Using a Committee Machine With Artificial Neural Networks To Predict PVT Properties of Iran Crude Oil

Abstract

Summary Reservoir-fluid properties are very important in material-balance calculations, well testing, and reserves estimates. Ideally, those data should be obtained experimentally. Sometimes, the results obtained from experimental tests are not reliable or accessible. In this study, we predict the pressure/volume/temperature (PVT) properties by a new artificial-neural-network (ANN) model using component mole percent, solution gas/oil ratio (GOR) (Rs), bubblepoint pressure (Pb), reservoir pressure, API oil gravity, and temperature as input data. The employed ANN model is from the committee machine type. The designed model processes its inputs using two parallel multilayer perceptron (MLP) networks, and then recombines their results. The results obtained show that the committee-machine model is a dependable network for prediction of PVT properties in reservoirs among the other ANNs and empirical correlations.