Shales will play an important role in the successful transition of energy from fossil-based resources to renewables in the coming decades. Aside from being a significant source of low-carbon intensity fuels, like natural gas, they also serve as geologic seals of subsurface formations that may be used to isolate nuclear waste, sequester CO₂, or store intermittent energy (e.g., solar hydrogen). Despite their importance, shales pose significant engineering and environmental challenges due to their nanoporous structure and extreme heterogeneity that spans at least ~10 orders of magnitude in spatial scale. Two challenges inhibit a system-level understanding: (1) the physics of fluid flow and phase behavior in shales are poorly understood due to the dominant molecular interactions between minerals and fluids under confinement, and (2) the apparent lack of scale separation that prevents a reliable (closed) description of the physics at any single scale of observation. In this review, we focus on the latter issue and discuss scale translation, which in its broadest sense is transforming data or simulations from one spatiotemporal scale to another. While effective scale translation is not exclusive to shales, but all geologic porous media, the need for it is especially acute in shales given their high degree of heterogeneity. Classical theories like homogenization, while indispensable, fail when scales are not separated. Other methods, like numerical upscaling, scale-translate in only one direction: small to large, but not the reverse, called downscaling. However, the confluence of advances in three areas are bringing challenging problems such as shales within reach: increased computational power and scalable algorithms; high-resolution imaging and multi-modal data acquisition; and machine learning to process massive amounts of data. While these advances equip geoscientists with a wide array of experimental and computational tools, no individual tool can probe the entire gamut of heterogeneity in shales. Their effective use, therefore, requires an ability to bridge between various data types obtained at different scales. The aim of this review is to present a coherent account of computational and experimental methods that may be used to achieve just that, i.e., to perform scale translation. We provide a broader definition of scale translation, one that transcends classical homogenization and upscaling methods, but is consistent with them and accommodates notions like downscaling and data translation. After a brief introduction to homogenization, we review hybrid methods, numerical upscaling and its recent extensions, multiscale computing, high-resolution imaging, and machine learning. We place particular emphasis on multiscale computing and propose an algorithmic framework to bridge between the pore (micro) and Darcy (macro) scales. Throughout the paper, we draw comparisons between the various methods and highlight their (often hidden) similarities, differences, benefits, and pitfalls. We finally conclude with two case studies on shales that exemplify some of the methods presented.

在未来几十年，页岩将在能源从化石资源成功过渡到可再生能源方面发挥重要作用。除了是天然气等低碳强度燃料的重要来源外，它们还可作为地下地层的地质密封，可用于隔离核废料、封存 CO ₂，或储存间歇性能量（例如，太阳能氢气）。尽管页岩很重要，但由于其纳米多孔结构和在空间尺度上跨越至少约 10 个数量级的极端异质性，页岩构成了重大的工程和环境挑战。有两个挑战阻碍了系统级的理解：(1) 由于矿物和流体之间的主要分子相互作用，对页岩中流体流动和相行为的物理学知之甚少，以及 (2) 明显缺乏防止水垢分离的水垢分离在任何单一观测尺度上对物理学的可靠（封闭）描述。在这篇评论中，我们关注后一个问题并讨论尺度翻译，从最广泛的意义上讲，这是将数据或模拟从一种时空尺度转换为另一种时空尺度。虽然有效的尺度转换不仅限于页岩，而是所有地质多孔介质，但鉴于页岩的高度非均质性，对它的需求在页岩中尤为迫切。同质化等经典理论虽然必不可少，但在尺度不分离时就会失败。其他方法，如数值放大，仅在一个方向上进行缩放：从小到大，但不是相反，称为缩小. 然而，三个领域的进步正在带来具有挑战性的问题，例如页岩触手可及：增加的计算能力和可扩展的算法；高分辨率成像和多模态数据采集；和机器学习来处理大量数据。虽然这些进步为地球科学家提供了广泛的实验和计算工具，但没有任何单独的工具可以探测页岩中的整个非均质性范围。因此，它们的有效使用需要能够在不同规模获得的各种数据类型之间建立桥梁。这篇综述的目的是对可用于实现这一目标的计算和实验方法进行连贯的说明，即执行尺度转换。我们提供了更广泛的尺度翻译定义，一种超越经典同质化和放大方法，但与它们一致并适应缩小和数据转换等概念的方法。在简要介绍了同质化之后，我们回顾了混合方法、数值放大及其最近的扩展、多尺度计算、高分辨率成像和机器学习。我们特别强调多尺度计算，并提出了一个算法框架来连接孔隙（微观）和达西（宏观）尺度。在整篇论文中，我们对各种方法进行了比较，并强调了它们（通常是隐藏的）相似之处、差异、优点和缺陷。我们最后以两个页岩案例研究作为结论，这些案例研究举例说明了所提出的一些方法。在简要介绍了同质化之后，我们回顾了混合方法、数值放大及其最近的扩展、多尺度计算、高分辨率成像和机器学习。我们特别强调多尺度计算，并提出了一个算法框架来连接孔隙（微观）和达西（宏观）尺度。在整篇论文中，我们对各种方法进行了比较，并强调了它们（通常是隐藏的）相似之处、差异、优点和缺陷。我们最后以两个页岩案例研究作为结论，这些案例研究举例说明了所提出的一些方法。在简要介绍了同质化之后，我们回顾了混合方法、数值放大及其最近的扩展、多尺度计算、高分辨率成像和机器学习。我们特别强调多尺度计算，并提出了一个算法框架来连接孔隙（微观）和达西（宏观）尺度。在整篇论文中，我们对各种方法进行了比较，并强调了它们（通常是隐藏的）相似之处、差异、优点和缺陷。我们最后以两个页岩案例研究作为结论，这些案例研究举例说明了所提出的一些方法。我们特别强调多尺度计算，并提出了一个算法框架来连接孔隙（微观）和达西（宏观）尺度。在整篇论文中，我们对各种方法进行了比较，并强调了它们（通常是隐藏的）相似之处、差异、优点和缺陷。我们最后以两个页岩案例研究作为结论，这些案例研究举例说明了所提出的一些方法。我们特别强调多尺度计算，并提出了一个算法框架来连接孔隙（微观）和达西（宏观）尺度。在整篇论文中，我们对各种方法进行了比较，并强调了它们（通常是隐藏的）相似之处、差异、优点和缺陷。我们最后以两个页岩案例研究作为结论，这些案例研究举例说明了所提出的一些方法。