Comparing Two Methods of Neural Networks to Evaluate Dead Oil Viscosity

Abstract

Reservoir characterization and asset management require comprehensive information about formation fluids. In fact, it is not possible to find accurate solutions to many petroleum engineering problems without having accurate pressure-volume-temperature (PVT) data. Traditionally, fluid information has been obtained by capturing samples and then by measuring the PVT properties in a laboratory. In recent years, neural network has been applied to a large number of petroleum engineering problems. In this paper, a multi-layer perception neural network and radial basis function network (both optimized by a genetic algorithm) were used to evaluate the dead oil viscosity of crude oil, and it was found out that the estimated dead oil viscosity by the multi-layer perception neural network was more accurate than the one obtained by radial basis function network.

Keywords

• Dead Oil Viscosity
• Radial Basis Function (RBF)
• Multi-layer Perceptron (MLP)
• Genetic algorithm
• Neural Network