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.

翻译：我们应用降阶模型（ROM）技术处理含断层多孔介质中的单相流问题，采用径向基函数网格变形方法考虑岩石物性变化和断层几何形态的变异。该方法受益于混合维度框架，能有效管理由此产生的非一致网格。为简化灵敏度分析和反问题求解等复杂重复性计算，我们采用深度学习降阶模型（DL-ROM）。这种基于神经网络的非侵入式技术与传统本征正交分解（POD）方法在不同场景下进行了对比评估，结果表明DL-ROM能以令人满意的精度和效率加速复杂分析过程。