Development of neural network models for the prediction of dewpoint pressure of retrograde gases and saturated oil viscosity of black oil systems

Abstract

Accurate prediction of gas condensate and crude oil fluid properties are critical elements in reservoir-engineering calculations. Dewpoint pressure of gas condensate reservoirs and oil viscosity of black oil systems are some of the important properties to be considered in activities such as reservoir management, hydrocarbon system optimization and flow measurement and transportation systems as well. This research focused on the use of artificial neural networks as a novel technique for the prediction of dewpoint pressure of retrograde gas and gas-saturated oil viscosity. The performance and accuracy of the new neural network models were tested against available correlations for dewpoint pressure and gas-saturated oil viscosity respectively. The artificial neural network for the prediction of dewpoint pressure was developed using a set of 802 experimental constant volume depletion (CVD) data points. This model was able to predict the dewpoint pressure with an average absolute error of 8.74% as a function of temperature, hydrocarbon and non-hydrocarbons composition, molecular weight, and specific gravity of heptanes-plus fraction. To develop the neural network model to predict oil viscosity for black oil systems at and below bubblepoint, a set of 2343 points of gas-saturated oil viscosity was used. The new model captured the underlying relationship between gas-saturated oil viscosity as an output, and the input parameters: temperature, solution gas/oil ratio, formation volume factor, pressure at and below bubblepoint, API gravity and separator-gas specific gravity. The overall performance of this model was better then the available correlations for the gas-saturated oil viscosity, with an average absolute error of 16.19%. This study showed that artificial neural network is a feasible technique and it can be used for the prediction of dewpoint pressure of retrograde gas and gas-saturated oil viscosity with more accurate results than the available correlations.