BEACON: Bayesian Experimental design Acceleration with Conditional Normalizing flows $-$ a case study in optimal monitor well placement for CO$_2$ sequestration
<|reference_start|>BEACON: Bayesian Experimental design Acceleration with Conditional Normalizing flows $-$ a case study in optimal monitor well placement for CO$_2$ sequestration: CO$_2$ sequestration is a crucial engineering solution for mitigating climate change. However, the uncertain nature of reservoir properties, necessitates rigorous monitoring of CO$_2$ plumes to prevent risks such as leakage, induced seismicity, or breaching licensed boundaries. To address this, project managers use borehole wells for direct CO$_2$ and pressure monitoring at specific locations. Given the high costs associated with drilling, it is crucial to strategically place a limited number of wells to ensure maximally effective monitoring within budgetary constraints. Our approach for selecting well locations integrates fluid-flow solvers for forecasting plume trajectories with generative neural networks for plume inference uncertainty. Our methodology is extensible to three-dimensional domains and is developed within a Bayesian framework for optimal experimental design, ensuring scalability and mathematical optimality. We use a realistic case study to verify these claims by demonstrating our method's application in a large scale domain and optimal performance as compared to baseline well placement.<|reference_end|>
@article{orozco2024beacon:,
    title={BEACON: Bayesian Experimental design Acceleration with Conditional
  Normalizing flows $-$ a case study in optimal monitor well placement for
  CO$_2$ sequestration},
    author={Rafael Orozco, Abhinav Gahlot, Felix J. Herrmann},
    journal={arXiv preprint arXiv:2404.00075},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.00075},
    primaryClass={cs.LG math-ph math.MP}
}