Hydrocarbon production systems generate huge datasets, often with time series going back many years. However, much of the data may be obsolete due to changing reservoir conditions and modification of the asset, and there may be scant data close to optimal operating conditions due to the inadequacy of existing optimization tools. It is widely recognized that data science, artificial intelligence (AI) and machine learning can contribute significantly to the optimization of production operations, and there is a trend towards hybrid AI, which combines data science with traditional physics-based simulators to deliver added value. In our work we show how to make use of physical principles in feature engineering to improve machine learning outcomes. This squeezes additional value from a pure data-based approach while avoiding expensive, time-consuming and often inaccurate simulations. Our toolbox includes energy, mass and force balances; PVT data for production fluids; order-of-magnitude analysis; and dimensional analysis. We illustrate the value of physics guided machine learning with three examples from production optimisation: First example shows a significant improvement in separator operation to achieve environmental limits for safe disposal of produced water using a root-cause analysis to identify bad actors in the production system and recommending operator actions to mitigate oil-in water issues. By physics modeling of key physical processes, such as choke-dispersion and separator efficiency, the predictions were greatly improved. Second example is a data-based VFM using physics-based feature engineering, outperforming a VFM based purely on measured data. Last use-case is a dynamic maximum separator flow capacity calculation that safely allows flow rates above static design limits. We conclude that physics-guided machine learning can add tremendous value to digitalisation rollout across a wide range of production optimisation use cases, and speed up the decision process toward mitigation of production losses in complex industrial phenomena.