Application of Deep Learning Reduced-Order Modeling for Single-Phase Flow in Faulted Porous Media

2024/03/06 by Enrico Ballini, Luca Formaggia, Ballini, Enrico +7 · 1 citation
Earth and Planetary Sciences · Engineering · #Enhanced Oil Recovery Techniques #FOS: Mathematics #Hydraulic Fracturing and Reservoir Analysis #Numerical Analysis (math.NA) #Seismic Imaging and Inversion Techniques

paper · pdf · doi:10.48550/arxiv.2403.03678

openalex publication_date 2024/03/06 · openalex created_date 2024/03/08 · openalex updated_date 2026/07/28

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.

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