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Prediction of Low-Permeability Reservoirs Performances Using Long and Short-Term Memory Machine Learning

Guowei Zhu*, Kangliang Guo, Haoran Yang, Xinchen Gao, Shuangshuang Zhang

School of Geosciences, Yangtze University, Wuhan, 430100, China

* Corresponding Author: Guowei Zhu. Email: email

(This article belongs to the Special Issue: Meshless, Mesh-Based and Mesh-Reduction Methods Based Analysis of Fluid Flow in Porous Media)

Fluid Dynamics & Materials Processing 2022, 18(5), 1521-1528. https://doi.org/10.32604/fdmp.2022.020942

Abstract

In order to overcome the typical limitations of numerical simulation methods used to estimate the production of low-permeability reservoirs, in this study, a new data-driven approach is proposed for the case of water-driven hypo-permeable reservoirs. In particular, given the bottlenecks of traditional recurrent neural networks in handling time series data, a neural network with long and short-term memory is used for such a purpose. This method can reduce the time required to solve a large number of partial differential equations. As such, it can therefore significantly improve the efficiency in predicting the needed production performances. Practical examples about water-driven hypotonic reservoirs are provided to demonstrate the correctness of the method and its ability to meet the requirements for practical reservoir applications.

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Cite This Article

APA Style
Zhu, G., Guo, K., Yang, H., Gao, X., Zhang, S. (2022). Prediction of low-permeability reservoirs performances using long and short-term memory machine learning. Fluid Dynamics & Materials Processing, 18(5), 1521-1528. https://doi.org/10.32604/fdmp.2022.020942
Vancouver Style
Zhu G, Guo K, Yang H, Gao X, Zhang S. Prediction of low-permeability reservoirs performances using long and short-term memory machine learning. Fluid Dyn Mater Proc. 2022;18(5):1521-1528 https://doi.org/10.32604/fdmp.2022.020942
IEEE Style
G. Zhu, K. Guo, H. Yang, X. Gao, and S. Zhang "Prediction of Low-Permeability Reservoirs Performances Using Long and Short-Term Memory Machine Learning," Fluid Dyn. Mater. Proc., vol. 18, no. 5, pp. 1521-1528. 2022. https://doi.org/10.32604/fdmp.2022.020942



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