Prediction of reservoir brine properties using radial basis function (RBF) neural network

Aquifers, which play a prominent role as an effective tool to recover hydrocarbon from reservoirs, assist the production of hydrocarbon in various ways. In 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 extraction. Some studies have indicated that oil recovery can be increased by modifying the salinity of the injected brine in water flooding methods. Furthermore, various characteristics of brines are required for different calculations used within the petroleum industry. Consequently, it is of great significance to acquire the exact information about PVT properties of brine extracted from reservoirs. The properties of brine that are of great importance are density, enthalpy, and vapor pressure. In this study, radial basis function neural networks assisted with genetic algorithm were utilized to predict the mentioned properties. The root mean squared error of 0.270810, 0.455726, and 1.264687 were obtained for reservoir brine density, enthalpy, and vapor pressure, respectively. The predicted values obtained by the proposed models were in great agreement with experimental values. In addition, a comparison between the proposed model in this study and a previously proposed model revealed the superiority of the proposed GA-RBF model.