Digital Twins in the era of generative AI - Application to Geological CO2 Storage
| Title | Digital Twins in the era of generative AI - Application to Geological CO2 Storage |
| Publication Type | Presentation |
| Year of Publication | 2024 |
| Authors | Abhinav Prakash Gahlot, Rafael Orozco, Haoyun Li, Huseyin Tuna Erdinc, Ziyi Yin, Mathias Louboutin, Felix J. Herrmann |
| Keywords | Amortized Variational Inference, Bayesian inference, CIG, conditional normalizing flows, control, data assimilation, deep learning, digital twin, FWI, GCS, Imaging, Inverse problems, MVA, RTM, Summary Statistics, Uncertainty quantification, WISE |
| 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. |
| URL | https://slim.gatech.edu/Publications/Public/Conferences/ICL/2024/gahlot2024ICLdtg |
| Citation Key | gahlot2024ICLdtg |