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Hybrid functional networks for PVT characterisation

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Hybrid functional networks for PVT characterisation. / Oloso, Munirudeen; Bader-El-Den, Mohamed; Buick, James; Hassan Sayed, Mohamed.

SAI Intelligent Systems Conference 2017: IntelliSys 2017. IEEE, 2018. p. 936-941.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Harvard

Oloso, M, Bader-El-Den, M, Buick, J & Hassan Sayed, M 2018, Hybrid functional networks for PVT characterisation. in SAI Intelligent Systems Conference 2017: IntelliSys 2017. IEEE, pp. 936-941, 2017 Intelligent Systems Conference, London, United Kingdom, 7/09/17. https://doi.org/10.1109/IntelliSys.2017.8324242

APA

Oloso, M., Bader-El-Den, M., Buick, J., & Hassan Sayed, M. (2018). Hybrid functional networks for PVT characterisation. In SAI Intelligent Systems Conference 2017: IntelliSys 2017 (pp. 936-941). IEEE. https://doi.org/10.1109/IntelliSys.2017.8324242

Vancouver

Oloso M, Bader-El-Den M, Buick J, Hassan Sayed M. Hybrid functional networks for PVT characterisation. In SAI Intelligent Systems Conference 2017: IntelliSys 2017. IEEE. 2018. p. 936-941 https://doi.org/10.1109/IntelliSys.2017.8324242

Author

Oloso, Munirudeen ; Bader-El-Den, Mohamed ; Buick, James ; Hassan Sayed, Mohamed. / Hybrid functional networks for PVT characterisation. SAI Intelligent Systems Conference 2017: IntelliSys 2017. IEEE, 2018. pp. 936-941

Bibtex

@inproceedings{b1d6565558154e99b52174bc223bd867,
title = "Hybrid functional networks for PVT characterisation",
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.",
keywords = "pressure volume temperature (PVT), API gravity, clustering, functional networks, neural network",
author = "Munirudeen Oloso and Mohamed Bader-El-Den and James Buick and {Hassan Sayed}, Mohamed",
year = "2018",
month = mar,
day = "26",
doi = "10.1109/IntelliSys.2017.8324242",
language = "English",
isbn = "978-1-5090-6436-6 ",
pages = "936--941",
booktitle = "SAI Intelligent Systems Conference 2017",
publisher = "IEEE",
note = "2017 Intelligent Systems Conference, IntelliSys 2017 ; Conference date: 07-09-2017 Through 08-09-2017",
url = "http://www.saiconference.com/IntelliSys",

}

RIS

TY - GEN

T1 - Hybrid functional networks for PVT characterisation

AU - Oloso, Munirudeen

AU - Bader-El-Den, Mohamed

AU - Buick, James

AU - Hassan Sayed, Mohamed

PY - 2018/3/26

Y1 - 2018/3/26

N2 - 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.

AB - 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.

KW - pressure volume temperature (PVT)

KW - API gravity

KW - clustering

KW - functional networks

KW - neural network

UR - http://saiconference.com/Downloads/IntelliSys2017/Agenda.pdf

U2 - 10.1109/IntelliSys.2017.8324242

DO - 10.1109/IntelliSys.2017.8324242

M3 - Conference contribution

SN - 978-1-5090-6436-6

SP - 936

EP - 941

BT - SAI Intelligent Systems Conference 2017

PB - IEEE

T2 - 2017 Intelligent Systems Conference

Y2 - 7 September 2017 through 8 September 2017

ER -

ID: 7829361