Application of Deep Learning Reduced-Order Modeling for Single-Phase Flow in Faulted Porous Media
<|reference_start|>Application of Deep Learning Reduced-Order Modeling for Single-Phase Flow in Faulted Porous Media: We apply reduced-order modeling (ROM) techniques to single-phase flow in faulted porous media, accounting for changing rock properties and fault geometry variations using a radial basis function mesh deformation method. This approach benefits from a mixed-dimensional framework that effectively manages the resulting non-conforming mesh. To streamline complex and repetitive calculations such as sensitivity analysis and solution of inverse problems, we utilize the Deep Learning Reduced Order Model (DL-ROM). This non-intrusive neural network-based technique is evaluated against the traditional Proper Orthogonal Decomposition (POD) method across various scenarios, demonstrating DL-ROM's capacity to expedite complex analyses with promising accuracy and efficiency.<|reference_end|>
@article{ballini2024application,
    title={Application of Deep Learning Reduced-Order Modeling for Single-Phase
  Flow in Faulted Porous Media},
    author={Enrico Ballini, Luca Formaggia, Alessio Fumagalli, Anna Scotti, Paolo
  Zunino},
    journal={arXiv preprint arXiv:2403.03678},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.03678},
    primaryClass={math.NA cs.NA}
}