Prediction of reservoir brine properties using radial basis function (RBF) neural network
作者机构：oung Researchers and Elite ClubNorth Tehran BranchIslamic Azad UniversityTehranIran Department of Petroleum EngineeringAhwaz Faculty of Petroleum Engineering Petroleum University of TechnologyAhwazIran Southern Cross UniversitySchool of EnvironmentScience and EngineeringPO Box 157LismoreNSWAustralia School of Chemical EngineeringYeungnam UniversityGyeungsanRepublic of Korea
出 版 物：《Petroleum》 (油气（英文）)
年 卷 期：2015年第1卷第4期
页 面：349-357页
学科分类：081803[工学-地质工程] 08[工学] 0818[工学-地质资源与地质工程]
主 题：Reservoir brine Intelligent method Density Enthalpy Vapor pressure Radial basis function neural network
摘 要：Aquifers,which play a prominent role as an effective tool to recover hydrocarbon from reservoirs,assist the production of hydrocarbon in various *** so-called water flooding methods,the pressure of the reservoir is intensified by the injection of water into the formation,increasing the capacity of the reservoir to allow for more hydrocarbon *** studies have indicated that oil recovery can be increased by modifying the salinity of the injected brine in water flooding ***,various characteristics of brines are required for different calculations used within the petroleum ***,it is of great significance to acquire the exact information about PVT properties of brine extracted from *** properties of brine that are of great importance are density,enthalpy,and vapor *** this study,radial basis function neural networks assisted with genetic algorithm were utilized to predict the mentioned *** root mean squared error of 0.270810,0.455726,and 1.264687 were obtained for reservoir brine density,enthalpy,and vapor pressure,*** predicted values obtained by the proposed models were in great agreement with experimental *** addition,a comparison between the proposed model in this study and a previously proposed model revealed the superiority of the proposed GA-RBF model.
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