Abstract
In a number of computational experiments, a meta-algorithm is used to solve the problems of the oil and gas industry. Such experiments begin in the hydrodynamic simulator, where the value of the function is calculated for specific nodal values of the parameters based on the physical laws of fluid flow through porous media. Then, the values of the function are calculated, either on a more detailed set of parameter values, or for parameter values that go beyond the nodal values.
Among other purposes, such an approach is used to calculate incremental oil production resulting from the application of various methods of enhanced oil recovery (EOR).
The authors found out that in comparison with the traditional computational experiments on a regular grid, computation using machine learning algorithms could prove more productive.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Guo, Z., Reynolds, A.C., Zhao, H.: A Physics-Based Data-Driven Model for History-Matching, Prediction and Characterization of Waterflooding Performance. Society of Petroleum Engineers. https://doi.org/10.2118/182660-MS
Shehata, A.M., El-banbi, A.H., Sayyouh, H.: Guidelines to Optimize CO2 EOR in Heterogeneous Reservoirs. Society of Petroleum Engineers. https://doi.org/10.2118/151871-MS
Weiser, A., Zarantonello, S.E.: A note on piecewise linear and multilinear table interpolation in many dimensions. Math. Comput. 50(181), 189–196 (1988)
Ghassemzadeh, S., Charkhi, A.H.: Optimization of integrated production system using advanced proxy based models. J. Nat. Gas Sci. Eng. 35, 89–96 (2016). ISSN 1875-5100
Dierckx, P.: Curve and Surface Fitting With Splines Monographs on Numerical Analysis. Oxford University Press, New York (1993)
Dyakonov, A.: Blog “Random Forest”, 14 November 2016. https://alexanderdyakonov.wordpress.com
Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001). https://doi.org/10.1023/A:1010933404324
Gashler, M., Giraud-Carrier, C., Martinez, T.: Decision tree ensemble: small heterogeneous is better than large homogeneous. In: The Seventh International Conference on Machine Learning and Applications, pp. 900–905 (2008). https://doi.org/10.1109/ICMLA.2008.154
Opitz, D., Maclin, R.: Popular ensemble methods: an empirical study. J. Artif. Intell. Res. 11, 169–198 (1999). https://doi.org/10.1613/jair.614
Pedregosa, F., et al.: Scikit-learn machine learning in python. JMLR 12, 2825–2830 (2011)
Oliphant, T.E.: Python for scientific computing. Comput. Sci. Eng. 9, 10–20 (2007). https://doi.org/10.1109/MCSE.2007.58
Jarrod Millman, K., Aivazis, M.: Python for scientists and engineers. Comput. Sci. Eng. 13, 9–12 (2011). https://doi.org/10.1109/MCSE.2011.36
van der Walt, S., Colbert, S.C., Varoquaux, G.: The NumPy array: a structure for efficient numerical computation. Comput. Sci. Eng. 13, 22–30 (2011). https://doi.org/10.1109/MCSE.2011.37
McKinney, W.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, pp. 51–56 (2010)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this paper
Cite this paper
Krasnov, F., Glavnov, N., Sitnikov, A. (2018). A Machine Learning Approach to Enhanced Oil Recovery Prediction. In: van der Aalst, W., et al. Analysis of Images, Social Networks and Texts. AIST 2017. Lecture Notes in Computer Science(), vol 10716. Springer, Cham. https://doi.org/10.1007/978-3-319-73013-4_15
Download citation
DOI: https://doi.org/10.1007/978-3-319-73013-4_15
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-73012-7
Online ISBN: 978-3-319-73013-4
eBook Packages: Computer ScienceComputer Science (R0)