Hybrid soft computing for PVT properties prediction

Pressure-Volume-Temperature (PVT) properties are very important in the reservoir engineering computations. There are many approaches for predicting various PVT properties based on empirical correlations and statistical regression models. Last decade, researchers utilized neural networks to develop more accurate PVT correlations. These achievements of neural networks open the door to data mining techniques to play a major role in oil and gas industry. Unfortunately, the developed neural networks correlations are often limited and global correlations are usually less accurate compared to local correlations. Recently, adaptive neuro-fuzzy inference systems have been proposed as a new intelligence framework for both prediction and classification based on fuzzy clustering optimization criterion and ranking. In this paper, a genetic-neuro-fuzzy inference system is proposed for estimating PVT properties of crude oil systems.