基于机器学习的增强型数据同化框架用于多尺度碳酸盐岩表征
Machine learning enhanced data assimilation framework for multiscale carbonate rock characterization
AI总结：
本文提出一种基于机器学习的数据同化框架，利用实验排水相对渗透率测量高效表征微尺度结构，通过DNN-ESMDA算法实现多尺度数字岩石建模，显著提升计算效率。
摘要：
碳酸盐储层在地下碳储存、石油开采和地下氢储存中具有重要潜力。X射线计算机断层扫描（X射线CT）结合数值模拟常用于研究碳酸盐岩的多相流行为。碳酸盐岩的孔隙尺寸分布跨越多个尺度，阻碍了传统X射线CT图像的全面研究。在宏观和微观尺度上成像（多尺度成像）在这一背景下成为可行选项。然而，多尺度成像面临两个关键限制：视场和体素大小之间的权衡需要资源密集型成像，而由此产生的数字模型上的多尺度多物理场数值模拟带来巨大的计算成本。为解决这些挑战，我们提出了一种基于机器学习增强的数据同化框架，利用实验排水相对渗透率测量来高效表征微尺度结构，提供一种数据驱动的高保真多尺度数字岩石建模解决方案。我们训练了一个密集神经网络（DNN）作为多尺度孔隙网络模拟器的代理，并将其与多个数据同化（ESMDA）算法耦合。DNN-ESMDA框架同时推断微孔隙相的CO2-Brine排水相对渗透率及其相关不确定性估计，揭示每个岩石相的相对重要性并指导未来表征。我们的DNN-ESMDA框架实现了计算速度提升，将推断时间从数千小时减少到秒级，与传统多尺度数值模拟相比。鉴于这种计算效率和适用性，基于机器学习增强的ESMDA框架为表征多尺度碳酸盐岩提供了一种可推广的方法。
英文摘要：
Carbonate reservoirs offer significant capacity for subsurface carbon storage, oil production, and underground hydrogen storage. X-ray computed tomography (X-ray CT) coupled with numerical simulations is commonly used to investigate the multiphase flow behaviors in carbonate rocks. Carbonates exhibit pore size distribution across scales, hindering the comprehensive investigation with conventional X-ray CT images. Imaging samples at both macro and micro-scales (multi-scale imaging) proved to be a viable option in this context. However, multi-scale imaging faces two key limitations: the trade-off between field of view and voxel size necessitates resource-intensive imaging, while multi-scale multi-physics numerical simulations on resulting digital models incur prohibitive computational costs. To address these challenges, we propose a machine learning-enhanced data assimilation framework that leverages experimental drainage relative permeability measurements to achieve efficient characterization of micro-scale structures, delivering a data-driven solution toward a high-fidelity multiscale digital rock modeling. We train a dense neural network (DNN) as a proxy to a multi-scale pore network simulator and couple it with an ensemble smoother with multiple data assimilation (ESMDA) algorithm. DNN-ESMDA framework simultaneously infers the CO2-brine drainage relative permeability of microporosity phases with associated uncertainty estimation, revealing the relative importance of each rock phase and guiding future characterization. Our DNN-ESMDA framework achieves a computational speedup, reducing inference time from thousands of hours to seconds compared with the usage of conventional multiscale numerical simulation. Given this computational efficiency and applicability, the machine learning-enhanced ESMDA framework presents a generalizable approach for characterizing multiscale carbonate rocks.
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