Hybrid functional networks for oil reservoir PVT characterisation

Munirudeen Oloso, Mohamed Hassan Sayed, Mohamed Bader-El-Den, James Buick

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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.
Original languageEnglish
Pages (from-to)363-369
Number of pages7
JournalExpert Systems with Applications
Early online date12 Jun 2017
Publication statusPublished - 30 Nov 2017


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