This paper proposes and implements a new approach for predicting Pressure -Volume-Temperature (PVT) properties of crude oils. Instead of the usual single or multi-data point prediction for any crude oil PVT property that is described by a curve, the approach in this study predicts such a property over a specified range of required reservoir pressures. Moreover, the shapes of the predicted curves are smooth and consistent with the experimental curves. Prediction models based on Artificial Neural Networks (ANN) and two of its advances; Support Vector Regression (SVR) and Functional Networks (FN), have been developed to execute the formulated approach.
The approach has been demonstrated for viscosity and solution gas/oil ratio (GOR) curves. These two properties vary with reservoir pressures and they are often required to be estimated over a specified range of pressures. In this study, three different data sets have been used. The first Data set consists of 12 variables which are the predictors, including crude oil hydrocarbon and non-hydrocarbon compositions and some reservoir properties. The other two data sets consist of laboratory viscosity-pressure measurements and laboratory gas/oil ratio-pressure measurements for plotting the viscosity and solution GOR curves respectively. In the simulation results, SVR and FN give better performances than the conventional ANN technique. Graphical plots and two common statistical measures (root mean square error, RMSE, and absolute percent relative error, AAPRE) have been used to evaluate the performances of all the developed models.