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• W4200434729 abstract "Sedimentary rocks are known for their complex pore system with varying morphology due to intricate diagenetic processes. The present study demonstrates the applicability of image analysis in analysing and defining reservoir rock properties. Conventional techniques provide quantitative results but fail to give information about the internal microstructure of the rock. On the other hand, digital image techniques reveal the micro and macro-pore types and their connectivity across multiple scales. Hence, we performed the digital image analysis on Field Emission Scanning Electron Microscopy (FESEM) images of sandstone and carbonate samples collected from the upper Assam and Bombay offshore basins. FESEM derived image analysis was used exclusively due to its several unique features over contemporary techniques involving lesser data acquisition, simulation time and performing analysis even on a rock chip obtained while drilling the borehole. Porosity was evaluated based on the percentage of pores available within the image, and permeability was evaluated using the Kozeny-Carman equation. Further, we developed statistical equations to understand the existence of coherence amongst these parameters. Our study shows that we could determine both open and closed porosities by this method. In addition, there is an agreement between the conventional porosity measurement and image-derived porosity for most rock samples, especially for very low and high porosity. Further, this study highlights the importance of thresholding, an essential component in evaluating porosity using digital images. We propose that the methodology developed can accurately characterise reservoirs based on pore networks using high-resolution imaging techniques. The developed methodology may be adopted to promote best practices. Since we used digital images obtained from small chip size rock samples, this method is advantageous to quickly calculate the porosity and permeability from rock chips retrieved from the sieve shaker while drilling. Digital datasets extracted from this analysis will be helpful for reservoir description and characterisation based on image-derived petrophysical parameters." @default.
• W4200434729 created "2021-12-31" @default.
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• W4200434729 date "2022-03-01" @default.
• W4200434729 modified "2023-10-16" @default.
• W4200434729 title "Pore scale image analysis for petrophysical modelling" @default.
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• W4200434729 doi "https://doi.org/10.1016/j.micron.2021.103195" @default.
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