Fokin Mikhail I, Nikitin Viktor V, Duchkov Anton A
Institute of Petroleum Geology and Geophysics SB RAS, 630090 Novosibirsk, Russia.
Advanced Photon Source, Argonne National Laboratory, Lemont, IL 60439, USA.
J Synchrotron Radiat. 2023 Sep 1;30(Pt 5):978-988. doi: 10.1107/S1600577523005635. Epub 2023 Jul 19.
Fast multi-phase processes in methane hydrate bearing samples pose a challenge for quantitative micro-computed tomography study and experiment steering due to complex tomographic data analysis involving time-consuming segmentation procedures. This is because of the sample's multi-scale structure, which changes over time, low contrast between solid and fluid materials, and the large amount of data acquired during dynamic processes. Here, a hybrid approach is proposed for the automatic segmentation of tomographic data from time-resolved imaging of methane gas-hydrate formation in sandy granular media, which includes a deep-learning 3D U-Net model. To prepare a training dataset for the 3D U-Net, a technique to automate data labeling based on sample-specific information about the mineral matrix immobility and occasional fluid movement in pores is proposed. Automatic segmentation allowed for studying properties of the hydrate growth in pores, as well as dynamic processes such as incremental flow and redistribution of pore brine. Results of the quantitative analysis showed that for typical gas-hydrate stability parameters (100 bar methane pressure, 7°C temperature) the rate of formation is slow (less than 1% per hour), after which the surface area of contact between brine and gas increases, resulting in faster formation (2.5% per hour). Hydrate growth reaches the saturation point after 11 h of the experiment. Finally, the efficacy of the proposed segmentation scheme in on-the-fly automatic data analysis and experiment steering with zooming to regions of interest is demonstrated.
由于涉及耗时的分割程序的复杂断层扫描数据分析，含甲烷水合物样品中的快速多相过程对定量微计算机断层扫描研究和实验控制构成了挑战。这是由于样品的多尺度结构会随时间变化，固体和流体材料之间的对比度低，以及动态过程中获取的大量数据。在此，提出了一种混合方法，用于对含沙粒状介质中甲烷水合物形成的时间分辨成像的断层扫描数据进行自动分割，该方法包括一个深度学习3D U-Net模型。为了为3D U-Net准备训练数据集，提出了一种基于关于矿物基质固定性和孔隙中偶尔的流体运动的特定于样品的信息来自动标记数据的技术。自动分割有助于研究孔隙中水合物生长的特性，以及诸如孔隙盐水的增量流动和重新分布等动态过程。定量分析结果表明，对于典型的天然气水合物稳定性参数（100巴甲烷压力、7°C温度），形成速率较慢（每小时小于1%），在此之后盐水与气体之间的接触表面积增加，导致形成速度加快（每小时2.5%）。在实验11小时后，水合物生长达到饱和点。最后，证明了所提出的分割方案在实时自动数据分析和通过缩放至感兴趣区域进行实验控制方面的有效性。
