期刊
APPLIED SOFT COMPUTING
卷 112, 期 -, 页码 -出版社
ELSEVIER
DOI: 10.1016/j.asoc.2021.107776
关键词
Neural network; Bayesian inference; Variational inference; Virtual flow metering; Heteroscedasticity
资金
- Solution Seeker AS
Recent works have shown promising results in using machine learning for modeling flow rates in oil and gas wells. This paper introduces a probabilistic virtual flow meter based on Bayesian neural networks, which helps to describe uncertainty in the model and measurements using variational inference. The research findings suggest the need for alternative strategies to enhance the robustness of data-driven virtual flow meters.
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 models. The modeling of uncertainty may help to build trust in models, which is a prerequisite for industrial applications. The contribution of this paper is the introduction of a probabilistic VFM based on Bayesian neural networks. Uncertainty in the model and measurements is described, and the paper shows how to perform approximate Bayesian inference using variational inference. The method is studied by modeling on a large and heterogeneous dataset, consisting of 60 wells across five different oil and gas assets. The predictive performance is analyzed on historical and future test data, where an average error of 4%-6% and 8%-13% is achieved for the 50% best performing models, respectively. Variational inference appears to provide more robust predictions than the reference approach on future data. Prediction performance and uncertainty calibration is explored in detail and discussed in light of four data challenges. The findings motivate the development of alternative strategies to improve the robustness of data-driven VFMs. (C) 2021 The Authors. Published by Elsevier B.V.
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