Abstract Fine-scale discrete fracture simulations provide a natural means to quantify the matrix-fracture fluxes and to provide reference solutions for upscaling approaches such as dual porosity/dual permeability models. Since typically the fine-scale simulations are computationally demanding, and the fractured reservoirs are highly heterogeneous, it is desirable to parametrize the fracture geometry and to obtain coarse-scale model closures using precomputed fine-scale results. We show that this can be done for the case of two-dimensional geometries and compressible single-phase flows. Specifically, a set of parameters linked to a coarse-scale grid block can be mapped to the underlying fracture geometry via a convolutional neural network. In particular, if a matrix-fracture transfer function can be parametrized with a number of parameters spatially varying on a coarse scale, the shape of the transfer function per grid block can be learned from fine-scale simulations.

中文翻译：

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来自离散裂缝模拟的双孔隙度模型闭合的机器学习

摘要 精细尺度离散裂缝模拟提供了一种自然的方法来量化基质裂缝通量，并为双孔隙度/双渗透率模型等放大方法提供参考解决方案。由于通常精细模拟的计算要求很高，并且裂缝性储层高度非均质，因此需要参数化裂缝几何形状并使用预先计算的精细结果获得粗尺度模型闭合。我们表明，这可以在二维几何形状和可压缩单相流的情况下完成。具体来说，可以通过卷积神经网络将与粗尺度网格块相关联的一组参数映射到基础裂缝几何结构。特别是，