Forecasting density, oil formation volume factor and bubble point pressure of crude oil systems based on nonlinear system identification approach
Abstract Accurate predictions of fluid properties, such as density, oil formation volume factor and bubble point pressure, are essentials for all reservoir engineering calculations. In this paper, an approach based on nonlinear system identification modeling; Nonlinear ARX (NARX) and Hammerstein Wiener (HW) predictive model, is proposed for forecasting the pressure/volume/temperature (PVT) properties of crude oil systems. To this end, two datasets; one containing 168 PVT samples from different Iranian oil reservoirs and other a databank containing 755 data from various geographical locations, were employed to construct (i.e. train) and evaluate (i.e. test) the models. Simulation results demonstrate that the proposed NARX and HW models outperform previously employed methods including three types of artificial neural networks models (committee machine, multilayer perceptron and radial basis function) two types of ANFIS models (grid partition and fuzzy c mean) and several empirical correlations with the smallest prediction error, and that they are reliable models for predicting the oil properties in reservoirs engineering among other soft computing approaches.