  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.
