Enabling Clean Energy Resilience with Machine Learning-Empowered Underground Hydrogen Storage (Proposals Track)

Alvaro R Carbonero Gonzales (Los Alamos National Lab); Shaowen Mao (Los Alamos National Laboratory); Mohamed Mehana (Los Alamos National Lab)

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Carbon Capture & Sequestration Computer Vision & Remote Sensing


To address the urgent challenge of climate change, there is a critical need to transition away from fossil fuels towards sustainable energy systems, with renewable energy sources playing a pivotal role. However, the inherent variability of renewable energy, without effective storage solutions, often leads to imbalances between energy supply and demand. Underground Hydrogen Storage (UHS) emerges as a promising long-term storage solution to bridge this gap, yet its widespread implementation is impeded by the high computational costs associated with high fidelity UHS simulations. This paper introduces UHS from a data-driven perspective and outlines a roadmap for integrating machine learning into UHS, thereby facilitating the large-scale deployment of UHS.