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Hybrid functional networks for oil reservoir PVT characterisation

Hybrid functional networks for oil reservoir PVT characterisation
Hybrid functional networks for oil reservoir PVT characterisation
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.
0957-4174
363-369
Oloso, Munirudeen
8e3da70c-6afc-488f-876a-8f7b244c28d6
Hassan Sayed, Mohamed
ce323212-f178-4d72-85cf-23cd30605cd8
Bader-El-Den, Mohamed
994f6122-b0e4-4288-b000-531d1f6ece50
Buick, James
c2b2f76c-1e64-43dc-8b57-d1fe018e1560
Oloso, Munirudeen
8e3da70c-6afc-488f-876a-8f7b244c28d6
Hassan Sayed, Mohamed
ce323212-f178-4d72-85cf-23cd30605cd8
Bader-El-Den, Mohamed
994f6122-b0e4-4288-b000-531d1f6ece50
Buick, James
c2b2f76c-1e64-43dc-8b57-d1fe018e1560

Oloso, Munirudeen, Hassan Sayed, Mohamed, Bader-El-Den, Mohamed and Buick, James (2017) Hybrid functional networks for oil reservoir PVT characterisation. Expert Systems with Applications, 87, 363-369. (doi:10.1016/j.eswa.2017.06.014).

Record type: Article

Abstract

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.

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More information

Accepted/In Press date: 9 June 2017
e-pub ahead of print date: 12 June 2017
Published date: 30 November 2017
Additional Information: 12 month embargo.

Identifiers

Local EPrints ID: 438236
URI: http://eprints.soton.ac.uk/id/eprint/438236
ISSN: 0957-4174
PURE UUID: 9e5ff17d-4b29-436d-b918-a7c93c00b821
ORCID for Mohamed Hassan Sayed: ORCID iD orcid.org/0000-0003-3729-4543

Catalogue record

Date deposited: 04 Mar 2020 17:31
Last modified: 07 Oct 2020 02:27

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Contributors

Author: Munirudeen Oloso
Author: Mohamed Hassan Sayed ORCID iD
Author: Mohamed Bader-El-Den
Author: James Buick

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