When is gray-box modeling advantageous for virtual flow metering?

• URL: http://arxiv.org/abs/2110.05034v1
• Date: Mon, 11 Oct 2021 07:01:48 GMT
• Title: When is gray-box modeling advantageous for virtual flow metering?
• Authors: M. Hotvedt, B. Grimstad, D. Ljungquist, L. Imsland
• Abstract summary: Integration of physics and machine learning in virtual flow metering applications is known as gray-box modeling. This article examines scenarios where a gray-box model is expected to outperform physics-based and data-driven models.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Integration of physics and machine learning in virtual flow metering applications is known as gray-box modeling. The combination is believed to enhance multiphase flow rate predictions. However, the superiority of gray-box models is yet to be demonstrated in the literature. This article examines scenarios where a gray-box model is expected to outperform physics-based and data-driven models. The experiments are conducted with synthetic data where properties of the underlying data generating process are known and controlled. The results show that a gray-box model yields increased prediction accuracy over a physics-based model in the presence of process-model mismatch. They also show improvements over a data-driven model when the amount of available data is small. On the other hand, gray-box and data-driven models are similarly influenced by noisy measurements. Lastly, the results indicate that a gray-box approach may be advantageous in nonstationary process conditions. Unfortunately, choosing the best model prior to training is challenging, and overhead on model development is unavoidable.

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