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Hybrid functional networks for PVT characterisation

Hybrid functional networks for PVT characterisation
Hybrid functional networks for PVT characterisation
Predicting pressure volume temperature 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.
pressure volume temperature (PVT), API gravity, clustering, functional networks, neural network
936-941
IEEE
Oloso, Munirudeen A.
8e3da70c-6afc-488f-876a-8f7b244c28d6
Hassan, Mohamed G.
ce323212-f178-4d72-85cf-23cd30605cd8
Bader-El-Den, Mohamed
994f6122-b0e4-4288-b000-531d1f6ece50
Buick, James M.
c2b2f76c-1e64-43dc-8b57-d1fe018e1560
Oloso, Munirudeen A.
8e3da70c-6afc-488f-876a-8f7b244c28d6
Hassan, Mohamed G.
ce323212-f178-4d72-85cf-23cd30605cd8
Bader-El-Den, Mohamed
994f6122-b0e4-4288-b000-531d1f6ece50
Buick, James M.
c2b2f76c-1e64-43dc-8b57-d1fe018e1560

Oloso, Munirudeen A., Hassan, Mohamed G., Bader-El-Den, Mohamed and Buick, James M. (2018) Hybrid functional networks for PVT characterisation. In SAI Intelligent Systems Conference 2017. IEEE. pp. 936-941 . (doi:10.1109/IntelliSys.2017.8324242).

Record type: Conference or Workshop Item (Paper)

Abstract

Predicting pressure volume temperature 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.

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More information

Published date: 26 March 2018
Venue - Dates: 2017 Intelligent Systems Conference, United Kingdom, 2017-09-07 - 2017-09-08
Keywords: pressure volume temperature (PVT), API gravity, clustering, functional networks, neural network

Identifiers

Local EPrints ID: 438244
URI: http://eprints.soton.ac.uk/id/eprint/438244
PURE UUID: da179c14-11a6-4d2f-9168-bc8452410d0b
ORCID for Mohamed G. Hassan: ORCID iD orcid.org/0000-0003-3729-4543

Catalogue record

Date deposited: 04 Mar 2020 17:31
Last modified: 20 May 2020 01:03

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Contributors

Author: Munirudeen A. Oloso
Author: Mohamed G. Hassan ORCID iD
Author: Mohamed Bader-El-Den
Author: James M. Buick

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