SUBSURFACE FRACTURE NETWORK CHARACTERIZATION AND GEO-ENERGY SYSTEM DESIGN OPTIMIZATION
Seminars
Summer Semester
Climate change has spurred ambitious renewable energy endeavors, dedicated to cultivating a low-carbon, resource-savvy, and climate-resilient planetary sphere, within which geothermal energy emerges as a notable player in the transition beyond fossil fuels. Machine learning and optimization have been burgeoned as a potent methodology for informed decision-making in enhanced geothermal systems, aiming to concurrently maximize economic yield, ensure enduring geothermal energy provision, and curtail carbon emissions. However, addressing a multitude of design parameters inherent in computationally intensive physics-driven simulations constitutes a formidable impediment for geothermal design optimization, as well as across a broad range of scientific and engineering domains. Toward accelerating scientific simulation, design and discovery with machine learning, this research delves into cutting-edge methodologies, including diffusion model for generative inversion, and multi-objective optimization for geothermal energy systems, towards the characterization and design optimization of subsurface energy systems, with a particular focus on fractured geothermal resources. Our work is poised to advance the state-of-the-art renewable geothermal energy system and enable widespread application to accelerate the discovery of optimal designs for complex systems.
Additional information: Mr. Guodong CHEN, u3008598@connect.hku.hk