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Reservoir performance predictions and optimization of field development have traditionally relied on computationally expensive physics-based models for flow and transport in porous media. More recently, there is an increasing trend to use purely data-driven models based on big data and machine learning techniques. The goal here is to exploit the multitude of data sources to extract intelligence, improve operational efficiency and optimize reservoir performance. In this workshop, we explored the opportunities presented by combining the data driven models (data scientists) with physics-based models (domain experts), to provide a balanced and informed view of reservoir insights and create predictive and generalizable models while enforcing known physical constraints and addressing gaps in the data.