Context-Aware Digital Twin for Underground Storage

This plenary session is part of the Artificial Intelligence and Digital Twins for Earth Systems workshop co-hosted by USACM and the Oden Institute for Computational Engineering and Sciences at UT Austin. 

Abstract: We introduce an uncertainty-aware Digital Twin (DT) for monitoring and optimizing underground storage operations, with a focus on Geological Carbon Storage (GCS). In real-world scenarios, forward models are often misspecified due to uncertainties in subsurface dynamics and observation models. Our DT addresses this challenge by incorporating context-awareness into its neural networks to account for complexities in indirect seismic observations, including variability in rock-physics relations that link reservoir states (e.g., pressure and saturation) to time-lapse seismic responses.

To achieve this, we employ sensitivity-aware amortized Bayesian inference (SA-ABI), a simulation-based inference method that integrates sensitivity analysis into the training phase. This enables the DT to quantify and propagate uncertainty stemming from model discrepancies, particularly in rock-physics parameters. Computational efficiency is maintained through shared neural network weights that capture structural similarities across simulations based on different rock-physics models. This design supports fast amortized inference: once trained, the network can evaluate the impact of varying rock-physics parameters without retraining.

In addition to producing posterior distributions over reservoir states conditioned on observed seismic data, our approach supports “what-if” scenario analysis, where posterior samples can be drawn for different values of the DT’s context variables. The resulting context-aware DT improves decision-making under uncertainty and provides a flexible, scalable foundation for future extensions to other sensing modalities and subsurface processes.

Speaker Bio: Felix J. Herrmann graduated from Delft University of Technology in 1992 and received his Ph.D. in engineering physics from that same institution in 1997. After research positions at Stanford University and the Massachusetts Institute of Technology, he became in 2020 faculty at the University of British Columbia. In 2017, he joined the Georgia Institute of technology where he is a Georgia Research Alliance Scholar Chair in Energy, cross-appointed between the Schools of Earth & Atmospheric Sciences, Computational Science & Engineering, and Electrical & Computer Engineering. His cross-disciplinary research program spans several areas of computational imaging including seismic, and more recently, medical imaging. Dr. Herrmann is widely known for tackling challenging problems in the imaging sciences by adapting techniques from randomized linear algebra, PDE-constrained and convex optimization, high-performance computing, machine learning, and uncertainty quantification. Over his career, he has been responsible for several cost-saving innovations in industrial time-lapse seismic data acquisition and wave-equation based imaging. In 2019, he toured the world presenting the SEG Distinguished Lecture "Sometimes it pays to be cheap – Compressive time-lapse seismic data acquisition". In 2020, he was the recipient of the SEG Reginald Fessenden Award for his contributions to seismic data acquisition with compressive sensing. At Georgia Tech, he leads the Seismic Laboratory for Imaging and modeling and he is co-founder/director of the Center for Machine Learning for Seismic (ML4Seismic), designed to foster industrial research partnerships to drive innovations in artificial-intelligence assisted seismic imaging, interpretation, analysis, and time-lapse monitoring.