Digital Twins in the era of generative AI u2014 Application to Geological  CO2 Storage

Professor Felix J. Herrmann will deliver the ESE Departmental Seminar on Thursday the 6th of June 2024: “Digital Twins in the era of generative AI — Application to Geological CO2 Storage”

Join us in room G41 – RSM Building – on Thursday 6th of June at 12h15.

Abstract

Our industry is experiencing significant changes due to AI and the challenges of the energy transition. While some view these changes as threats, recent advances in AI offer unique opportunities, especially in the context of Digital Twins for subsurface monitoring and control. IBM defines “A digital twin is a virtual representation of an object or system that spans its lifecycle, is updated from real-time data, and uses simulation, machine learning and reasoning to help decision-making.” During this talk, I will explore these concepts and their significance in addressing the challenges of monitoring & control of geological CO2 storage projects. This talk also aims to illustrate how Digital Twins can serve as a platform to integrate the seemingly disparate and siloed fields of geophysics and reservoir engineering

About the speaker

Profile picture of Felix HerrmannFelix J. Herrmann is a professor with appointments at the College of Sciences (EAS), Computing (CSE), and Engineering (ECE) at the Georgia Institute of Technology. He leads the Seismic Laboratory for Imaging and modeling (SLIM) and he is co-founder/director of the Center for Machine Learning for Seismic (ML4Seismic). This Center is designed to foster industrial research partnerships and drive innovations in artificialintelligence assisted seismic imaging, interpretation, analysis, and timelapse monitoring. In 2019, he toured the world presenting the SEG Distinguished Lecture. In 2020, he was the recipient of the SEG Reginald Fessenden Award for his contributions to seismic data acquisition with compressive sensing. Since his arrival at Georgia Tech in 2017, he expanded his research program to include machine learning for Bayesian wave-equation based inference using techniques from simulation-based inference. More recently, he started a research program on seismic monitoring of Geological Carbon Storage, which includes the development of an uncertainty-aware Digital Twin.

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