@Article{fdmp.2022.020942,
AUTHOR = {Guowei Zhu, Kangliang Guo, Haoran Yang, Xinchen Gao, Shuangshuang Zhang},
TITLE = {Prediction of Low-Permeability Reservoirs Performances Using Long and Short-Term Memory Machine Learning},
JOURNAL = {Fluid Dynamics \& Materials Processing},
VOLUME = {18},
YEAR = {2022},
NUMBER = {5},
PAGES = {1521--1528},
URL = {http://www.techscience.com/fdmp/v18n5/47962},
ISSN = {1555-2578},
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.},
DOI = {10.32604/fdmp.2022.020942}
}