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Impact of the Spatial Correlation of Microporosity on Fluid Flow in Carbonate Rocks

机译:微孔间隙对碳酸盐岩中流体流动的影响

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Pore-network modelling, or digital petrophysics, is an emerging technology which allows computing physically consistent two- and three-phase flow functions such as relative permeability and capillary pressure curves at arbitrary wettability. Considering the difficulty in measuring these functions reliably in the laboratory, pore-network modelling is increasingly used to guide and complement costly and time-consuming SCAL programmes. Although pore-network modelling is now well-established for clastic reservoirs, applying it to carbonate rocks is significantly more challenging due to their complex and multi-scale pore structure, comprising micro- and macro-pores as well as cracks and fractures. We have hence developed a novel method to integrate pore-networks, which have been extracted at multiple scales directly from CT images at different resolutions, into a single pore-network model that can be used in subsequent calculations of two- and three-phase flow functions. This method has been validated by comparing computed two-phase relative permeability and capillary pressure curves for a multiscale network to the corresponding laboratory measurements. However, in this approach it is important to accurately consider the impact of the spatial distribution of fine network elements, which are extracted from high-resolution images. We therefore show the impact of the spatial correlation of high-resolution porosity on single- and two-phase fluid flow and propose a model that allows us to simulate the spatial correlation between coarse-scale pores and fine-scale porosity in the absence of a suitable CT image that segments the rock into three phases (pores, sub-resolution matrix porosity, and solid). We demonstrate the impact of the spatial correlation of microporosity on absolute and relative permeabilities by applying this model to various datasets, including CT images of an off-shore carbonate reservoir. This demonstrates that the spatial correlation of microporosity is one of the key factors controlling recovery from carbonate reservoirs and that our new method allows us to quantify it.
机译:孔网络建模或数字岩石物理学是一种新兴技术,其允许计算物理上一致的两阶段和三相流动功能,例如在任意润湿性处具有相对渗透性和毛细管压力曲线。考虑到在实验室中可靠地测量这些功能的困难,孔网建模越来越多地用于指导和补充昂贵和耗时的巨大节目。虽然孔网造型现在为碎屑储层提供充分建立,但由于其复杂和多尺寸的孔隙结构,将其施加到碳酸盐岩石中的挑战性显着较大,包括微观和宏观孔以及裂缝和裂缝。因此,我们已经开发了一种集成孔网络的新方法,该方法直接从不同分辨率的CT图像直接从多个尺度提取到一个孔网络模型中,该模型可以用于两种和三相流的后续计算职能。通过将计算的两相相对渗透率和毛细管压力曲线与多尺度网络进行比较至相应的实验室测量,已经验证了该方法。然而,在这种方法中,重要的是准确地考虑从高分辨率图像中提取的精细网络元件的空间分布的影响。因此,我们展示了高分辨率孔隙度对单相流体流动的空间相关性的影响,并提出了一种允许我们在不存在A的情况下模拟粗尺寸孔隙和微尺度孔隙之间的空间相关性的模型合适的CT图像将岩石分成三相(孔,子分辨率基质孔隙率和固体)。我们通过将该模型应用于各种数据集,展示了微孔孔隙度的空间相关性对绝对和相对渗透性的影响,包括散壳碳酸盐储层的CT图像。这表明微孔的空间相关性是控制从碳酸盐储层恢复的关键因素之一,并且我们的新方法允许我们量化它。

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