ARTIFICIAL INTELLIGENCE IN SUBSURFACE ENERGY SYSTEMS: A COMPREHENSIVE REVIEW OF CO₂ STORAGE, HYDROGEN STORAGE, AND GEOTHERMAL ENERGY

Abstract

The global shift toward clean energy is turning deep underground rock layers into a vital infrastructure for storing energy and cutting carbon emissions. This paper examines how computer models and smart data tools are being used to improve three major areas: capturing and burying carbon dioxide, storing hydrogen underground for later use, and tapping into volcanic heat for geothermal energy. By analyzing 46 relevant peer-reviewed studies in this field, this review shows how these smart computer models can quickly assess underground sites, monitor for gas leaks, handle the stress of repeatedly refilling and emptying storage zones, and prevent small earthquakes. The paper also looks at the big picture by comparing the pros, cons, and real-world readiness of these different technologies. Finally, we address current hurdles, such as data gaps and the difficulty of understanding how these 'black box' computers make decisions, while exploring future trends, such as secure data sharing and automated safety systems. Ultimately, bringing computer data science and earth sciences together creates a reliable digital foundation for managing our future clean energy networks.