Liyuan Xin1,2,3, Xiang Rao1,2,3,*, Xiaoyin Peng1,2,3, Yunfeng Xu1,2,3, Jiating Chen1,2,3
Energy Engineering, Vol.119, No.5, pp. 1905-1922, 2022, DOI:10.32604/ee.2022.019556
Abstract The rapid production dynamic prediction of water-flooding reservoirs based on well location deployment has
been the basis of production optimization of water-flooding reservoirs. Considering that the construction of
geological models with traditional numerical simulation software is complicated, the computational efficiency of
the simulation calculation is often low, and the numerical simulation tools need to be repeated iteratively in the
process of model optimization, machine learning methods have been used for fast reservoir simulation. However,
traditional artificial neural network (ANN) has large degrees of freedom, slow convergence speed, and complex
network model. This paper aims to predict the production performance of… More >
Login
Register
