Corpus ID: 231750051

Bayesian Neural Networks for Virtual Flow Metering: An Empirical Study

@article{Grimstad2021BayesianNN,
  title={Bayesian Neural Networks for Virtual Flow Metering: An Empirical Study},
  author={Bjarne Grimstad and Mathilde Hotvedt and A. Sandnes and O. Kolbj{\o}rnsen and L. Imsland},
  journal={ArXiv},
  year={2021},
  volume={abs/2102.01391}
}
Recent works have presented promising results from the application of machine learning (ML) to the modeling of flow rates in oil and gas wells. Encouraging results and advantageous properties of ML models, such as computationally cheap evaluation and ease of calibration to new data, have sparked optimism for the development of data-driven virtual flow meters (VFMs). Data-driven VFMs are developed in the small data regime, where it is important to question the uncertainty and robustness of… Expand

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