ORCID Profile
0000-0001-8917-0775
Current Organisation
Indian Institute of Technology Delhi
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Publisher: Society of Petroleum Engineers
Date: 2019
DOI: 10.2118/196088-MS
Publisher: Springer Science and Business Media LLC
Date: 06-10-2020
Publisher: Elsevier BV
Date: 12-2019
Publisher: Springer Science and Business Media LLC
Date: 18-10-2022
Publisher: Springer Singapore
Date: 17-10-2019
Publisher: IEEE
Date: 07-2018
Publisher: Elsevier BV
Date: 10-2023
Publisher: IOP Publishing
Date: 19-02-2020
Publisher: Springer Singapore
Date: 17-10-2019
Publisher: ARMA
Date: 26-06-2022
Abstract: Volumetric image data of rocks often need sophisticated image processing steps in any rock physics and petrophysics workflow. While the segmentation is highly dependent on the quality of data, a trade-off between resolution and field of view is inevitable. This work attempts to resolve this using multiple-point statistics that have long been used for generating synthetic images, though mostly applied to sandstone rocks where the heterogeneity is significantly less than that of carbonates. These algorithms work by sequentially populating a grid to emulate the observed image. However, finding the optimum kernel parameters is crucial to capturing the spatial characteristics of the data. Also, when dealing with multiple images, finding a single set of kernel parameters might not be a trivial task. Further these methods work by computing a covariance kernel that scales as the third power with the number of training ex les, thus not scaling well with the more data. Therefore, we seek to design a single image-based upscaling method that would help alleviate these difficulties. We test the proposed methodology on carbonate rock s le data which are known for their complexities at various scales. In this study images of 4 s les are considered. An ups le-deblur is developed that consistently works better than the conventional bicubic interpolation based ups ling technique. For this, a low-resolution 2D image s le is extracted from an X-ray microtomography dataset which was then subjected to a Random Forest based ups ling algorithm. It is found that the data from low scale could be improved to form a single super-resolution image. The algorithm produces an image that is always better than the bicubic algorithm. We anticipate this strategy would help design advanced algorithms where the amount of training ex les is less. Digital Rock Physics workflow has gained significant attention from researchers due to its promising accuracy to characterize rocks and predict desired properties through numerical simulation (Andrä et al., 2013a, 2013b). Digital rock physics is a numerical workflow to compute and simulate various rock properties such as permeability, electrical conductivity, and elastic moduli based on high-resolution representations of the complex pore geometry obtained from imaging (Andrä et al., 2013a, 2013b Arns et al., 2019 Devarapalli et al., 2017 Mehmani et al., 2020 Wildenschild & Sheppard, 2013).
Publisher: Elsevier BV
Date: 06-2018
No related grants have been discovered for Anil Kumar, PhD.