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

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 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, and 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, model selection prior to training is challenging, and overhead on gray-box model development and testing is unavoidable.