This paper provides an overview of the following three modeling approaches – Physics-based Modeling, Machine Learning, and Hybrid Modeling (Physics-based Modeling & Machine Learning combined), and addresses their applicability for multiphase flow simulations in specific use-cases.

Physics-based modeling builds on well understood concepts in, e.g., thermodynamics, fluid dynamics, fluid modeling and optimization techniques. It requires deep domain knowledge as well as accurate fluid data and may incur significant computational cost.

Machine Learning systems are based on learning algorithms, which find relationships between sensor data and output variables in a training dataset. The approach requires a good understanding of the learning algorithms and statistics. Small datasets or changes in operational conditions limit the suitability of this approach.

Hybrid Models combine Physics with Machine Learning, and these models are on a sliding scale between pure Physics-based Models and pure Machine Learning Models. The individual use-case defines how the Hybrid Model is configured and where it sits on the sliding scale. Here, we will investigate three different use-cases:

  1. Physics supporting Machine Learning: Use physics to generate data to support training of machine learning models when data is sparse.

  2. Machine Learning supporting Physics: Use machine learning to provide additional input to physics modeling when available data does not suffice to achieve accurate numerical solutions.

  3. True Hybrid: Close the circle and use physics for feature engineering to improve the machine learning models, which then provide synthetic data as input to physics-based simulations.

We observe that there is not one universal solution. On the one hand, flow simulations may address situations which are stochastic in nature and not deterministic. Here, a machine learning model based on physics-based simulations fits the purpose well. On the other hand, there are situations when machine learning can deduce relations between parameters such that you can provide additional input into physics-based models. One such example is to address the oil-water split at very high water cuts. By adopting the appropriate combination, the hybrid approach results in superior accuracy and offers the ability to address a broader range of applications than physics or machine learning alone.

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