HETEROGENEOUS ENSEMBLE LEARNING FOR VIRTUAL FLOW METERING APPLICATIONS
With the increase of marginal fields and deepwater offshore fields in the petroleum industry, the demand for continuous and cost-effective multiphase flow monitoring becomes increasingly significant for reservoir management, operational diagnosis, and well-level production optimization. Virtual f...
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my-utp-utpedia.219882021-09-29T09:33:24Z http://utpedia.utp.edu.my/21988/ HETEROGENEOUS ENSEMBLE LEARNING FOR VIRTUAL FLOW METERING APPLICATIONS AZIZ HASAN, TAREQ AL-QUTAMI TK Electrical engineering. Electronics Nuclear engineering With the increase of marginal fields and deepwater offshore fields in the petroleum industry, the demand for continuous and cost-effective multiphase flow monitoring becomes increasingly significant for reservoir management, operational diagnosis, and well-level production optimization. Virtual flow metering (VFM) is a software-based computational model that represents an attractive solution to meet these rising demands and accomplish fully integrated operations. VFM also plays a significant role in augmenting and backing up physical multiphase flow meters. However, mode-driven VFMs are difficult to deploy and very expensive to maintain, while current data-driven VFM studies are limited, suffer inherent limitations, and do not deliver performance analysis over the complete operating envelope. 2017-09 Thesis NonPeerReviewed application/pdf en http://utpedia.utp.edu.my/21988/1/2017%20-%20ELECTRICAL%20%26%20ELECTRONIC%20-%20HETROGENEOUS%20ENSEMBLE%20LEARNING%20FOR%20VIRTUAL%20FLOW%20METERING%20APPLICATIONS%20-%20TAREQ%20AZIZ%20HASAN%20AL-QUTAMI.pdf AZIZ HASAN, TAREQ AL-QUTAMI (2017) HETEROGENEOUS ENSEMBLE LEARNING FOR VIRTUAL FLOW METERING APPLICATIONS. Masters thesis, Universiti Teknologi PETRONAS. |
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TK Electrical engineering. Electronics Nuclear engineering AZIZ HASAN, TAREQ AL-QUTAMI HETEROGENEOUS ENSEMBLE LEARNING FOR VIRTUAL FLOW METERING APPLICATIONS |
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With the increase of marginal fields and deepwater offshore fields in the petroleum
industry, the demand for continuous and cost-effective multiphase flow monitoring
becomes increasingly significant for reservoir management, operational diagnosis, and
well-level production optimization. Virtual flow metering (VFM) is a software-based
computational model that represents an attractive solution to meet these rising demands and
accomplish fully integrated operations. VFM also plays a significant role in augmenting
and backing up physical multiphase flow meters. However, mode-driven VFMs are
difficult to deploy and very expensive to maintain, while current data-driven VFM studies
are limited, suffer inherent limitations, and do not deliver performance analysis over the
complete operating envelope. |
format |
Thesis |
author |
AZIZ HASAN, TAREQ AL-QUTAMI |
author_facet |
AZIZ HASAN, TAREQ AL-QUTAMI |
author_sort |
AZIZ HASAN, TAREQ AL-QUTAMI |
title |
HETEROGENEOUS ENSEMBLE LEARNING FOR VIRTUAL FLOW
METERING APPLICATIONS |
title_short |
HETEROGENEOUS ENSEMBLE LEARNING FOR VIRTUAL FLOW
METERING APPLICATIONS |
title_full |
HETEROGENEOUS ENSEMBLE LEARNING FOR VIRTUAL FLOW
METERING APPLICATIONS |
title_fullStr |
HETEROGENEOUS ENSEMBLE LEARNING FOR VIRTUAL FLOW
METERING APPLICATIONS |
title_full_unstemmed |
HETEROGENEOUS ENSEMBLE LEARNING FOR VIRTUAL FLOW
METERING APPLICATIONS |
title_sort |
heterogeneous ensemble learning for virtual flow
metering applications |
publishDate |
2017 |
url |
http://utpedia.utp.edu.my/21988/1/2017%20-%20ELECTRICAL%20%26%20ELECTRONIC%20-%20HETROGENEOUS%20ENSEMBLE%20LEARNING%20FOR%20VIRTUAL%20FLOW%20METERING%20APPLICATIONS%20-%20TAREQ%20AZIZ%20HASAN%20AL-QUTAMI.pdf http://utpedia.utp.edu.my/21988/ |
_version_ |
1739832936786558976 |
score |
13.19449 |