Open Access
ARTICLE
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:
(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
Received 21 December 2021; Accepted 08 February 2022; Issue published 27 May 2022
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.
Keywords
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