---
title: "Context- and uncertainty-aware Digital Twins for Integrated Reservoir Management"
author: 
  - name: Felix J. Herrmann
    orcid: "0000-0003-1180-2167"
    email: felix.herrmann@gatech.edu
    affiliations: 
    - ref: gatech
affiliations:
  - id: gatech
    name: Seismic Laboratory for Imaging and Modeling (SLIM), Georgia Institute of Technology
date: last-modified
format: docx
---


## Abstract

We present a new framework for Digital Twins (DTs) that supports uncertainty-aware monitoring, optimization, and risk management of underground storage operations, with a particular focus on Geological Carbon Storage (GCS) and Enhanced Oil Recovery (EOR). The proposed DT addresses key challenges in reservoir characterization, surveillance, and management by integrating generative AI methods with simulation-based inference, enabling scalable, probabilistic decision support under uncertainty.

Our approach combines sensitivity-aware amortized Bayesian inference (SA-ABI) with ensemble-based data assimilation to efficiently incorporate multimodal time-lapse observations, such as seismic and well data, into dynamic reservoir models. By embedding context-awareness into neural networks, we account for variability in rock-physics relationships linking pressure, saturation, and seismic responses, and quantify uncertainties arising from model misspecifications---e.g. usage of patchy versus uniform saturation models. Computational efficiency is maintained through weight sharing across the network trained on different rock-physics scenarios, enabling rapid evaluation of the impact changing the underlying rock-physics model.

The DT is built on industry-strength open-source software: `JutulDarcy.jl` for fully coupled multiphase flow simulations and `JUDI.jl`, a seismic inversion package leveraging the `Devito` finite-difference framework. This computational foundation allows scalable integration of physics-based simulations and data assimilation across large reservoir models, while retaining flexibility to adapt to varying physical contexts such as the inclusion of pressure-dependence of the compressional wavespeed. 

The resulting DT not only delivers posterior distributions for the reservoir's state (pressure/saturation) conditioned on available observations but also enables rapid “what-if” scenario analysis by drawing posterior samples across a range of physical contexts without retraining. This capability supports proactive storage management, optimization of injection operations, and improved assurance of storage conformance.

<!-- ## Executive Summary

*We introduce a new Digital Twin (DT) designed to improve monitoring, optimization, and risk management for underground storage operations, including Geological Carbon Storage (GCS) and Enhanced Oil Recovery (EOR). This approach uses advanced simulation and machine learning techniques to combine reservoir flow models and seismic data, enabling real-time, uncertainty-aware decision support. The DT accounts for the natural variability of subsurface processes and addresses limitations of traditional modeling by continuously learning from incoming observations. Built on scalable, industry-grade simulation and inversion tools, the system allows operators to quickly assess “what-if” scenarios, optimize injection strategies, and strengthen confidence in storage performance. By providing a flexible and efficient platform for dynamic reservoir management, this framework supports safer, more reliable, and cost-effective subsurface energy operations.* -->

## Bio 

Felix 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 artificial-intelligence assisted seismic imaging, interpretation, analysis, and time-lapse 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. In 2023, the manuscript entitled [“Learned multiphysics inversion with differentiable programming and machine learning”](https://library.seg.org/doi/10.1190/tle42070474.1) was the most downloaded paper of 2023 in Society of Exploration Geophysicist's [The Leading Edge](https://library.seg.org/journal/leedff).  

[Seismic Laboratory for Imaging and Modeling]:https://slim.gatech.edu
[Georgia Institute of Technology]:https://www.gatech.edu
[https://slim.gatech.edu]:https://slim.gatech.edu
[Center for Machine Learning for Seismic]:https://slim.gatech.edu/projects/center-for-machine-learning-for-seismic
[Geological Carbon Storage]:https://slim.gatech.edu/research/geological-carbon-storage
[EAS]:https://eas.gatech.edu
[CSE]:https://cse.gatech.edu
[ECE]:https://ece.gatech.edu

<!-- 
We present a new framework for Digital Twins (DTs) that supports uncertainty-aware monitoring, optimization, and risk management of underground storage operations, with a particular focus on Geological Carbon Storage (GCS). The proposed DT addresses key challenges in reservoir characterization, surveillance, and management by integrating generative ML methods with simulation-based inference, enabling scalable, probabilistic decision support under uncertainty.

Our approach combines sensitivity-aware amortized Bayesian inference (SA-ABI) with ensemble-based data assimilation to efficiently incorporate multimodal time-lapse observations, such as seismic and well data, into dynamic reservoir models. By embedding context-awareness into neural networks, we account for variability in rock-physics relationships linking pressure, saturation, and seismic responses, and quantify uncertainties arising from model misspecifications. Neural Wave-Based Inference, physics-informed summary statistics, and differentiable programming are leveraged to maintain computational tractability despite the high dimensionality and complexity of nonlinear multiphysics models.

The resulting DT not only delivers posterior distributions over reservoir states conditioned on available observations but also enables rapid “what-if” scenario analysis by drawing posterior samples across a range of operational and physical contexts without retraining. This capability supports proactive storage management, optimization of injection operations, and improved assurance of storage conformance, while providing a flexible foundation for extensions to other subsurface energy and storage applications.

⸻

Would you like a second, slightly shorter version as well in case the workshop has a word or character limit?
(SEG/SPE often wants abstracts around 300–400 words.)





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 models. 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.
 -->
