  Virtual flow metering (VFM) is a cost-effective and non-intrusive technology
for inferring multi-phase flow rates in petroleum assets. Inferences about flow
rates are fundamental to decision support systems which operators extensively
rely on. Data-driven VFM, where mechanistic models are replaced with machine
learning models, has recently gained attention due to its promise of lower
maintenance costs. While excellent performance in small sample studies have
been reported in the literature, there is still considerable doubt towards the
robustness of data-driven VFM. In this paper we propose a new multi-task
learning (MTL) architecture for data-driven VFM. Our method differs from
previous methods in that it enables learning across oil and gas wells. We study
the method by modeling 55 wells from four petroleum assets. Our findings show
that MTL improves robustness over single task methods, without sacrificing
performance. MTL yields a 25-50% error reduction on average for the assets
where single task architectures are struggling.
