MrOmar Aleassa

My research aims to support the safe and efficient deployment of underground hydrogen storage as part of the transition to low-carbon energy systems. By combining reservoir simulation, geomechanics, and physics-informed machine learning, my work seeks to improve the prediction of hydrogen flow, pressure behaviour, and caprock integrity during cyclic injection and withdrawal. This research has the potential to reduce modelling time, improve operational decision-making, and support safer storage-site design and management. My broader work on artificial intelligence in renewable energy forecasting also contributes to understanding how advanced computational methods can improve the reliability and integration of renewable energy systems.