (2006). Does polymer stabilize foam in porous media?. In SPE/DOE symposium on improved oil recovery. Society of Petroleum Engineers.Google Scholar
- Sheng, J. (Ed.). (2013). Enhanced oil recovery field case studies. Boston: Gulf Professional Publishing.Google Scholar
- Simjoo, M., & Zitha, P. L. J. (2018). New insight into immiscible foam for enhancing oil recovery. In N. Narayanan, B. Mohanadhas, & V. Mangottiri (Eds.), Flow and transport in subsurface environment (pp. 91–115). Springer transactions in civil and environmental engineering. Singapore: Springer. https://doi.org/10.1007/978-981-10-8773-8_3.CrossRefGoogle Scholar
- Yang, X. S., & Deb, S. (2009). December. Cuckoo search via Lévy flights. In World congress on nature & biologically inspired computing, 2009. NaBIC 2009 (pp. 210–214). IEEE.Google Scholar
- Ydstebø, T. (2013). Enhanced oil recovery by CO 2 and CO 2-foam in fractured carbonates. MSc. thesis, The University of Bergen.Google Scholar
- Zhao, J. (2017). Comprehensive experimental study on foam flooding for enhancing heavy oil recovery. Doctoral dissertation, Faculty of Graduate Studies and Research, University of Regina.Google Scholar