Application of Deep Learning Reduced-Order Modeling for Single-Phase Flow in Faulted Porous Media
Keywords
Computational learning
Advanced Numerical Methods for Scientific Computing
Geosciences/Protection of Land and Water Resources
Code:
25/2024
Title:
Application of Deep Learning Reduced-Order Modeling for Single-Phase Flow in Faulted Porous Media
Date:
Wednesday 6th March 2024
Author(s):
Enrico Ballini e Luca Formaggia e Alessio Fumagalli e Anna Scotti e Paolo Zunino
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Abstract:
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.