Chapter 20 Using RBF network to model the reservoir fluid behavior of black oil systems

This paper presents a new technique to model the behavior of crude oil systems. The proposed technique is using a radial basis function neural network model (RBFNM). The model predicts oil formation volume factor, solution gas-oil ratio, oil viscosity, saturated oil density, undersaturated oil compressibility, and evolved gas gravity. Input data to the RBFNM are reservoir pressure, temperature, stock tank oil gravity, and separator gas gravity. The model is trained using differential PVT analysis of numerous black-oil samples collected from various oilfields. The proposed RBFNM is tested using PVT properties of other samples that have not been used during the training process. Accuracy of the proposed network model to predict PVT properties of black-oils systems is compared to the accuracy of numerous published PVT correlations.