Artificial Intelligence (AI) techniques for predicting the reservoir fluid properties of crude-oil systems

Reservoir fluid properties PVT such as oil bubble point pressure, oil formation volume factor, solution gas-oil ratio, gas formation volume factor, and gas and oil viscosities are very important in reservoir engineering computations. Perfectly, these properties should be obtained from actual laboratory measurements on samples collected from the bottom of the wellbore or at the surface. Quite often, however, these measurements are either not available, or very costly to obtain. For these reasons, there is the need for a quick and reliable method for predicting the reservoir fluid properties. Recently, Artificial Intelligence (AI) techniques were used comprehensively for this task. This study presents back propagation network (BPN), radial basis functions networks (RBF) and fuzzy logic (FL) techniques for predicting the formation volume factor, bubble point pressure, solution gas-oil ratio, the oil gravity and the gas specific gravity. These models were developed using 760 data sets collected from published sources. Statistical analysis was performed to see which of these techniques are more reliable and accurate method for predicting the reservoir fluid properties. The new fuzzy logic (FL) models outperform all the previous artificial neural network models and the most common published empirical correlations. The present models provide predictions of the formation volume factor, bubble point pressure, solution gas-oil ratio, the oil gravity and the gas specific gravity with correlation coefficient of 0.9995, 0.9995, 0.9990, 0.9791 and 0.9782, respectively.