Liyuan Xin^1,2,3, Xiang Rao^1,2,3,*, Xiaoyin Peng^1,2,3, Yunfeng Xu^1,2,3, Jiating Chen^1,2,3

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 >

Xi Zhang¹, Changquan Wang^1,*, Hua Wu², Xu Zhao²

FDMP-Fluid Dynamics & Materials Processing, Vol.18, No.3, pp. 661-677, 2022, DOI:10.32604/fdmp.2022.019486

Abstract The oil production predicted by means of the conventional water-drive characteristic curve is typically affected by large deviations with respect to the actual value when the so-called high water-cut stage is entered. In order to solve this problem, a new characteristic relationship between the relative permeability ratio and the average water saturation is proposed. By comparing the outcomes of different matching methods, it is verified that it can well reflect the variation characteristics of the relative permeability ratio curve. Combining the new formula with a reservoir engineering method, two new formulas are derived for the water flooding characteristic curve in… More >