HETEROGENEOUS ENSEMBLE LEARNING FOR VIRTUAL FLOW METERING APPLICATIONS - AZIZ HASAN, TAREQ AL-QUTAMI (2017)
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.