Algorithms for 3D Particles Characterization Using X-Ray Microtomography in Proppant Crush Test

We present image processing algorithms for a new technique of ceramic proppant crush resistance characterization. To obtain the images of the proppant material before and after the test we used X-ray microtomography. We propose a watershed-based unsupervised algorithm for segmentation of proppant particles, as well as a set of parameters for the characterization of 3D particle size, shape, and porosity. An effective approach based on central geometric moments is described. The approach is used for calculation of particles’ form factor, compactness, equivalent ellipsoid axes lengths, and lengths of projections to these axes. Obtained grain size distribution and crush resistance fit the results of conventional test measured by sieves. However, our technique has a remarkable advantage over traditional laboratory method since it allows to trace the destruction at the level of individual particles and their fragments; it grants to analyze morphological features of fines. We also provide an example describing how the approach can be used for verification of statistical hypotheses about the correlation between particles’ parameters and their crushing under load.

[1]  M. Tuller,et al.  Segmentation of X‐ray computed tomography images of porous materials: A crucial step for characterization and quantitative analysis of pore structures , 2009 .

[2]  P. Withers.  X-ray nanotomography , 2007 .

[3]  N. Otsu.  A threshold selection method from gray level histograms , 1979 .

[4]  Ilia V. Safonov,et al.  An Overview of Watershed Algorithm Implementations in Open Source Libraries , 2018, J. Imaging.

[5]  M. Blunt,et al.  Pore-scale imaging and modelling , 2013 .

[6]  Alexander G. Mamistvalov.  n-Dimensional Moment Invariants and Conceptual Mathematical Theory of Recognition n-Dimensional Solids , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  Catherine O'Sullivan,et al.  Non-invasive characterization of particle morphology of natural sands , 2012 .

[8]  Chanho Jung,et al.  Segmenting Clustered Nuclei Using H-minima Transform-Based Marker Extraction and Contour Parameterization , 2010, IEEE Transactions on Biomedical Engineering.

[9]  John D. Hunter,et al.  Matplotlib: A 2D Graphics Environment , 2007, Computing in Science & Engineering.

[10]  Gioacchino Viggiani,et al.  An investigation of single sand particle fracture using X-ray micro-tomography , 2015 .

[11]  Mohammad Piri,et al.  The effect of deformation on two-phase flow through proppant-packed fractured shale samples: A micro-scale experimental investigation , 2017 .

[12]  Andrew K. C. Wong,et al.  A new method for gray-level picture thresholding using the entropy of the histogram , 1985, Comput. Vis. Graph. Image Process..

[13]  Ryan T. Armstrong,et al.  Linking pore-scale interfacial curvature to column-scale capillary pressure , 2012 .

[14]  Ivan Lunati,et al.  Special issue in Advances in Water Resources: Pore-scale modeling and experiments , 2016 .

[15]  Karsten E. Thompson,et al.  Image-based Stokes flow modeling in bulk proppant packs and propped fractures under high loading stresses , 2015 .

[16]  Veerle Cnudde,et al.  A three-dimensional classification for mathematical pore shape description in complex carbonate reservoir rocks , 2016, Mathematical Geosciences.

[17]  Stuart D. C. Walsh,et al.  Non-invasive measurement of proppant pack deformation , 2016 .

[18]  B. Bhanu,et al.  Adaptive image segmentation using genetic and hybrid search methods , 1995, IEEE Transactions on Aerospace and Electronic Systems.

[19]  E LorensenWilliam,et al.  Marching cubes: A high resolution 3D surface construction algorithm , 1987 .

[20]  R. Ketcham,et al.  Acquisition, optimization and interpretation of X-ray computed tomographic imagery: applications to the geosciences , 2001 .

[21]  Hui Zhang,et al.  Image segmentation evaluation: A survey of unsupervised methods , 2008, Comput. Vis. Image Underst..

[22]  Oleg Yurievich Dinariev,et al.  Direct Hydrodynamic Simulation of Multiphase Flow in Porous Rock , 2014 .

[23]  A. Kwan,et al.  Particle shape analysis of coarse aggregate using digital image processing , 1999 .

[24]  Katsuhiko Kaneko,et al.  Segmentation of multi-phase X-ray computed tomography images , 2015 .

[25]  Oleg Yurievich Dinariev,et al.  Multiphase flow modeling with density functional method , 2016, Computational Geosciences.

[26]  Veerle Cnudde,et al.  Imaging and image-based fluid transport modeling at the pore scale in geological materials : a practical introduction to the current state-of-the-art , 2016 .

[27]  Luc Vincent,et al.  Morphological grayscale reconstruction in image analysis: applications and efficient algorithms , 1993, IEEE Trans. Image Process..

[28]  Richard Barnes.  Parallel Priority-Flood depression filling for trillion cell digital elevation models on desktops or clusters , 2016, Comput. Geosci..

[29]  Jianfeng Wang,et al.  3D quantitative shape analysis on form, roundness, and compactness with μCT , 2016 .

[30]  W. Brent Lindquist,et al.  Image Thresholding by Indicator Kriging , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[31]  Emmanuelle Gouillart,et al.  Analyzing microtomography data with Python and the scikit-image library , 2016, Advanced Structural and Chemical Imaging.

[32]  Josiane Zerubia,et al.  Bayesian image classification using Markov random fields , 1996, Image Vis. Comput..

[33]  Mark L. Rivers,et al.  Comparison of image segmentation methods in simulated 2D and 3D microtomographic images of soil aggregates , 2011 .

[34]  Weihong Guo,et al.  3D Clumped Cell Segmentation Using Curvature Based Seeded Watershed , 2016, J. Imaging.

[35]  I. V. Safonov,et al.  Segmentation of convex cells with partially undefined boundaries , 2006, Pattern Recognition and Image Analysis.

[36]  Alasdair N. Houston,et al.  Adaptive-window indicator kriging: A thresholding method for computed tomography images of porous media , 2013, Comput. Geosci..

[37]  Ernesto Bribiesca,et al.  An easy measure of compactness for 2D and 3D shapes , 2008, Pattern Recognit..

[38]  Adrian Sheppard,et al.  Mapping permeability in low‐resolution micro‐CT images: A multiscale statistical approach , 2016 .

[39]  Emmanuelle Gouillart,et al.  scikit-image: image processing in Python , 2014, PeerJ.