Study on fracture characteristics in coal and shale for coal-measure gas reservoir based on 3D CT reconstruction and fractal features

doi:10.1007/s11707-022-1027-9

Front. Earth Sci.

2023, Vol. 17

Issue (2) : 514-526 https://doi.org/10.1007/s11707-022-1027-9

RESEARCH ARTICLE

Study on fracture characteristics in coal and shale for coal-measure gas reservoir based on 3D CT reconstruction and fractal features

Huijun WANG¹, Shangbin CHEN^1,²(

), Shaojie ZHANG^1,², Chengxing ZHANG³, Yang WANG^1,², Gaofeng YI¹, Yixuan PENG¹

¹. School of Resources and Geoscience, China University of Mining and Technology, Xuzhou 221116, China
². Key Laboratory of Coalbed Methane Resources and Reservoir Formation Process (the Ministry of Education), China University of Mining and Technology, Xuzhou 221116, China
³. School of Earth Sciences and Engineering, Sun Yat-sen University, Guangzhou 510275, China

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Abstract

Pores and fractures are important components of flow channels in coal-measure gas reservoirs. While considerable studies have been conducted on pore structure evolution, very few studies have investigated the fracture distribution and self-similarity characteristics. To reveal the characteristics of fracture distribution in coal and shale reservoirs, computed tomography studies were performed on 15 coal and shale samples from the Shanxi and Taiyuan formations. The results show that the fracture distribution of samples of the same lithology differs significantly, and the fracture distribution heterogeneity of shale samples is much higher than that of coal samples. In shale, the heterogeneity of fracture distribution is mainly caused by pores and fractures smaller than 2 μm in the z-direction, with relatively little contributions from pores and fractures in the x and y directions. However, the heterogeneity of fracture distribution in coal is mainly controlled by pores and fractures larger than 2 μm in all directions, and the difference between the three directions is minor. It was shown that a great number of microscopic pores and fractures contribute to the highest fractions of porosity in different lithological samples. This method is useful for determining the fracture distribution characteristics in shale and coal-measure gas reservoir.

Keywords pore-fracture system fracture distribution directionality heterogeneity CT experiment coal-measure gas reservoirs

Corresponding Author(s): Shangbin CHEN

Online First Date: 12 June 2023 Issue Date: 04 August 2023

Cite this article:

Huijun WANG,Shangbin CHEN,Shaojie ZHANG, et al. Study on fracture characteristics in coal and shale for coal-measure gas reservoir based on 3D CT reconstruction and fractal features[J]. Front. Earth Sci., 2023, 17(2): 514-526.

URL:

https://academic.hep.com.cn/fesci/EN/10.1007/s11707-022-1027-9
https://academic.hep.com.cn/fesci/EN/Y2023/V17/I2/514

Fig.1 Structural outline diagram (a) and sampling formation (b). (a) Formation geological period of each structural trace; (b) sampling locations of coal and shale samples used in this study.

Tab.1 Element composition and type identification of kerogen in shale samples

Tab.2 Voxel resolution and model volume information table

Fig.2 Schematic diagram of the sample and data processing flow. After the sample has undergone (a) fracture segmentation, (b) 3D model construction, (c) fracture parameter extraction and (d) binary data extraction, the obtained data are input into the CTSTA script for calculation, and the output file contains the pores and cracks required for our analysis and their fractal dimension information.

Fig.3 Schematic diagram of box-counting dimension division. The extracted ROI is divided into cubes with different side lengths for calculation.

Fig.4 Schematic diagram of pore and fracture visualization process and result display. The CT processing process of the sample includes (a) selecting the ROI; (b) extracting mineral components; (c) extracting pores and cracks; and (d) rendering pores and cracks.

Fig.5 Porosity contribution and porosity accumulation curve in the x, y, z directions.

Fig.6 Porosity component and its cumulative pore radius component.

Y-1		Y-2		Y-3		Y-4		Y-5
Fractal dimension		Fractal dimension		Fractal dimension		Fractal dimension		Fractal dimension
2.20268		2.44193		2.13249		2.27559		2.52048
log(1/ε)	logN(ε)	log(1/ε)	logN(ε)	log(1/ε)	logN(ε)	log(1/ε)	logN(ε)	log(1/ε)	logN(ε)
−1.9758	4.23411	−0.49584	2.70805	−0.75139	2.63906	−0.86436	1.38629	−2.59803	5.45104
−2.1881	4.85981	−0.54759	2.83321	−1.09676	3.55535	−1.52413	2.83321	−2.82802	6.07074
−2.2817	5.0689	−0.7749	3.43399	−1.35	4.06044	−1.80838	3.49651	−3.02537	6.6107
−2.436	5.41165	−0.9847	3.91202	−1.5334	4.36945	−2.12369	4.11087	−3.16202	6.97354
−2.6986	5.96871	−1.03229	4.00733	−1.538	4.38203	−2.34382	4.67283	−3.46478	7.75662
−2.8196	6.21461			−1.6052	4.58497	−2.66528	5.45104	−3.70908	8.37931
−2.8243	6.22851			−1.80205	4.96981	−2.86699	5.93754	−3.93529	8.93879
−3.019	6.55962			−1.80236	4.97673	−2.98796	6.2106	−4.08995	9.31344
				−1.9664	5.26269	−3.03427	6.27099	−4.37803	9.87473

Y-6		Y-7		Y-8		Y-9		Y-10
Fractal dimension		Fractal dimension		Fractal dimension		Fractal dimension		Fractal dimension
2.01242		2.74387		2.43728		2.45474		2.60968
log(1/ε)	logN(ε)	log(1/ε)	logN(ε)	log(1/ε)	logN(ε)	log(1/ε)	logN(ε)	log(1/ε)	logN(ε)
−1.85925	4.17439	−0.99414	2.99573	−0.34269	2.19722	−0.9459	2.07944	−4.56691	7.99092
−2.11702	4.81218	−1.28118	3.71357	−0.66645	2.94444	−1.41993	3.17805	−4.66386	8.28853
−2.30659	5.21494	−1.45333	4.15888	−0.81245	3.3322	−1.56089	3.7612	−4.78789	8.64611
−2.42509	5.52943	−1.5206	4.34381	−0.94892	3.61092	−1.73194	4.2485	−4.87829	8.90788
−2.59682	5.95064	−1.67089	4.80402	−1.01819	3.82864	−1.94841	4.7362	−4.9879	9.20774
−2.82094	6.34739	−1.85081	5.34711	−1.09491	3.97029	−2.17525	5.22575	−5.12605	9.54438
−3.22038	6.86276	−1.99216	5.68698	−1.21777	4.29046	−2.32524	5.53733	−5.23066	9.77985
				−1.33169	4.65396	−2.51257	5.83481	−5.35172	10.02796
				−1.41787	4.77912

Y-11		Y-12		Y-13		Y-14		Y-15
Fractal dimension		Fractal dimension		Fractal dimension		Fractal dimension		Fractal dimension
2.61626		2.39535		2.75558		2.89323		2.19326
log(1/ε)	logN(ε)	log(1/ε)	logN(ε)	log(1/ε)	logN(ε)	log(1/ε)	logN(ε)	log(1/ε)	logN(ε)
−1.62587	2.48491	−0.48652	1.38629	−4.29255	7.29912	−3.9649	6.43615	−1.18446	5.31321
−2.12201	3.52636	−1.10673	2.89037	−4.35831	7.58528	−3.96595	6.44731	−1.41969	6.3081
−2.44392	4.35671	−1.34144	3.3673	−4.37327	7.64779	−3.97124	6.4552	−1.6145	7.05618
−2.66628	4.97673	−1.60714	4.04305	−4.55733	8.36939	−3.97709	6.47389	−1.73443	7.46107
−2.81491	5.47227	−1.76709	4.40672	−4.57658	8.44741	−3.98245	6.49224	−1.9163	8.00169
−2.93916	5.86079	−1.90508	4.72739	−5.01464	9.49582	−3.98944	6.50578	−2.22405	8.55082
−3.09533	6.29895	−2.07776	5.15906	−5.19216	9.6303	−3.99269	6.52062	−2.53818	8.80387
−3.24189	6.62804	−2.17507	5.4161	−5.28837	9.67263	−3.99758	6.53524	−2.80462	8.97487
−3.42898	6.99760

Tab.3 The number of grids and the logarithm of grid side length

Fig.7 Fitting results of fracture fractal dimensions.

Fig.8 The 3D scatter plot of sample burial depth, porosity and fracture fractal dimension.

Fig.9 Variation trends of fractal dimension and porosity for coal and shale samples.

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