Fu Jinlong, Wang Min, Chen Bin, Wang Jinsheng, Xiao Dunhui, Luo Min, Evans Ben
Zienkiewicz Institute for Modelling, Data and AI, Faculty of Science and Engineering, Swansea University, Swansea, SA1 8EN UK.
Fluid Dynamics and Solid Mechanics Group, Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM 87545 USA.
Eng Comput. 2023 May 19:1-32. doi: 10.1007/s00366-023-01841-8.
Understanding the microstructure-property relationships of porous media is of great practical significance, based on which macroscopic physical properties can be directly derived from measurable microstructural informatics. However, establishing reliable microstructure-property mappings in an explicit manner is difficult, due to the intricacy, stochasticity, and heterogeneity of porous microstructures. In this paper, a data-driven computational framework is presented to investigate the inherent microstructure-permeability linkage for natural porous rocks, where multiple techniques are integrated together, including microscopy imaging, stochastic reconstruction, microstructural characterization, pore-scale simulation, feature selection, and data-driven modeling. A large number of 3D digital rocks with a wide porosity range are acquired from microscopy imaging and stochastic reconstruction techniques. A broad variety of morphological descriptors are used to quantitatively characterize pore microstructures from different perspectives, and they compose the raw feature pool for feature selection. High-fidelity lattice Boltzmann simulations are conducted to resolve fluid flow passing through porous media, from which reliable permeability references are obtained. The optimal feature set that best represents permeability is identified through a performance-oriented feature selection process, upon which a cost-effective surrogate model is rapidly fitted to approximate the microstructure-permeability mapping via data-driven modeling. This surrogate model exhibits great advantages over empirical/analytical formulas in terms of prediction accuracy and generalization capacity, which can predict reliable permeability values spanning four orders of magnitude. Besides, feature selection also greatly enhances the interpretability of the data-driven prediction model, from which new insights into the mechanism of how microstructural characteristics determine intrinsic permeability are obtained.
理解多孔介质的微观结构-性质关系具有重要的实际意义，基于此，宏观物理性质可直接从可测量的微观结构信息学中推导得出。然而，由于多孔微观结构的复杂性、随机性和非均质性，以明确的方式建立可靠的微观结构-性质映射是困难的。本文提出了一个数据驱动的计算框架，用于研究天然多孔岩石固有的微观结构-渗透率联系，该框架集成了多种技术，包括显微镜成像、随机重建、微观结构表征、孔隙尺度模拟、特征选择和数据驱动建模。通过显微镜成像和随机重建技术获取了大量孔隙率范围广泛的三维数字岩石。使用了各种各样的形态描述符从不同角度定量表征孔隙微观结构，它们构成了用于特征选择的原始特征库。进行了高保真格子玻尔兹曼模拟以解析流体通过多孔介质的流动，从中获得可靠的渗透率参考值。通过以性能为导向的特征选择过程确定最能代表渗透率的最优特征集，在此基础上，通过数据驱动建模快速拟合出一个经济高效的替代模型，以近似微观结构-渗透率映射。该替代模型在预测准确性和泛化能力方面比经验/解析公式具有很大优势，能够预测跨越四个数量级的可靠渗透率值。此外，特征选择还大大增强了数据驱动预测模型的可解释性，从中获得了关于微观结构特征如何决定固有渗透率机制的新见解。
