Reconstruction of Вazhenov Shales
A 3D Microstructural Analysis
- Fig. 1: BSE SEM image of the shale.
- Fig. 2: a – the 3D reconstruction of the structure: gray - mineral matrix, blue - pore space, brown - kerogen. b - the 3D reconstruction of pores. c - joint 3D reconstruction of pores and kerogen: blue - pore space, brown - kerogen. d – part of kerogen volume with pores. e - pores in the kerogen. f – the skeletonization of the pore space.
- Fig. 3: 3D reconstruction of the area 2 in figure 1: a – gray - mineral matrix, blue - pores and kerogen, yellow - pyrite. b – the 3D reconstruction of pyrite inclusions.
Focused Ion Beam / Scanning Electron Microscopy tomography is an unique method for the 3D description of different materials on the micro- and nanoscale. The application of this method to the microstructural study of gas and oil shales is presented. The 3D microstructure of the shales is characterized quantitatively with special attention to the pore and kerogen volume and pore connectivity.
One of the goals of the gas and oil shales microstructural study is the analysis of multiscale porosity. Pores are located in the areas, containing organic compounds, particularly kerogen, and in the mineral matrix. The analysis of the porosity is required for the characterization of core samples and the development of the technology for hydrocarbon recovery. The parameters, which have to be determined, are: a) pore size distribution, b) total pores volume and c) pores connectivity. Different methods are used for the solution of these problems like mercury intrusion  and gas sorption porosimetry , the X-ray tomography [3,4]. However, the porosimetry provides an averaged information on pore distribution. The traditional X-ray tomography has limited space resolution and do not reveal the pore space at the nanoscale. Nowadays one of the frequently used methods of the porous material characterization is the scanning electron microscopy (SEM) combined with the focused ion beam (FIB) , so-called “slice-and-view” method. This method allows the three-dimensional (3D) reconstruction of the microstructure, in particular porous space, by successive imaging (by SEM) and slicing (by FIB) the sample. An important step of the 3D reconstruction is the SEM images segmentation, which allows identifying pores (images binarization). Often this task is complicated due to peculiarities of the SEM contrast formation and specific appearance of pores in SEM images. The solution of these problems and completion of all processing steps results in generation of pores surface model and that allows obtaining the quantitative characteristics of the pore space, including the connectivity of pores. The example of such study is presented in the paper.
Material and Methods
The study of pore space was performed on a number of Bazhenov shales samples, characterized by different thermal maturity of kerogen on which its porosity depends.
Bazhenov formation is one of the largest Russian shale formation (Western Siberia) with unconventional hydrocarbon reserves, formed by sediments of the seabed in the late Jurassic and early Cretaceous period. The composition of the Bazhenov formation rocks is characterized by a large volume fraction of organic matter with the domination of the kerogen. Other components, determined by X-ray diffraction, SEM and energy-dispersive X-ray spectroscopy, were silica minerals (e.g., quartz), carbonates, clay minerals and pyrite. Mineral and organic compounds contain pores and cracks. Their sizes, morphology and distribution differ over a wide range over the samples. The 3D microstructure, including quantitative pore space characterization, was revealed with FIB/SEM nanotomography through serial sectioning, which is known as “slice-and-view” method. Helios, Scios and Versa 3D (all three FEI, USA) DualBeam FIB/SEM systems with the registration of secondary (SE) and back-scattered electrons (BSE) were used. The images were processed with Avizo and Amira software (FEI, USA). The 3D reconstruction, i.e. the surface models generation, was performed on binarized images, which were produced by the segmentation of SEM images. The automatic segmentation by the selection of a threshold level for pores was almost impossible due to the high depth of field value, which is the disadvantage in the case of a pore back wall visualization. The charging of oxide minerals also leads to the deterioration of images. Therefore, an advanced image processing, including several steps of image filtering, namely noise reduction, sharpening, FFT-filtering, artifact neutralization, shading correction, was performed. The pores volume distribution was calculated on the basis of the 3D binarized image dataset. Next procedure was the skeletonization of pore space and the estimation of pores connectivity. The skeletonization process represents extraction from image data the centerline of interconnected regions, such as pores network. Then the pore space was virtually filled with spheres with their centers located on the skeleton. The radii of the spheres reflect the shortest distance from the skeleton to the pore surface. After the 3D reconstruction it was easy to estimate the volume fractions of the pores, kerogen, pyrite and mineral matrix in the sample.
A typical SEM image of the shale sample is shown in figure 1. Two different areas can be distinguished in the sample: kerogen component dominates in one area and it was depicted by a yellow square and marked as “1”. A mineral component is dominated in area “2” which is marked by red square. The 3D reconstruction was performed on similar areas and the results are presented in figure 2 and figure 3. The overall microstructure consists of three main components: mineral matrix, kerogen space and pyrite inclusions. An example of 3D reconstruction of the kerogen-rich volume is presented in figure 2. The kerogen in this sample is characterized by the high level of thermal maturity and high density of pores of different sizes. The pores in the kerogen looked mostly equi-axed. More irregular pores were in mineral matrix and typically they were larger in size. The volume fractions, estimated from the reconstructed volume, are: pores - 10% (fig. 2b), kerogen – 12% (fig. 2c), the pores in the kerogen – 13% (fig. 2d,e). The skeletonization of the pore space (fig. 2f) makes clear the connectivity of the pores: the overall - 59% and the kerogen area – 50%.
Figure 3a represents an example of mineral rich sample 3D reconstruction, and the reconstruction of pyrite inclusions is separately shown (fig. 3b). Estimation of the volume fractions of the pore-kerogen space, pyrite in the reconstruction gives the following result: volume fraction of the pore-kerogen space – 16%; volume fraction of the pyrite inclusions – 2%.
Detailed results of 3D reconstruction of the pore space is presented in .
In this work we demonstrated the application of FIB/SEM tomography - “slice-and-view” method to the study of the shales from Bazhenov formation. Experimental data acquisition by this method combined with advanced computer processing provides unique possibilities for quantitative characterization of cores samples, containing kerogen and pores, on micro- and nanoscale, including: volume fractions of shales compounds, porous space, kerogen, pyrite and mineral matrix, and pores connectivity.
This work was supported by Ministry of Education and Science of the Russian Federation under the contract RFMEFI58114X0008. This work has been carried out using computing resources of the federal collective usage center Complex for Simulation and Data Processing for Mega-science Facilities at NRC “Kurchatov Institute”.
A.A. Mikhutkin1, E.B. Pichkur1, M.Yu. Spasennykh2, N.N. Bogdanovich2, G.A. Kalmikov3, A.L. Vasiliev1,4
1 National Research Centre “Kurchatov Institute”, Moscow, Russia
2 Skolkovo Institute of Science and Technology, Moscow, Russia
3 Moscow State University, Moscow, Russia
4 Shubnikov Institute of Crystallography, Russian Academy of Sciences, Moscow, Russia
Alexander L. Vasiliev
Head of Electron Microscopy laboratory
NRC “Kurchatov Institute”
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