InterPore2018 New Orleans

Session

Parallel 5-D

Parallel 5-D

15 May 2018, 14:35

New Orleans

86. Multi-Step Segmentation Protocol for Digital Rock Systems

Chaitanya Pradhan

15/05/2018, 14:37

MS 2.06: New Trends in Image Processing: From Discrete Tomography over Machine Learning to in-situ Contact Angle Measurement

Oral 20 Minutes

A key element in digital rock physics is the segmentation of µ-CT scanned grayscale images into their constituent components, i.e. rock and pore. In this work, a multistep approach of segmentation is presented where the high degree of correlation present in neighboring voxels is utilized. The first step in the workflow is a modified fuzzy c-means algorithm which incorporates spatial...

405. 4D-µCT analysis through piecewise linear fitting

Marjolein Heyndrickx (Ghent University)

15/05/2018, 14:55

MS 2.06: New Trends in Image Processing: From Discrete Tomography over Machine Learning to in-situ Contact Angle Measurement

Oral 20 Minutes

4D-µCT is an increasingly popular tool to study dynamic processes in situ, for example in material science and porous media studies. The technique allows to resolve changes in a material's microstructure over time and in three spatial dimensions. Typically, a sample is scanned continuously during a relevant time-span, corresponding to multiple sequential conventional µCT scans, which are...

585. Quantum-computational approach to discrete tomography for porous media

Daniel O'Malley (Los Alamos National Laboratory)

15/05/2018, 15:13

MS 2.06: New Trends in Image Processing: From Discrete Tomography over Machine Learning to in-situ Contact Angle Measurement

Oral 20 Minutes

Making predictions about flow and transport in a porous medium requires knowledge of the heterogeneous properties of the porous medium such as permeability. Computational methods for inverse analysis are commonly used to infer these properties from quantities that are more readily observable (such as hydraulic head in a hydrologic context). We present a method for computational inverse...

586. Shape analysis for grains and pores on 3d digital images

Fabian Biebl (Math2Market)

15/05/2018, 15:40

MS 2.06: New Trends in Image Processing: From Discrete Tomography over Machine Learning to in-situ Contact Angle Measurement

Oral 20 Minutes

With our Software GeoDict® [1] we are able to segment 3d scans, e.g. CT or FIB-SEM scans. This segmented data can then be analyzed using GeoDict's new modules GrainFind and PoreFind. In this talk, we introduce GeoDict and present the features of these two modules.

For each individual pore, PoreFind extracts an equivalent ellipsoid from the segmented data. From the equivalent ellipsoids, we...

897. A multiscale segmentation strategy for low-resolution, whole-core computerized tomography images of carbonates

Prof. Maša Prodanović (The University of Texas at Austin)

15/05/2018, 15:58

MS 2.06: New Trends in Image Processing: From Discrete Tomography over Machine Learning to in-situ Contact Angle Measurement

Oral 20 Minutes

Image segmentation is a critical step in any digital rock workflow. The classification of voxels into phases representing grains, pores, and sub-resolution features affects all the subsequent quantitative analyses performed from the tomograms as well as numerical modeling of physical processes within pore and solid phases. Although it has been an active research field for many years,...

986. Fast Evaluation of Rock Permeability from Images using Physics-Based Simulations and Machine Learning

Xiaolong Yin (Colorado School of Mines)

15/05/2018, 16:16

MS 2.06: New Trends in Image Processing: From Discrete Tomography over Machine Learning to in-situ Contact Angle Measurement

Oral 20 Minutes

Physical properties of rocks such as permeability, relatively permeability, and dispersion coefficient are of critical importance for prediction of subsurface flow and transport. Advances in microscopic imaging have made it possible to obtain the pore-scale microstructure of rock samples via SEM, TEM, or CT scan at low costs and fast turn-around time. Image-based reconstructions have also...

781. Enhanced grain partitioning of microtomography segmented images

Nicholas Skrivanos (Louisiana State University Petroleum Engineering Department)

15/05/2018, 16:52

MS 2.06: New Trends in Image Processing: From Discrete Tomography over Machine Learning to in-situ Contact Angle Measurement

Poster + 3 Minute Pitch

Grain partitioning of three-dimensional microtomography segmented images provides valuable in-situ properties and statistics that allow for accurate particle and structure characterization of porous media samples. There are many applications of this technology, ranging from analyzing core samples in petroleum engineering and soil science to developing novel structures in material science. This...