Impact of mineralisation on digital coal properties - ACS Publications

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Impact of mineralisation on digital coal properties Yu Jing, Ryan Troy Armstrong, and Peyman Mostaghimi Energy Fuels, Just Accepted Manuscript • DOI: 10.1021/acs.energyfuels.7b01401 • Publication Date (Web): 27 Aug 2017 Downloaded from http://pubs.acs.org on August 29, 2017

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1

Energy & Fuels

Impact of mineralisation on digital coal properties

2

Yu Jing, Ryan T. Armstrong and Peyman Mostaghimi

3

School of Petroleum Engineering, The University of New South Wales, NSW 2052, Sydney,

4

Australia

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Abstract

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Coal seam gas (CSG) is an unconventional energy resource, whose production is mainly

7

controlled by the underlying fracture networks, known as “cleats”. The natural cleats are

8

generally mineralised during diagenesis, which significantly reduces fracture conductivity

9

because of the more tortuous flow pathways and smaller cross-sectional area perpendicular to the

10

flow. This paper aims at characterising the mineral fillings of the coal cleat network by utilising

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X-ray micro-computed tomography (micro-CT) imaging to investigate the effect of minerals on

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the petrophysical properties of coal. We apply a high-resolution micro-CT to obtain a 3D digital

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representation of a mineralised coal sample collected from Moura mine of Bowen Basin. The

14

main components of the coal sample, including coal matrix, cleats and minerals, are individually

15

analysed to give the statistics of cleat orientation, length, and thickness using quantitative image

16

analysis. According to the measured statistical data, digital coal models with mineralisation are

17

stochastically constructed and then used for simulation to obtain petrophysical properties. Our

18

results show that minerals in cleats can significantly reduce the conductivity of the cleat network 1

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by reducing the permeability by up to 75% and increasing the tortuosity by 21%. In addition, by

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studying the deformed sample under external stresses, we find that porosity reduction due to

21

compression of the cleat network with minerals is significantly less than models without

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minerals. In addition, mineralisation has a detrimental effect on the matrix-fracture contact area,

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which is reduced by 17% with the addition of minerals. Lastly, by comparing digital coal models

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to the original micro-CT images, the mineralised digital coal models are found to be more

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representative in terms of permeability estimation, with an error of only 4.4%.

26

Keywords: Coal seam gas, mineralisation, digital coal model, coal permeability, pore-scale

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modelling

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1 Introduction

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Coal, as a complex and heterogeneous rock, is composed of three physical components, coal

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matrix, fractures and minerals 1. The matrix, also known as “maceral”, is fundamental for

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combustion and methane adsorption 2. Coal is formed from the accumulation of plant materials

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under high temperature and high pressure during long-time periods. Therefore, coal matrix is

33

divided into multiple lithotypes that originate from different plant materials

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appear to be banded and composed of alternating bright and dull materials 5, 6. The bright band is

35

rich in vitrain, which is a brittle material with bright lustre. It is permeated with fine cracks at

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right angles, giving a blocky appearance. In contrast, the dull band, comprised of durain, is a

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grey to black material with a dull lustre.

3, 4

. Most coals

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Coal fractures, also known as “cleats”, are the dominant flow pathways that determine the

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permeability and production rate of coal seam gas (CSG) 7, 8. Cleats in different lithotypes tend to 2


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have different properties. For example, cleats in bright bands, named “bright cleats”, occur in

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two orthogonal sets of sub-parallel cleats known as face and butt cleats. Face cleats form first

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during coalification and are easier to visualise because they extend longer than butt cleats 9. Butt

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cleats occur later due to the relaxation of the original stress field and terminate at face cleats 10, 11,

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such that the connectivity pattern of an organised bright cleat system is mostly present as “T-

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junctions” between face and butt cleats 12. In contrast, cleats of dull bands lack a regular pattern,

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where face and butt cleats are hardly recognised 5, 13. Dull cleats are poorly developed and rarely

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observed in coal samples 5, 14.

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Minerals are the inorganic material formed during peat accumulation as well as changes to the

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subsurface fluids 15, 16. Based on the time of formation, coal minerals are classified as syngeneic

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or epigenetic, where syngeneic minerals formed during peat formation while the epigenetic form

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after coal has matured 17. Epigenetic minerals are commonly deposited in cleats and are mainly

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composed of kaolinite, illite, pyrite and calcite

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matrix or cleats

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morphology

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similar to the coal cleats network. For example, “T-junction” connections can be observed in the

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mineral phase, which resembles the connectivity pattern of face and butt cleats

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fillings are also oriented perpendicular to the bedding plane. Thus, mineral fillings can also be

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described by geometrical properties, such as orientation, length and mineral size. With the help

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of X-ray micro-computed tomography (micro-CT), geometrical properties of minerals can be

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statistically analysed by applying quantitative image analysis 24-30.

22, 23

20

15, 18-21

. Minerals can occur within either coal

. Mineral fillings distributed in cleats tend to have a well-developed

. According to Zhang et al.

22

, the morphology of mineral fillings in cleats is

22

. Mineral

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The occurrence of minerals in cleats during diagenesis can significantly affect the conductivity

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of coal 31. Because minerals that are lined or filled in the void cleat space 18, 19 tend to block gas 3


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flow pathways, thus the hydraulic fracture aperture is lowered and fluid pathways are more

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tortuous. As a result, the cleat network with mineralisation is less interconnected or even non-

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conductive depending on the degree of mineralisation

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to be able to prevent coal fractures from completely closing under external stress 34. Furthermore,

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minerals can decrease the gas sorption capacity of the coal matrix by reducing the pore volume

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and internal surface area

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reacts with organic matrix is reduced

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fundamental requirement for enhanced CSG recovery because the mineralisation of coal is

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closely associated with its permeability and methane sorption capacity.

2, 20

32, 33

. However, mineral fillings are found

. Because of the presence of minerals, the contact area where gas 23

. Therefore, the study of coal minerals is a primary and

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In this paper, a mineralised coal sample collected from Moura Mine of Bowen Basin is

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studied. We apply micro-CT technology to obtain 3D digital images, based on which the coal

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matrix, cleat network and mineral matter are individually analysed to obtain statistical data.

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According to measured statistical data, an advanced digital coal model is constructed, which

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preserves the banding information of the coal matrix, geometrical properties of cleats as well as

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the mineralisation. To investigate the effect of mineralisation on digital coal properties, the

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petrophysical properties of our digital coal models are calculated and compared to the original

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micro-CT data. We find that the characterisation of mineralisation is crucial for digital coal

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modelling since minerals have a significant impact on cleat conductivity and porosity.

4


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2 Methodology

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2.1 Micro-CT Scanning and Image Processing

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The sample studied in this work is from Moura mine of Bowen Basin, which is a medium

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volatile bituminous coal with a vitrinite reflectance of 1.15% 35. A high-resolution, helical micro-

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CT scanner developed at the Australian National University 36 is applied to scan the coal sample

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to obtain its three-dimensional internal structure. The 3D micro-CT data are represented by an

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array of voxels, where each voxel value corresponds to the X-ray attenuation coefficient of a

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given phase

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the investigated object

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grey-scale values in the micro-CT data, which are also called “CT numbers”. Therefore, the main

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coal components, including matrix, cleats and mineral matter, can be identified by the particular

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range of CT numbers

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by applying image segmentation methods 1. For example (Figure 1), in the grey image, cleats are

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shown in dark black, the mineral matter is highlighted with high CT numbers (bright colours),

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and the coal matrix is identified by medium CT numbers (light grey). For details on the image

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segmentation process, please refer to Ramandi et al.

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grey-scale images are partitioned into distinct phases, where each component can be extracted

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for further analyses 42, 43.

37-40

. As the voxel values are a function of density and effective atomic number of 41

, different coal components with distinct densities will have different

24, 25, 41

, such that the grey-scale image can be partitioned to unique phases

35

. After the segmentation, the continuous

5


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Figure 1: In the grey image (a), cleats are shown in dark black colour, the mineral matter are

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highlighted with high CT numbers, and the coal matrix is identified by medium CT numbers. In

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the segmented image (b), the continuous grey-scale image is segmented into distinct phases:

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cleats (green), minerals (red), bright bands (grey) and dull bands (black).

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The 3D digital micro-CT images studied in this work have a dimension of 500×500×500

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voxels with the resolution of 16.5 µm (Figure 2a). As can be seen, bright and dull bands are

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alternatively layered in this sample (Figure 2a). Two bright bands can be observed in this

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domain, both of which have well-developed cleat networks (Figure 2b). Minerals (Figure 2c) are

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widely distributed in both bright and dull bands, while minerals in bright bands have more

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regular morphology as cleats.

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Figure 2: (a) The 3D micro-CT image has the dimension of 500×500×500 voxel with a

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resolution of 16.5 µm; (b) Two bright bands are observed within the sample, both of which are

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well-fractured; (c) Mineral matter occurs in both bright and dull bands, while minerals in bright

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bands have more regular morphology as cleats.

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2.2 Statistics Acquisition

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Based on the segmented micro-CT images, all three components of coal are extracted for

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quantitative analysis individually. For the matrix phase, a binary lithotype profile consisting of

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bright and dull bands is obtained by applying the thresholding method

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are measured based on the lithotype profile: (1) volume fraction of bright bands, (2) band

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thickness and (3) band orientation. The volume fraction of bright bands is calculated by dividing

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the number of the bright band voxels by the total number of voxels (500×500×500). To measure

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the band thickness and orientation, an edge detector with Sobel approximation 44 is used to detect

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the band boundaries, where points with maximum intensity gradient are highlighted as edges.

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Then the highlighted boundaries are used for band thickness and orientation measurements,

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where the band thickness is determined by measuring the spacing of two edges of a band . Since

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dull cleats are poorly developed, only bright cleats are extracted for analyses by overlapping the

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lithotype profile onto the cleat network phase. Geometrical properties, including orientation,

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length, spacing and aperture size are measured by an automatic quantitative image analysis

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method 29. The aperture size is defined as the opening width of a fracture. Prior to measurements,

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a cleat-grouping algorithm

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independent geometrical statistics since face and butt cleats have different properties

29

1, 24-26

. Three parameters

is utilised to partition face cleats and butt cleats, providing two 45

.

7


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Measurements of geometric properties and the cleat grouping process based on micro-CT images

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are explained in detail by Jing et al. 29.

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Minerals occurring along the cleat surfaces are observed to have well-developed morphologies

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that are similar to the cleat network 23. Similar with cleats, the mineral fillings are also oriented

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perpendicular to the bedding plane and connected with “T-junctions”, which resemble the

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morphology of face and butt cleats 22. This type of pattern indicates that minerals are deposited

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after formation of the cleats 27. Frequency, volume fraction of minerals and length of each bright

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band are measured separately. Frequency is the number of mineral fillings that are observed in

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cleats. The volume fraction of minerals represents the percentage of mineral volume to the bright

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band volume, where the mineral volume can be determined by the number of mineral-identified

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voxels. Length refers to the extent of a mineral along the cleat surface. Herein, length is

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measured based on the 2D slices of micro-CT images. Minerals on the micro-CT images are first

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skeletonised with a thinning algorithm

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Therefore, the length of a mineral can be calculated by counting the number of voxels that the

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mineral has.

147

46

, reducing the widths of minerals to be one-voxel.

As geometric properties of cleats, such as spacing and aperture, are functions of band thickness

148

45, 47-49

149

complete set of statistical data is obtained (Figure 3). Each bright band is divided into cleats and

150

minerals, where cleats are further grouped into face cleats and butt cleats.

, individual bright bands with unique thicknesses are analysed separately. As a result, a

8


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Figure 3: The structure of mineral statistics data. Different bright bands of various band

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thickness values are extracted for further analysing. Under individual bright bands, minerals

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filling within different cleat groups are distinguished and statistically analysed separately.

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2.3 Mineralised Digital Coal Model

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A mineralised digital coal model (Figure 4c) is constructed numerically to characterise the

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three main components of coal, including band information (Figure 4a), cleat geometrical

158

properties (Figure 4b), and mineral fillings (Figure 4c). The construction consists of three steps:

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(1) generating layered bright and dull bands; (2) constructing DFN models in bright bands; and

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(3) distributing minerals in the constructed DFN models.

a

b

c

2 mm

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Figure 4: The mineralised digital coal model (c) contains band information (a) and cleat

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geometrical properties (b). The colours indicate different phases of coal: bright bands (black),

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dull bands (grey), cleats (green), minerals (red).

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Based on the banding statistics of the coal matrix, a banded model is constructed where bright

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and dull bands with stochastic band thicknesses are alternatively layered. Boundaries of each

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band are generated according to the thickness and orientation distribution of bands. A region-

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filling algorithm

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procedure is repeated until the volume fraction of bright bands is equal to that of the original

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micro-CT data with an error less than 5%. Furthermore, the bright cleat network is merged with

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the banded model by generating discrete fracture network (DFN) models within the bright bands.

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The DFN model is comprised of discrete cleat planes, whose geometrical properties follow

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statistical data

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band, DFN models are stochastically constructed within corresponding bright bands. As coal

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cleats of bright bands have a particular network pattern, an improved DFN modelling method is

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developed by Jing et al.

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connectivity as the cleat connections of the original sample. Dull bands are assumed to be

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impermeable in this work, so there are no cleat networks constructed in dull bands. This is

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aligned with the observations of Ramandi et al.

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dull bands is 3-4 orders of magnitude smaller than that of bright bands for the same coal sample.

50, 51

52-54

is then applied to fill the volume within boundaries. The modelling

. In this work, according to the cleat statistics of the corresponding bright

29

where face cleats and orthogonal butt cleats have “T-junction”

35

where they showed that the permeability of

181

Based on the cleat network, minerals are randomly generated along bright cleat surfaces.

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According to the frequency statistics, a certain number of mineral fillings are initially generated,

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where one mineral filling is made up of mineral voxels. The resulting volume fraction of

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generated minerals is calculated and compared with that of original micro-CT data. To insure 10


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that the total volume of generated minerals is identical to that of the original micro-CT data, a

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volume adjusting process is developed to vary the size of the mineral volume by changing

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adjacent voxels to be solid (mineral phase) or void (cleat space). After each volume adjusting

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process, the resulting volume fraction of minerals is calculated and compared to the fraction

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value measured from the micro-CT images. The aforementioned procedure is stopped when we

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obtain a mineralised cleat network with the desired degree of mineralisation in terms of mineral

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frequency and mineral volume fraction. The workflow of the mineralisation process is illustrated

192

in Figure 5.

193 194

Figure 5: Flowchart of mineralisation process. Based on the cleat network, certain pieces of

195

minerals are generated. A volume adjusting process of varying the mineral size is applied until

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the total volume of generated minerals is identical to that of original micro-CT images. Lastly,

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mineralised cleat network with desired mineralisation degree is obtained.

11


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2.4 Evaluation of Mineralised Digital Coal Models

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2.4.1 Porosity and permeability

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Porosity (φ) based on a segmented binary image is determined by dividing the number of cleat-

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identified voxels by the total number of voxels. Permeability (k) is simulated by a Navier-Stokes

202

solver in GeoDict software

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directional permeability, a pressure difference is applied in the flow direction of the domain and

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fluid flow is simulated through the sample. The boundary condition in the flow direction is set to

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be periodic with a constant pressure difference, while no-flow boundary is defined in the

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tangential direction. Additional void regions of 10-voxels are added at both inflow and outflow

207

boundaries to avoid the possibility of dead-end flow channels under periodic conditions. No-slip

208

boundary conditions are applied at other boundaries. The simulation will stop if the change of

209

permeability is less than 5% between successive iterations. Both matrix and minerals are

210

considered impermeable in the flow simulation.

211

2.4.2 Deformation

55, 56

, which uses the finite volume method

57-59

. To determine the

212

The cleat network alters when the structure is deformed under external stresses 60. In order to

213

study the impact that minerals can have on this alteration, the sample is subject to external loads

214

and simulated for permeability after structure deformation. We use the Elastodict solver of the

215

GeoDict software

216

composition, e.g. coal matrix and minerals, is assigned with its corresponding mechanical

217

parameters, such as Young’s modulus (E) and Poisson ratio (ν). Herein, the minerals are

218

assumed to be calcite, which is a common mineral type for coal cleats

219

parameters of calcite are set to be E =84 Gpa and ν = 0.32 63. For the coal matrix, we define the

61

to solve the elasticity equations

62

. For the mineralised digital model, each

18, 19

. The mechanical

12


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220

mechanical properties according to the work of Aziz et al.

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load type of compression is applied on the model boundaries with specified strain (in %). Based

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on the input information, including mechanical properties of each constituent and applied

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boundary conditions, stresses and corresponding strains at each solid voxel are computed based

224

on Hooke’s Law 65,

225

, where E =4 Gpa and ν = 0.3. A

= ∑ , ɛ , , {1, 2, 3}

(1)

226

where is the stress tensor, ε is strain tensor and C is symmetric elasticity or stiffness tensor,

227

which is given by, 2μ + λ

λ

λ λ

0 0 0 μ 0 0

0 0 0 0 μ 0

0 0! 0 0 0 μ

228

C=

229

where λ and μ are Lamé and shear moduli, which depend on mechanical properties of specific

230

λ λ

0 0 0

λ

0 0 0

2μ + λ 0 0 0

(2)

material, including Young’s moduli (E), Poisson ratio (ν) and bulk moduli (B): %( λ'(%)

231

E=

232

ν = ((λ'%)

233

B=

234

2μ + λ

(3)

λ'% λ

(4)

λ'(%

(5)

2.4.3 Specific surface area

235

Specific surface area (SSA) is defined as the total surface area per bulk volume. Because of the

236

presence of minerals along cleat surfaces, the contact between gas and the organic matrix is

237

changed

238

digital images, boundary voxels where there is a change in the voxel values are highlighted

23

, which in turn has been shown to influence reaction rates and fluid transport

66

. In 67

.

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For each boundary voxel, we identify surfaces that are exposed to the interface. Thus, SSA is

240

determined by summing the surface area of voxels that are on the solid-void boundaries 30.

241

2.4.4 Tortuosity

242

Tortuosity (τ) is a parameter that characterises the sinuosity and interconnectedness of porous 68, 69

243

media

. Herein, geometrical tortuosity is calculated, which is defined as the ratio of the

244

shortest length of the path connecting two points in the pore space to the straight-line distance.

245

So, it is a structural character of the medium, independent of any particular transport process. In

246

binary image, tortuosity is determined by the geodesic distance (*+ ) of two void voxels divided

247

by the Euclidean distance (*, ) between them 70, ./

248

τ=

249

Herein, the geodesic distance is determined by the city block distance that examines the

(3)

.0

250

absolute differences between the coordinates of two objects

251

voxels A (1 , 2 , 3 ) and B (1( , 2( , 3( ) in three dimensions is,

71

. For example, the distance of

252

*+ = |1 − 1( | + |2 − 2( | + |3 − 3( |

253

However, for 3D digital data, the geodesic and Euclidean distances between two opposite

254

surfaces rather than two void voxels are measured. The geodesic distance of every voxel in the

255

cleat space to the sample surfaces is computed

256

there are a variety of geodesic distance values where the average geodesic distance is obtained.

257

Next, the average geodesic distance values are plotted as a function of corresponding Euclidean

258

distances, which provides a linear correlation. The slope of the fitted linear relationship is the

259

tortuosity.

(4)

72

. As a result, for a certain Euclidean distance,

14


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3 Results and Discussion

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3.1 Statistics

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Table 1 summarises the measured distributions of the three components: coal matrix, cleat

263

network and mineral matter. The two bright bands (Bright Band #1 and #2) are oriented in

264

parallel and approximately 1.62mm and 3.67mm in thickness, respectively. Also, the volume

265

fraction of bright bands is 61.5%. For each bright band, cleats and minerals are individually

266

extracted for the analysis. According to the statistics, the face and butt cleats are almost equally

267

spaced, while spacing values vary within different bright bands. For example, Bright Band #1 is

268

fractured more coarsely than Bright Band #2 in terms of the development of face cleats. Because

269

of the limited size of the sample, the thickness values are measured from parts of bright bands in

270

the studied domain rather than the entire sample. Therefore, the thickness cannot represent the

271

real thickness of the bright bands. However, according to the findings of our previous work 45, 47-

272

49

273

be indicated that the whole Bright Band #1 is thicker than Bright Band #2. Furthermore, the face

274

cleats in Band #1 have an average aperture size of 0.17mm, slightly larger than those in Band #2

275

(0.16mm). Based on the linear positive correlation between aperture size and band thickness

276

provided by Close and Mavor et al. 47, Bright Band #1 is also inferred to be thicker than Bright

277

Band #2. The orientation distributions of the bands and cleat network indicate that face and butt

278

cleats are mutually orthogonal and both normal to the bedding plane. For mineralisation, Bright

279

Band #1 is more extensively mineralised compared with Bright Band #2. Bright Band #1 has

280

longer minerals fillings (the average length is 5.15mm) than Bright Band #2 (3.92mm). The

281

volume fraction of minerals of Band #1 is 3 times greater than that of Band #2. In addition, 15

, average spacing of cleats is linearly proportional to the coal band thickness. Therefore, it can


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282

mineralisation is not the same for different cleat families: face cleats are filled with minerals,

283

which indicates that the minerals deposit after the creation of face cleats; but there are no

284

minerals observed in butt cleats in the studied sample, since they may be formed after water has

285

been driven from the coal, such that they are less likely to be mineral coated

286

different bright bands and cleat families tend to have a different degree of mineralisation, leading

287

to the importance of the proposed coal banding characterisation and cleat family grouping

288

process.

289

Table 1: Measured statistical data, including banding, cleat network and mineralisation, of a coal

290

sample from Moura Mine of Bowen Basin.

73-75

. Therefore,

Bright Band #1 Bright Band Thickness Ave. 1.65

Azimuth Dev. 0.08

Ave. 5.08

Dip Dev. 3.98

Ave. 92.75

Dev. 3.74

Aperture Ave. Dev. 0.17 0.03 0.04 0.02

Length Ave. Dev. 3.72 2.65 1.83 1.21

Cleat Network

Face cleats Butt cleats

Azimuth Ave. Dev. -2.41 4.61 92.26 4.78

Dip Ave. Dev. 94.41 4.59 91.23 4.67

Spacing Ave. Dev. 2.72 0.37 2.19 0.43

Mineral Volume Fraction 0.0160

Frequency Face cleats Butt cleats 3 0

Length Ave. 5.15

Dev. 3.71

Bright Band #2 Bright Band Thickness Ave. 3.67

Azimuth Dev. 0.63

Ave. 4.89

Dip Dev. 3.82

Ave. 91.45

Dev. 3.46

Aperture Ave. Dev. 0.16 0.03

Length Ave. Dev. 3.22 1.66

Cleat Network

Face cleats

Azimuth Ave. Dev. -2.52 4.54

Dip Ave. Dev. 95.42 4.65

Spacing Ave. Dev. 2.17 0.39

16


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Energy & Fuels

Butt cleats

91.44

4.74

90.71

4.79

2.30

0.52

0.04

0.02

1.04

0.46

Mineral Volume Fraction 0.0056

291

Frequency Face cleats Butt cleats 2 0

Length Ave. 3.92

Dev. 2.85

3.2 Study of mineral effects on digital coal models

292

The mineralised digital coal models are constructed with identical banding information, cleat

293

geometrical properties and mineral distributions as the original miro-CT images (Figure 6). It is

294

shown that the digital coal model preserves the main features of the original micro-CT images:

295

(1) the model comprises alternating bright and dull bands, where the volume fraction of bright

296

bands is 61.5%; (2) bright bands have well-developed cleat networks with “T-junction”

297

connectivity, and are constrained by adjacent dull bands; and (3) there are 5 mineral fillings with

298

the total mineral volume fraction of 0.8% within the bright cleats, which is identical to the

299

mineralisation of original micro-CT images. A total of 15 pairs of digital coal realisations are

300

stochastically constructed with the Monte Carlo method

301

one mineralised digital coal model and one without minerals. Next, porosity, permeability,

302

specific surface area and tortuosity are computed for each realisation.

76

. Each pair of realisations consists of

17


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303 304

Figure 6: An example digital coal model with mineralisation (b) with a clip in y-direction. It

305

preserves the main features of original micro-CT images (a).

306

3.2.1 Petrophysical properties

307

In total 15 model pairs, i.e. with and without minerals are stochastically generated. Each model

308

pair has an identical cleat network. We then obtain the porosity and simulate flow through each

309

model pair to measure permeability. The average petrophysical properties of all model pairs are

310

plotted as a function of the number of models that are used to give the average values (Figure 7).

311

It can be seen that the average porosity and permeability of digital models are significantly

312

reduced by mineralisation. For example, the average porosity of digital models without minerals

313

reaches a plateau of 4.1%, which decreases to 3.2% after mineralisation. The effect of

314

mineralisation on permeability is more dramatic, where average permeability is reduced by

315

approximately 75%. Figure 8 provides insight into how minerals influence the flow field by

316

presenting the streamlines for a model with and without minerals. For the mineralised digital

18


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317

models, the mineral fillings block the pathways of fluid (Figure 8c), resulting in lower

318

transmissibility.

319

Additionally, the mineralised digital coal models provide permeability values that are closer to

320

the original micro-CT images than digital coal models without minerals. Specifically, the

321

average permeability of digital coal models without mineralisation stabilises at 3.96D with an

322

error of 16.8% compared with the non-mineralised original micro-CT images (4.76D).

323

Conversely, mineralised digital coal models have an average permeability of 0.94D, which is

324

near the permeability of the mineralised original micro-CT images (0.90D), with an error of only

325

4.4%.

326 327

Figure 7: The average porosity (a) and permeability (b) as a function of the number of models

328

that are used to give the average values. It can be seen that both the porosity and permeability are

329

significantly reduced by mineralisation.

19


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330 331

Figure 8: Illustration of the streamlines change with the effects of mineralisation. (a) a digital

332

coal model with mineralisation; (b) and (c) shows the streamlines of models without and with

333

minerals in 3D.

334

3.2.2 Mechanical study of mineralised models

335

Since the permeability of fractured media is stress-dependent

60

, we compress the cleat

336

network of the digital coal model to study the sensitivity of permeability to external stresses. In

337

order to reduce computational time, a subsample (169×244×273) that has a well-developed cleat

338

network is cropped and investigated for an external loading test. In the subsample (Figure 9a),

339

there are two face cleats, one of which is filled with minerals, while butt cleats are not

340

mineralised. We numerically compress the subsample with a stress of 10Mpa in the X-direction

341

(normal to face cleats), while there is no pore pressure for this model. The resulting strain

342

distribution within the domain (Figure 9b) shows that the coal matrix near mineralised face cleat

343

has less strain than that of the non-mineralised face cleat. Thus, mineral fillings can mitigate the

344

geometrical deformation induced by confining stress.

345

The subsample is compressed in X- and Z-directions with external stresses ranging from 1

346

MPa to 10 MPa and the porosity of the resulting deformed models are measured (Figure 10). It is 20


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Energy & Fuels

347

shown that both models with and without minerals become less porous under compression. The

348

porosity of non-mineralised subsamples presents a sharper decrease under increasing loads,

349

while porosity reduction of mineralised subsample is less significant. When applied stress is

350

greater than 5 MPa, models with mineral have greater porosity than non-mineralised models that

351

have the same cleat network. Figure 11 compares the resulting deformed geometries when the

352

subsample (Figure 11a) is subject to a confining stress of 4MPa in X- and Z-directions with and

353

without mineralisation. In Figure 11b, the non-mineralised face cleat disappears completely

354

under the compression, while the face cleat with minerals remains open. On the other hand, in

355

Figure 11c, the same face cleat is closed when there are no minerals. Therefore, mineral fillings

356

can aid in maintaining cleat aperture sizes while under confining stresses

357

less compressible than the coal matrix 22.

77

, since minerals are

358 359

Figure 9: (a) A subsample has well-developed cleat network, where minerals (in red) are

360

observed in one of the face cleats; (b) strain distribution under specified stress in the x direction

361

(as indicated by the arrows), where the colour of mineral (white) is not indication of strain.

21


Energy & Fuels

0.06 Models without minerals

0.05

Porosity

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

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Models with minerals

0.04 0.03 0.02 0.01 0 1

362

3

5 7 Effective Stress, Mpa

9

363

Figure 10: The porosity variation of models with and without minerals as a function of external

364

stresses applied on surfaces. When applied stress is higher than 5 MPa, models with mineral

365

fillings inversely have greater porosity than non-mineralised one that has the identical cleat

366

structure.

367 368 369 370

Figure 11: The subsample (a) is compressed under the identical stresses (4Mpa), where the compression directions are indicated by the arrows. Resulting deformed geometries (b) and (c) are for mineralised and non-mineralised model, respectively. 22


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371

Energy & Fuels

3.2.3 Geometrical properties

372

Since the minerals in cleats tend to block fluid flow paths, the flow channels are more tortuous

373

compared with those in models without mineralisation. Figure 12 (a) plots the ratios of geodesic

374

Euclidean distances for one pair of digital models. The geodesic and Euclidean distances are

375

measured based on the surface at Z = 0 and another arbitrary parallel surface in the Z-direction. It

376

is shown that for models without minerals, the ratio values (in squares) are around 1, indicating

377

less tortuous flow channels. However, data points of the mineralised model (in circles) lie above

378

the data for models without minerals, which means that the fluid in the mineralised model travel

379

a longer pathway from inlet to outlet. Therefore, mineralisation can increase tortuosity by 21%

380

approximately. Tortuosity results of all generated models are shown in Figure 12b. Models

381

without minerals have an average tortuosity of 1.07. Conversely, after the inclusion of minerals,

382

the average tortuosity rises to 1.30 with larger variation. Compared with mineralised original

383

micro-CT images (τ = 1.32), the tortuosity estimated from mineralised models is more accurate

384

with an error of 1.5%.

385

Another geometrical property of the fluid flow channel that is influenced by mineralisation is

386

specific surface area (SSA). We find that the SSA of digital models is reduced from 30.27 cm-1

387

to 25.09cm-1 due to mineralisation. Therefore, mineralisation could have a detrimental influence

388

on the reaction rates by decreasing the contact area between adsorbed methane and coal matrix.

389

However, measured SSA of digital models is lower than that of the original micro-CT images

390

(66.00 cm-1 and 69.17 cm-1 for models with and without minerals, respectively), which is likely

391

induced by segmentation error where some regions are mislabelled as cleats. As a result, the

392

segmented micro-CT data will provide void space and SSA values that are greater than reality

393

(Figure 13b). Besides, isolated short cleats and dead-end pores of the original micro-CT images 23


Energy & Fuels

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30

Page 24 of 31

394

also result in larger internal surface area

. Since digital models are excluded of segmentation-

395

induced pores and isolated short cleats, so the estimated SSA values based on digital models are

396

more close to the area of the contact surface where fluids (e.g. methane and water) react with the

397

organic coal matrix.

398 399

Figure 12: Comparison of tortuosity between models with and without minerals. (a) the ratios of

400

geodesic distance and Euclidean distance for one pair of digital model; (b) tortuosity values of all

401

digital models and mineralised original micro-CT images.

402 24


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403

Figure 13: (a) is one slice of original grey images, where noises (in circle) have similar grey

404

value with cleats. As a result, the noises are mistakenly segmented into cleats in segmented

405

image (b).

406

4 Conclusions

407

This work develops a mineralised digital coal model that is able to characterise the core-scale

408

components of coal, including coal matrix, cleats and minerals. The developed digital model

409

comprises of alternating bright and dull bands such that the banding morphology of different

410

lithotypes are preserved. Then, a novel discrete fracture network model is integrated with the

411

banded model where geometrical properties and the particular connectivity pattern of the cleat

412

network are characterised, resulting in a digital coal model without mineralisation. Based on

413

micro-CT images of a coal sample from Moura mine of Bowen Basin, the mineralisation is

414

characterised regarding the mineral frequency, the volume fraction of mineral fillings and the

415

mineral length. Stochastic minerals are further generated within the digital coal model, with the

416

identical degree of mineralisation as the original micro-CT images. By computing the

417

petrophysical and morphological properties of the digital coal models, it is found that

418

mineralisation can significantly influence coal petrophysical properties. For instance, mineral

419

fillings act as barriers to fluid migration, resulting in less permeable coal and more tortuous flow

420

paths. The contact area between organic coal matrix and gas is reduced by 17% due to

421

mineralization, which could influence reaction rates. In addition, the less compressible minerals

422

can aid in keeping cleats open and thus reduce coal matrix deformation under confining pressure.

423

Therefore, mineralisation is crucial in the characterisation of coal since it not only retards cleat 25


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424

network transmissibility but also supports coal cleat apertures sizes under external stresses.

425

Furthermore, the developed digital coal models that characterise mineralisation, are found to be

426

more representative in terms of permeability estimation. So the construction of mineralised

427

digital coal models can lead to more accurate evaluation of coal samples and thus provide more

428

reliable parameters for the prediction of coal seam gas production rates.

429

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