Authors: Lara Arikan, Livia Fulchignoni, Daniel M. Tartakovsky
The increasing demand for faster oil and gas flow simulations can be satisfied in various ways. One of them is the use of proxy models with low computational cost. In this sense, Black Oil fluid modeling can be thought of as a simplified model of the more rigorous compositional approach. However, not all reservoir fluid compositions can be modeled through the Black Oil approach with reasonable accuracy. This paper investigates when the empirical Black Oil formulation can be used to model reservoir fluids and which set of equations represents better a particular composition.
A total of 8 and 4 traditional Black Oil equations for the gas solubility ratio (Rs) and oil formation volume factor (Bo) respectively were tested against 1626 experimental data points from a collection of 197 Brazilian fluid samples. This data was extracted from a large set of PVT reports through an automatic data mining procedure. First, samples were separated into two groups, according to the quality of the Black Oil predictions. For samples that could be appropriately represented by the equations, patterns that dictate the best correlation for each property were observed. A k-nearest neighbors classifier was trained to predict the best set of correlations for a reservoir fluid characterized by its API gravity (API), gas density (dg), gas-oil ratio (GOR), bubble point pressure (Pb), and carbon dioxide molar content. It performed at 57% accuracy for the Bo and 71% accuracy for the Rs model predictions. However, plotting these characteristics against the “best” class of model for each property shows that in many cases, the differences between the errors associated with each model are very close to each other. Therefore a better approach might be to train a multi-label classifier that would predict multiple applicable models to account for comparable accuracy levels.