A New Image Processing Workflow for the Detection of Quartz Types in Shales: Implications for Shale Gas Reservoir Quality Prediction
Abstract
:1. Introduction
2. Geological Setting
3. Samples
4. Methods
4.1. Bulk Density, Optical and Scanning Electron Microscopy-Based Petrography
4.2. Shale Lithofacies Classification
4.3. Quartz Type Classification
4.4. Image Analysis
5. Results
5.1. Bulk Density
5.2. Optical Microscopy
- Spiral-like quartz (Figure 6c) is rarely observed. It has a columnar outline and is longer than the surrounding detrital minerals. When the microscope focus was slightly adjusted, an internal three-dimensional spiral-like microstructure could be observed, which can be used to differentiate it with sponge spicules.
- Siliceous sponge spicules often show up as needle-like, or with a massive debris attached (Figure 6d–f). In some cases, when observed with the crossed polarizers under microscopy and by rotating the object stage, the structure of the needle-like object embedded in the massive debris could be found (Figure 6e). However, in some cases, the embedded structure could not be found (Figure 6f).
- Polycrystalline quartz (Figure 6g) tends to exceed or approach the size of the largest detrital grains and shows up a variety of morphologies, such as elliptical and elongated. It was common in the N-6, but relatively rare in the other samples.
- Quartz veins are bedding-parallel and have a very high length to width ratio. They are of variable size (normally around several hundred micrometers, Figure 7a,b; unlike fault veins cutting through the sample), and contain vertically fibrous crystals [48]. Moreover quartz veins typically contain solid bitumen [8], either along their margins or within the veins.
5.3. Scanning Electron Microscopy
5.4. Quartz Types Based on Optical and Electron Microscopy
5.5. Shale Lithofacies
5.6. Image Analysis
- (i)
- 4–9 µm,
- (ii)
- 9–14 µm,
- (iii)
- 14–19 µm,
- (iv)
- 19–24 µm,
- (v)
- 24–29 µm,
- (vi)
- 35–39 µm, and
- (vii)
- 39–44 µm.
6. Discussion
6.1. Advantages and Disadvantages of This Workflow
6.2. Influence of Clay-Sized Quartz on Shale Gas Reservoir
6.3. Origin of Clay-Sized Quartz
7. Conclusions
- A workflow was proposed in this paper to calculate the content of clay-sized quartz in gas shales, by using a combination of microscopies, image analysis, and data of TOC content, bulk density, XRD.
- A refined shale lithofacies classification with seven major categories and nineteen subcategories was conducted. Based on it, three lithofacies of the Niutitang Fm shales were identified.
- A variety of quartz types in shale were observed and described in detail, including the detrital quartz, vein quartz, “elongated” quartz, needle-like quartz, and spiral-like quartz. Furthermore, the intergrowth of organic matter with oriented minerals was also considered as a type of quartz.
- The contents of various components, e.g., detrital quartz, clay-sized quartz and detrital feldspar, were semi-quantitatively calculated. Clay-sized quartz may be the main factor affecting mesopore volume and BET specific surface area. It is believed that clay-sized quartz accounts for most of the bulk quartz content in the Niutitang Fm shales and could origin from feldspar and quartz dissolution, clay transformation, and biogenic quartz transformation.
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Sample | Bulk Mineralogical Composition (wt.%). 1 | TOC (%). 1 | Bulk Density (g/cm3) | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|
Q | K-Feldspar | Plagioclase | Dolomite | Pyroxene | Illite | Kaolinite | Pyrite | Barite | |||
N-1 | 71.1 | 10 | 4.1 | 8 | 6.8 | 10.0 | 2.24 | ||||
N-2 | 44.4 | 6.3 | 13.5 | 11.2 | 8.1 | 10.8 | 5.7 | 12.7 2 | 2.33 | ||
N-3 | 47.7 | 5.6 | 4.3 | 8.5 | 27.5 | 6.4 | 4.8 | 2.46 | |||
N-4 | 46.2 | 6.5 | 2.8 | 16.9 | 7.3 | 17 | 3.3 | 9.9 | 2.19 | ||
N-5 | 60.2 | 7.9 | 6.8 | 21.2 | 3.9 | 10.0 | 2.30 | ||||
N-6 | 50.6 | 4 | 3.7 | 27 | 10.4 | 4.3 | 8.8 | 2.34 |
AR, (%) | N-1 | N-2 | N-3 | N-4 | N-5 | N-6 | |||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
dQ | dF | dQ + F | dQ | dF | dQ + F | dQ | dF | dQ + F | dQ | dF | dQ + F | dQ | dF | dQ + F | dQ | dF | dQ + F | ||
Particle Size Interval, (μm) | (4–9) | 1.6 | 1.1 | 2.6 | 1 | 1 | 2 | 3 | 1.6 | 4.6 | 2.3 | 1.2 | 3.5 | 1.2 | 1.9 | 3.1 | 1.7 | 1.6 | 3.3 |
(9–14) | 2.9 | 2.1 | 4.9 | 1.7 | 1.9 | 3.5 | 3.3 | 2.1 | 5.4 | 3.5 | 2.1 | 5.6 | 1.7 | 1.6 | 3.3 | 2.5 | 1.0 | 3.5 | |
(14–19) | 3.4 | 1.2 | 4.6 | 2.6 | 1.8 | 4.4 | 3.1 | 1.4 | 4.5 | 3.2 | 1.5 | 4.7 | 1.7 | 3.5 | 5.2 | 1.3 | 1.7 | 3.1 | |
(19–24) | 1.8 | 2.4 | 4.2 | 1.4 | 1.6 | 3 | 3.1 | 2.7 | 5.7 | 3.5 | 0.9 | 4.4 | 2.2 | 2.7 | 4.9 | 1 | 1 | 2.1 | |
(24–29) | 1.5 | 0.4 | 2 | 1.2 | 2.2 | 3.4 | 3.6 | 2.0 | 5.6 | 1.3 | 0.9 | 2.1 | 1.1 | 1 | 2 | 1.1 | 0.4 | 1.5 | |
(29–34) | 1.7 | 0.5 | 2.2 | 0 | 0.4 | 0.4 | 0.5 | 1.4 | 1.9 | 0.7 | 0 | 0.7 | 1.5 | 1.8 | 3.4 | 0 | 0 | 0 | |
(34–39) | 0 | 0 | 0 | 0.4 | 0.5 | 0.9 | 2.7 | 0 | 2.7 | 0 | 0 | 0 | 0 | 1.4 | 1.4 | 0 | 0 | 0 | |
(39–44) | 0 | 0 | 0 | 0.5 | 0.6 | 1.1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | |
dQ/dF/dQ + F | 12.8 | 7.6 | 20.5 | 8.7 | 9.9 | 18.6 | 19.1 | 5.3 | 24.3 | 14.3 | 6.7 | 21 | 9.4 | 9.6 | 19 | 7.7 | 5.7 | 13.4 | |
(9–24)dQ | 8.1 | 5.7 | 9.4 | 10.1 | 5.6 | 4.9 | |||||||||||||
(9–24)dQ–dQ | 63 | 65 | 49 | 71 | 60 | 62.9 | |||||||||||||
(9–24)dQ + F | 13.7 | 10.9 | 15.6 | 14.7 | 13.4 | 8.7 | |||||||||||||
(9–24)dQ + F-dQ + F | 67.1 | 58.5 | 51.6 | 70 | 57.7 | 64.3 | |||||||||||||
Average dQ + F Size, (μm) | 11.1 | 12 | 11 | 10.2 | 11 | 9.4 | |||||||||||||
Standard deviation of dQ + F Size | 6.5 | 7.1 | 7.1 | 5.7 | 6.7 | 4.9 | |||||||||||||
RdQ-dF | 1.7 | 0.9 | 3.6 | 2.1 | 0.98 | 1.4 |
Sample | N-1 | N-2 | N-3 | N-4 | N-5 | N-6 | |
---|---|---|---|---|---|---|---|
Particle Size Interval, (μm) | (4, 9) | ||||||
(9, 14) | |||||||
(14, 19) | 0.08 | 0.29 | |||||
(19, 24) | 0.25 | 0.28 | 0.29 | 0.33 | 0.14 | ||
(24, 29) | 0.14 | 0.28 | |||||
(29, 34) | 0.18 | 0.62 | |||||
(34, 39) | 0.46 | 0.38 | |||||
(39, 44) | 0.3 |
Sample | Weight of Mineral Matter in 1 cm3 (g) | ||||||||
---|---|---|---|---|---|---|---|---|---|
Quartz | K-Feldspar | Plagioclase | Feldspar | Dolomite | Pyroxene | Clay Mineral | Pyrite | Barite | |
N-1 | 1.42 | 0.2 | 0.08 | 0.28 | 0 | 0 | 0.16 | 0 | 0.14 |
N-2 | 0.89 | 0.13 | 0 | 0.13 | 0.27 | 0 | 0.39 | 0.22 | 0.11 |
N-3 | 1.11 | 0.13 | 0.1 | 0.23 | 0 | 0.20 | 0.64 | 0.15 | 0 |
N-4 | 0.9 | 0.13 | 0.05 | 0.18 | 0.33 | 0.14 | 0.33 | 0 | 0.06 |
N-5 | 1.23 | 0.16 | 0 | 0.16 | 0 | 0.14 | 0.43 | 0 | 0.08 |
N-6 | 1.07 | 0.08 | 0.08 | 0.16 | 0 | 0 | 0.57 | 0.22 | 0.09 |
Parameter | N-1 | N-2 | N-3 | N-4 | N-5 | N-6 |
---|---|---|---|---|---|---|
ρQ, ρF (g/cm3) | 2.65 | |||||
TOC (%) | 10 | 12.7 | 4.8 | 9.9 | 10 | 8.8 |
WOM (g/cm3) | 0.25 | 0.33 | 0.13 | 0.24 | 0.26 | 0.23 |
WMM (g/cm3) | 2 | 2 | 2.3 | 2 | 2 | 2.1 |
VRdQ (%) | 12.9 | 8.7 | 19.1 | 14.3 | 9.4 | 7.7 |
WdQ (g/cm3) | 0.34 | 0.23 | 0.51 | 0.38 | 0.25 | 0.2 |
WRdQ-MM (%) | 17.1 | 11.6 | 21.7 | 19.5 | 12.2 | 9.7 |
WRdQ-S (%) | 15.2 | 9.9 | 20.6 | 17.4 | 10.8 | 8.7 |
ρS (g/cm3) | 2.24 | 2.33 | 2.46 | 2.19 | 2.3 | 2.34 |
WRtQ-MM (%) | 71.1 | 44.4 | 47.7 | 46.2 | 60.2 | 50.6 |
WcQ (g/cm3) | 1.05 | 0.64 | 0.59 | 0.51 | 0.96 | 0.84 |
WRcQ-tQ (%) | 74 | 72 | 53 | 57 | 78 | 79 |
WRcQ-S (%) | 47 | 28 | 24 | 23 | 42 | 36 |
WRcQ-MM (%) | 53 | 32 | 25 | 26 | 47 | 40 |
VReQ (%) | 0.8 | 0.61 | 0.65 | 0.29 | 0.9 | 0.76 |
WeQ (g/cm3) | 0.021 | 0.016 | 0.017 | 0.008 | 0.024 | 0.02 |
WReQ-tQ (%) | 1.5 | 1.8 | 1.6 | 0.9 | 1.9 | 1.9 |
WReQ-S (%) | 0.94 | 0.69 | 0.7 | 0.35 | 1.03 | 0.86 |
WReQ-MM (%) | 1.06 | 0.81 | 0.74 | 0.4 | 1.16 | 0.96 |
VRP (%) | - | - | 6 | 4.3 | 4.2 | - |
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Guo, S.; Misch, D.; Sachsenhofer, R.F.; Zhu, Y.; Tang, X.; Bai, W. A New Image Processing Workflow for the Detection of Quartz Types in Shales: Implications for Shale Gas Reservoir Quality Prediction. Minerals 2022, 12, 1027. https://doi.org/10.3390/min12081027
Guo S, Misch D, Sachsenhofer RF, Zhu Y, Tang X, Bai W. A New Image Processing Workflow for the Detection of Quartz Types in Shales: Implications for Shale Gas Reservoir Quality Prediction. Minerals. 2022; 12(8):1027. https://doi.org/10.3390/min12081027
Chicago/Turabian StyleGuo, Sen, David Misch, Reinhard F. Sachsenhofer, Yanming Zhu, Xin Tang, and Weichen Bai. 2022. "A New Image Processing Workflow for the Detection of Quartz Types in Shales: Implications for Shale Gas Reservoir Quality Prediction" Minerals 12, no. 8: 1027. https://doi.org/10.3390/min12081027
APA StyleGuo, S., Misch, D., Sachsenhofer, R. F., Zhu, Y., Tang, X., & Bai, W. (2022). A New Image Processing Workflow for the Detection of Quartz Types in Shales: Implications for Shale Gas Reservoir Quality Prediction. Minerals, 12(8), 1027. https://doi.org/10.3390/min12081027