Estimating permeability from thin sections without reconstruction: Digital rock study of 3D properties from 2D images
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TL;DR: The proposed models are proposed to first calibrate the proposed models using the available 3D information on the rock microstructure and then predict the permeability for rocks from the same geological formation for which only 2D thin sections are available.
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About: This article is published in Computers & Geosciences. The article was published on 31 May 2017. and is currently open access. The article focuses on the topics: Rock microstructure & Permeability (earth sciences).
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Citations
Application of upscaling methods for fluid flow and mass transport in multi-scale heterogeneous media: A critical review
Xiaoying Zhang,Funing Ma,Shangxian Yin,Corey D. Wallace,Mohamad Reza Soltanian,Zhenxue Dai,Robert W. Ritzi,Ziqi Ma,Chuanjun Zhan,Xiaoshu Lü +9 more
TL;DR: This review covers, in a comprehensive manner, the upscaling approaches available in the literature and their applications on various processes, such as advection, dispersion, matrix diffusion, sorption, and chemical reactions.
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References and benchmarks for pore-scale flow simulated using micro-CT images of porous media and digital rocks
Nishank Saxena,Ronny Hofmann,Faruk O. Alpak,Steffen Berg,Jesse Dietderich,Umang Agarwal,Kunj Tandon,Sander Hunter,Justin Freeman,Ove Bjørn Wilson +9 more
TL;DR: A novel reference dataset is generated to quantify the impact of numerical solvers, boundary conditions, and simulation platforms on permeability of microstructures ranging from idealized pipes to digital rocks and finds that more stringent convergence criteria can improve solver accuracy but at the expense of longer computation time.
134
Towards Bayesian Quantification of Permeability in Micro-scale Porous Structures – The Database of Micro Networks
Babak Fazelabdolabadi,Mohammad Hossein Golestan +1 more
- 01 Dec 2020
TL;DR: A Bayesian framework to quantify the absolute permeability of water in a porous structure from the geometry and clustering parameters of its underlying pore-throat network, using a Database of Micro Networks for micro-scale porous structures as main input stream for the proposed Bayesian scheme.
Comparative analysis on pore‐scale permeability prediction on micro‐CT images of rock using numerical and empirical approaches
TL;DR: Empirical relation can over‐estimate permeability by a magnification of 50 or more, particularly for those strong heterogeneous structures reported in this study, Nevertheless, empirical relation is still applicable for artificial rocks.
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Application of deep learning for semantic segmentation of sandstone thin sections
TL;DR: The effectiveness of the models, in predicting a petrology class in a thin section, strongly correlates with the amount of labeled data available to train the model to interpret the class in question.
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