CCSNet.ai, an AI-powered modeling solution, revolutionises subsurface energy simulations by addressing the time constraints and uncertainties inherent in planning CO2 geological storage. Traditional approaches can take days, challenging the goal of establishing 5,000-10,000 CO2 injection wells by 2050. CCSNet.ai enhances predictions up to 1,000,000 times faster, streamlining site selection and project optimisation. Gaining global traction in the energy sector, CCSNet.ai not only addresses CO2 storage but also shows promise to reshape other energy transition technologies, aiding worldwide carbon reduction efforts.
Gege Wen is an incoming assistant professor at Imperial College London, co-appointed by the Earth Science Engineering department and the newly launched I-X initiative on Artificial Intelligence. She obtained her Ph.D. in Energy Sciences and Engineering at Stanford University, advised by Professor Sally M. Benson. Her research interest is developing computational methods for Earth and environmental science problems to help fulfill society’s energy needs and transition toward a low-carbon future. She specializes in multiphase flow and transport for CO2 geological storage and ML for scientific computing. morePortfolio LinkedIn
Catherine Callas is a Ph.D. candidate in the Benson Lab in Energy Resources Engineering at Stanford University. She is an ExxonMobil Emerging Energy Fellow, and her research is focused on site selection for carbon storage in depleted hydrocarbon reservoirs and saline reservoirs. She obtained her M.S. degree in the Atmosphere and Energy program within Civil and Environmental Engineering from Stanford University and a B.S. degree in Chemical Engineering from Brown University. Before attending Stanford, she worked as a Financial Analyst within the Fixed Income group at Goldman Sachs in New York City for three years. moreLinkedIn