Real-time production monitoring in oil and gas industry has become very significant particularly as fields become economically marginal and reservoirs deplete. Virtual flow meters (VFMs) are intelligent systems that infer multiphase flow rates from ancillary measurements and are attractive and cost-effective solutions to meet monitoring demands, reduce operational costs, and improve oil recovery efficiency. Current VFMs are very challenging to develop and very expensive to maintain, most of which were developed for wells with dedicated physical meters where there exists an abundance of well test data. This study proposes a VFM system based on ensemble learning for fields with common metering infrastructure where data generated is very limited. The proposed method generates diverse neural network (NN) learners by manipulating training data, NN architecture and learning trajectory. Adaptive simulated annealing optimization is proposed to select the best subset of learners and the optimal combining strategy. The proposed method was evaluated using actual well test data and managed to achieve a remarkable performance with average errors of 4.7 and 2.4 for liquid and gas flow rates respectively. The accuracy of the developed VFM was also analyzed using cumulative deviation plot where the predictions are within a maximum deviation of ± 15. Furthermore, the proposed ensemble method was compared to standard bagging and stacking and remarkable improvements have been observed in both accuracy and ensemble size. The proposed VFM is expected to be easier to develop and maintain than model-driven VFMs since only well test samples are required to tune the model. It is hoped that the developed VFM can augment and backup physical meters, improve data reconciliation, and assist in reservoir management and flow assurance ultimately leading to a more efficient oil recovery and less operating and maintenance costs. © 2017 Elsevier Ltd