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

作     者：Guowei Zhu Kangliang Guo Haoran Yang Xinchen Gao Shuangshuang Zhang 

作者机构：School of GeosciencesYangtze UniversityWuhan430100China 

出 版 物：《Fluid Dynamics & Materials Processing》 (流体力学与材料加工（英文）)

年 卷 期：2022年第18卷第5期

页      面：1521-1528页

核心收录：

学科分类：0202[经济学-应用经济学] 02[经济学] 020205[经济学-产业经济学] 0805[工学-材料科学与工程（可授工学、理学学位）] 

基　　金：The authors received no specific funding for this study 

主　　题：Water flooding flow in porous media data-driven LSTM CFD 

摘      要：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 *** 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 *** method can reduce the time required to solve a large number of partial differential *** such,it can therefore significantly improve the efficiency in predicting the needed production *** 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.