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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| Format: | Thesis |
| Language: | English |
| Published: |
2017
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| Subjects: | |
| Online Access: | 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/ |
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| Summary: | 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. |
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