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Pore-scale modeling is essential in understanding and predicting flow and transport properties of rocks. Generally, pore-scale modeling is dependent on imaging technologies such as Micro Computed Tomography (micro-CT), which provides visual confirmation into the pore microstructures of rocks at a representative scale. However, this technique is limited in the ability to provide high resolution images showing the pore-throats connecting pore bodies. Pore scale simulations of flow and transport properties of rocks are generally done on a single 3D pore microstructure image. As such, the simulated properties are only representative of the simulated pore-scale rock volume. These are the technological and computational limitations which we address here by using a stochastic pore-scale simulation approach. This approach consists of constructing hundreds of 3D pore microstructures of the same pore size distribution and overall porosity but different pore connectivity. The construction of the 3D pore microstructures incorporates the use of Mercury Injection Capillary Pressure (MICP) data to account for pore throat size distribution, and micro-CT images to account for pore body size distribution. The approach requires a small micro-CT image volume (7–19 mm3) to reveal key pore microstructural features that control flow and transport properties of highly heterogeneous rocks at the core-scale. Four carbonate rock samples were used to test the proposed approach. Permeability calculations from the introduced approach were validated by comparing laboratory measured permeability of rock cores and permeability estimated using five well-known core-scale empirical model equations. The results show that accounting for the stochastic connectivity of pores results in a probabilistic distribution of flow properties which can be used to upscale pore-scale simulated flow properties to the core-scale. The use of the introduced stochastic pore-scale simulation approach is more beneficial when there is a higher degree of heterogeneity in pore size distribution. This is shown to be the case with permeability and hydraulic tortuosity which are key controls of flow and transport processes in rocks.
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Single image multi-scale enhancement for rock Micro-CT super-resolution using residual U-Net
Micro-CT, also known as X-ray micro-computed tomography, has emerged as the primary instrument for pore-scale properties study in geological materials. Several studies have used deep learning to achieve super-resolution reconstruction in order to balance the trade-off between resolution of CT images and field of view. Nevertheless, most existing methods only work with single-scale CT scans, ignoring the possibility of using multi-scale image features for image reconstruction. In this study, we proposed a super-resolution approach via multi-scale fusion using residual U-Net for rock micro-CT image reconstruction (MS-ResUnet). The residual U-Net provides an encoder-decoder structure. In each encoder layer, several residual sequential blocks and improved residual blocks are used. The decoder is composed of convolutional ReLU residual blocks and residual chained pooling blocks. During the encoding-decoding method, information transfers between neighboring multi-resolution images are fused, resulting in richer rock characteristic information. Qualitative and quantitative comparisons of sandstone, carbonate, and coal CT images demonstrate that our proposed algorithm surpasses existing approaches. Our model accurately reconstructed the intricate details of pores in carbonate and sandstone, as well as clearly visible coal cracks.
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- Award ID(s):
- 1946231
- PAR ID:
- 10510488
- Publisher / Repository:
- ELSEVIER
- Date Published:
- Journal Name:
- Applied Computing and Geosciences
- Volume:
- 22
- Issue:
- C
- ISSN:
- 2590-1974
- Page Range / eLocation ID:
- 100165
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1016/j.acags.2024.100165
