TY - JOUR
T1 - Hybrid functional networks for oil reservoir PVT characterisation
AU - Oloso, Munirudeen
AU - Hassan Sayed, Mohamed
AU - Bader-El-Den, Mohamed
AU - Buick, James
N1 - 12 month embargo.
PY - 2017/11/30
Y1 - 2017/11/30
N2 - Predicting pressure-volume-temperature (PVT) properties of black oil is one of the key processes required in a successful oil exploration. As crude oils from different regions have different properties, some researchers have used API gravity, which is used to classify crude oils, to develop different empirical correlations for different classes of black oils. However, this manual grouping may not necessarily result in correlations that appropriately capture the uncertainties in the black oils. This paper proposes intelligent clustering to group black oils before passing the clusters as inputs to the functional networks for prediction. This hybrid process gives better performance than the empirical correlations, standalone functional networks and neural network predictions.
AB - Predicting pressure-volume-temperature (PVT) properties of black oil is one of the key processes required in a successful oil exploration. As crude oils from different regions have different properties, some researchers have used API gravity, which is used to classify crude oils, to develop different empirical correlations for different classes of black oils. However, this manual grouping may not necessarily result in correlations that appropriately capture the uncertainties in the black oils. This paper proposes intelligent clustering to group black oils before passing the clusters as inputs to the functional networks for prediction. This hybrid process gives better performance than the empirical correlations, standalone functional networks and neural network predictions.
U2 - 10.1016/j.eswa.2017.06.014
DO - 10.1016/j.eswa.2017.06.014
M3 - Article
SN - 0957-4174
VL - 87
SP - 363
EP - 369
JO - Expert Systems with Applications
JF - Expert Systems with Applications
ER -