Na Zhang, Manchao He, Bo Zhang, Fengchao Qiao, Hailong Sheng, Qinhong Hu. Pore Structure Characteristics and Permeability of Deep Sedimentary Rocks Determined by Mercury Intrusion Porosimetry. Journal of Earth Science, 2016, 27(4): 670-676. doi: 10.1007/s12583-016-0662-z
Citation:
Xing Chen, Yanjun Shen, Qingyi Mu, Panpan Xu, Fenghao Duan, Jianbing Peng. Why are the Qinling Mountains Significant to China?. Journal of Earth Science, 2025, 36(1): 357-363. doi: 10.1007/s12583-024-0140-y
Na Zhang, Manchao He, Bo Zhang, Fengchao Qiao, Hailong Sheng, Qinhong Hu. Pore Structure Characteristics and Permeability of Deep Sedimentary Rocks Determined by Mercury Intrusion Porosimetry. Journal of Earth Science, 2016, 27(4): 670-676. doi: 10.1007/s12583-016-0662-z
Citation:
Xing Chen, Yanjun Shen, Qingyi Mu, Panpan Xu, Fenghao Duan, Jianbing Peng. Why are the Qinling Mountains Significant to China?. Journal of Earth Science, 2025, 36(1): 357-363. doi: 10.1007/s12583-024-0140-y
Sedimentary rock is the most dominant rock type distributed on the Earth surface and also a typical kind of natural porous media. About 80% of the world total reserves of mineral resources are stored in sedimentary rocks. Thus, pore structure of sedimentary rock plays an important role in many fields of rock mechanics and geotechnical engineering. In addition, lots of critical engineering geological problems or hazards are closely associated with pore structure characteristics (i.e., pore size distribution and porosity) and pore structure related properties (e.g., permeability, strength and durability) of rock.
Pore structure characteristics are of great importance to rock engineering projects, mainly because it can affect the engineering properties of rock in many ways. On one hand, it can have an important effect on the physical-mechanical properties (Di Benedetto et al., 2015) and failure modes of various rocks (Hudyma et al., 2004). For example, Al-Harthi et al. (1999) studied the effect of pores on the engineering properties of Saudi Arabia basalt by using an image analysis technique to estimate the porosity of basalt core samples, and they found that there are good correlations between porosity and mechanical parameters such as compressive strength, modulus of elasticity and Poisson*s ratio. Hudyma et al. (2004) assessed the effect of macroporosity on both the uniaxial compressive strength and failure modes of the lithophysae-rich tuff and plaster analog models, and it was discovered that the compressive strength decreases with an increasing porosity due to lithophysae in tuff and cavities in plaster analog specimens. Meanwhile, it was also noticed that the failure modes transited from spalling through web failure as the percentage of macroporosity within the specimen increased. Sabatakakis et al. (2008) investigated the intrinsic influence of microstructure on the measured strength parameters of intact chemical and clastic sedimentary rocks through a large number of laboratory tests and established the regression equation between total porosity and strength. Török and Vársárrhelyi (2010) suggested that, compared with other petrophysical constituents, porosity had the dominant influence on strength and durability of travertine. Moreover, it is also well known that the mechanical properties of rock vary with water content (Yilmaz, 2010; Erguler and Ulusay, 2009), and the water absorption of rock is greatly affected by pore structure (Zhang et al., 2014, 2012).
On the other hand, pore structure also has a close relationship with permeability and diffusivity of rock and it can directly affect the fluid flow in rock formations. For instance, Rezaee et al. (2006) published a set of relationships between permeability, porosity and pore-throat size for 144 carbonate samples, and these relationships can be used to estimate permeability from porosity and pore-throat radii. Siitari-Kauppi et al. (1997) studied the matrix diffusion of non-sorbing tracers in tonalite from Central Finland and discussed the effect of microscale pore structure on matrix diffusion. Also, Clavaud et al. (2008) analyzed the effect of pore space geometry on permeability anisotropy of three different rocks, and their results showed that for sandstones it is well correlated with the presence of bedding, while for volcanic rocks it is related to the orientation of vesicles or cracks.
Furthermore, pore structure plays an important role in evaluating rock reservoir storage and productivity in oil and gas exploration. Thus, so far extensively specialized research works regarding pore networks and permeability characterization of reservoir rocks have been carried out in the field of oil and gas engineering (Dong et al., 2015; Swanson et al., 2015; Loucks et al., 2012; Ross and Bustin, 2009). Overall, in-depth understanding and quantitative description of the pore structure characteristics have a considerable significance in a wide range of rock engineering fields, including civil, mining, petroleum, geothermal and water conservancy.
However, systematic investigations on engineering sedimentary rocks, especially deep engineering sedimentary rocks, are currently still inadequate. In the present study, pore structure characteristics, such as pore size distribution and porosity, were measured using mercury intrusion porosimetry (MIP) for seven types of deep sedimentary rocks including mudstone, sandy mudstone, siltstone, fine sandstone, medium sandstone, coarse sandstone and conglomerate, with 50 rock samples in total. Permeability of these rocks was also calculated based on MIP data using empirical equations proposed in the literature. Finally, a correlative relationship between rock permeability and porosity was statistically obtained.
1.
MATERIALS AND METHODS
1.1
Collection and Preparation of Rock Samples
The sedimentary rock samples investigated in this study were all collected in the depth interval of 1 050-1 500 m from a deep underground coal mine located in Northeast China. During collection, samples were wrapped with ziplock bags and then sealed immediately with wax after being transported to the ground to maintain their original state. In laboratory, in order to remove moisture in pore spaces, numbered samples were dried in a vacuum drying oven for 2 days at 60 ℃ until their weights had a little change. Before MIP analysis, all dried rock samples were kept in desiccators. Table 1 lists the basic information of seven types of deep sedimentary rock samples.
Table
1.
Sample information of the sedimentary rocks investigated in this study
Sample No.
Lithology
Number of samples
Depth (m)
Geological description
M1-M15
Mudstone
15
1 159.56-1 416.55
Gray, dense, obvious bedding, occasional grey or brown filling
As a widely accepted and well established technique for characterizing pore structure of porous media, MIP has many special features compared with other pore structure characterization methods (Gao and Hu, 2013; Giesche, 2006). Firstly, it can investigate a wide range of pore-throat sizes (normally between 3 nm and ~500 μm). Secondly, it is a relatively time-saving technique. Thirdly, it can provide a comprehensive set of pore characteristics information. In summary, MIP approach not only can directly measure pore-throat size distribution (PSD) and porosity, but also can indirectly evaluate other pore characteristics, such as total pore surface area and median pore diameter. Therefore, in this study the pore structure characteristics of rock samples including PSD, porosity, total pore surface area, median pore diameter, bulk density and apparent density were all obtained by MIP using an AutoPore Ⅳ 9510 porosimeter (Micromeritics Instrument Corporation, Norcross, GA).
The definitions of indirect parameters are briefly introduced as follows. Total pore surface area is the sum of all calculated pore surface areas. Median pore diameter by area is the diameter corresponding to 50% total area on the cumulative pore area versus diameter plot. Bulk density is defined as the mass of the dry bulk sample divided by the bulk volume that includes not only the volume of the solid particles but also the space between the particles. Whereas, apparent density (also called skeletal density) is defined as the mass of the dry bulk sample divided by its apparent volume that is the total sample volume, excluding space between the particles and open pores, but including closed pores.
During MIP tests, both low pressure and high pressure analyses were performed. The highest pressure applied to all the rock samples was approximately 60 000 psia. And the lowest pressure applied to all the rock samples except mudstone was about 0.16 psia. However, for mudstone and sandy mudstone samples it was about 5 psia due to their higher tightness compared to other types of rock samples. Correspondingly, the smallest and largest pore-throat diameters determined for mudstone and sandy mudstone rock samples were about 3 nm and 36 μm, while for the other types of rock samples they were about 3 nm and 1 100 μm. Each rock sample (~5 g; solid) was evacuated to 50 μm Hg for 5 min and the equilibration time was set to be 10 s.
1.3
Permeability Estimation Method Based on MIP Data
Due to the difficulties and high costs related to direct measurement of rock permeability, especially for tight rocks with extremely low permeability, estimation of permeability using measured MIP data is now regarded as an efficient and reliable alternative method (Gao and Hu, 2013). The following equation introduced by Katz and Thompson(1987, 1986) was employed to calculate the permeability
k=1/89(Lmax)2(Lmax/Lc)ϕS(Lmax)
(1)
where k (darcy, D) is the air permeability; Lmax (μm) is the pore-throat diameter at which hydraulic conductance is maximum; Lc (μm) is the characteristic length which is the pore-throat diameter corresponding to the threshold pressure Pt (psia); ϕ is porosity; S(Lmax) represents the fraction of connected pore space at pore width of Lmax which is calculated as (VLmax)/(Vtot). The threshold pressure Pt is determined at the inflection point of the cumulative intrusion curve and the selection of Lmax is dependent on Pt. The step-by-step data processing procedures to determine each parameter in Eq. (1) can be found in the work of Gao and Hu (2013).
2.
RESULTS AND DISCUSSION
2.1
PSD Curves
The PSD curves of these seven types of sedimentary rocks (Fig. 1) are classified into three broader groups (mudstone, sandstone, and conglomerate) according to their lithology. It can be seen from Fig. 1 that all of the three groups have a kind of S-shaped distribution patterns. The PSD patterns of the rock samples in the same lithological group are relatively similar even if slight differences can be found among them. However, the patterns are quite different among distinct groups. Specifically, the front and middle segment of the PSD curve for mudstone is relatively flat and wide with a sharp rise around the pore diameter of 0.05 μm. Unlike mudstone, the PSD curves of sandstone and conglomerate generally have two inflection points which divide the curves into three segments. According to Hartmann and Beaumont (2000), pore sizes are classified as nanopores (< 0.1 μm), micropores (0.1-0.5 μm), mesopores (0.5-2.5 μm), macropores (2.5-10 μm) and megapores (> 10 μm). Therefore, it is suggested that there are predominantly higher percentage of nanopores (pore diameter < 0.05 μm) for mudstone while sandstone and conglomerate apparently have a relatively more proportional of larger-sized pores. Meanwhile, from Fig. 1 it can also be clearly observed that the total pore volumes of the rock samples in different lithological groups are quite different. Moreover, each group has a distinct distribution range of total pore volume. Overall, the ascending order of the average total pore volume for each lithological group is mudstone < < sandstone < conglomerate.
Figure
1.
Pore size distribution of seven types of sedimentary rocks.
The cause of this discrepancy among different lithological groups may be closely linked to their geological origins. Other reasons for different lithological groups having distinct pore size distribution may include the grain size distribution and the shape of the grains of the original particles forming the sedimentary rock, and whether a secondary pore system could have developed for the siltstone.
2.2
Pore Distribution Frequency
In order to analyze the dominant pore-size ranges of different rock types, pore distribution frequencies at ten different intervals of pore-throat diameter were calculated as pore volume percentages and listed in Table 2. The values in Table 2 show that the pore distribution frequencies are quite different for each lithological group. Meanwhile, a comparison of the frequencies among different types of sedimentary rocks reveals that different rock types have different dominant pore-size ranges. Specifically, for mudstone and sandy mudstone, about 80%-95% of the pores are smaller than 10 μm among which about 60%-80% of the pores are smaller than 0.05 μm. For siltstone approximately 66% of the pores distribute in the range of 0.01-0.05 μm. And for the other three types of sandstone and conglomerate, about 30%-40% of the pore-throats are concentrated in the range of 0.01-0.05 μm and about 30%-40% of the pores are larger than 10 μm.
Table
2.
Pore distribution frequency (averages±standard deviation, %) at ten different intervals of pore-throat diameter
Pore-throat diameter (μm)
Mudstone
Sandy mudstone
Siltstone
Fine sandstone
Medium sandstone
Coarse sandstone
Conglomerate
500-1 100
N/Aa
N/A
11.1±5.1
12.6±7.1
16.7±3.3
19.0±10.3
10.7±5.9
100-500
N/A
N/A
12.4±7.2
12.2±6.0
15.9±4.6
14.6±6.4
14.9±5.2
36-100
N/A
N/A
1.2±0.6
1.9±0.6
2.0±0.4
2.0±0.2
1.9±0.5
10-36
19.5±5.7
5.8±2.1
1.0±0.4
1.4±0.5
1.5±0.3
1.7±0.4
2.1±0.6
1-10
8.6±6.4
5.2±4.9
0.5±0.3
1.0±0.3
3.6±5.4
2.0±0.9
5.9±2.9
0.1-1
8.3±12.3
5.6±4.2
1.2±0.7
6.3±2.4
4.9±2.9
7.2±4.3
15.5±5.4
0.05-0.1
3.5±4.6
2.7±2.0
2.6±2.4
3.4±1.3
3.3±1.5
5.0±1.1
12.7±4.6
0.01-0.05
16.1±11.5
43.6±14.1
66.2±9.3
40.2±10.8
31.1±12.1
37.1±8.0
39.8±8.2
0.005-0.01
22.2±11.6
32.9±13.4
8.0±3.6
18.0±2.4
17.2±4.7
9.8±3.6
3.5±1.5
0.003-0.005
21.8±15.1
4.2±5.0
1.3±1.5
2.9±1.7
3.7±1.2
1.5±1.0
0.3±0.3
a N/A. Pore size ranges are not applicable to mudstone and sandy mudstoneone and sandmudstone due to different minimum intrusion pressures chosen for their MIP measurements according to their high tightness.
In summary, for fine grained rock types such as mudstone, sandy mudstone and siltstone, their pore spaces are mainly occupied by nanopores with pore-throats < 0.05 μm while for coarse grained rock types such as conglomerate and sandstone, there are relatively high percentage of pore spaces occupied by large-sized megapores with pore-throats > 10 μm. Finally, what needs to be pointed out is that the different patterns of PSD for different lithological groups presented in Subsection 2.1 can be well explained by the distinct pore distribution frequencies summarized above.
2.3
Porosity and Other Pore-Structure Parameters
The porosity and a few other relative pore structure parameters of the seven types of sedimentary rocks determined by MIP are listed in Table 3. Among different rock types, mudstone has the lowest averaged porosity value of 3.37%, whereas conglomerate has the largest one of 18.79%. Moreover, if we consider these seven lithological types as three major groups, then the porosities for mudstone, sandstone, and conglomerate groups are 3.96%±2.16%, 14.03%±4.09%, and 18.79%±3.63%, respectively, from which we can more clearly see the remarkable differences among different lithological groups. The porosities of different types of sedimentary rocks are further compared in Fig. 2. It can be easily found that from mudstone to conglomerate with an increase in grain size the corresponding porosity also increases notably.
Table
3.
Basic rock and pore structure parameters determined by mercury intrusion porosimetry (MIP)
2.4
Permeability of Different Types of Sedimentary Rocks
Table 4 presents the statistical data of the estimated permeability for seven types of sedimentary rocks investigated in this study. According to the permeability average values listed in Table 4, mudstone has the lowest permeability (3.64℅10-6 mD) while conglomerate has the highest one (8.59℅10-4 mD). A comparison of permeability values (Fig. 3) shows a remarkable difference among seven different rock types. Moreover, it is suggested that from mudstone to conglomerate, rock permeability increases with an increase of grain size, while there is only an exception of siltstone which has a relatively larger porosity (15.2%±4.34%).
Table
4.
Statistics of the estimated permeability for seven different types of sedimentary rocks investigated in this study
The relationship between permeability and porosity of the investigated sedimentary rocks is shown in Fig. 4. As indicated in the figure, with an increase of porosity, permeability increases exponentially. The regression analysis shows that there is a good fitting between them (R2=0.95) with following relationship
k=2×10−6e0.3241ϕ
(2)
Figure
4.
Relationship between permeability and porosity of the deep sedimentary rocks.
where k (mD) represents permeability and ϕ (%) is porosity. The observed empirical relationship between permeability and porosity in this study could be easily used to derive permeability of similar engineering rocks, only based on relatively easy-to-obtain porosity data. It will be a useful and practical reference for engineers working in the related engineering (rock, mining, oil/gas and geothermal) fields.
3.
CONCLUSIONS
As the most common rock type on the Earth surface, sedimentary rocks have attracted continuous attention of the academics and engineers in different engineering fields. This study investigated the pore size distribution and important pore structure parameters of seven common types of deep sedimentary rocks by means of MIP. Moreover, air permeability of these rock types was calculated based on MIP data according to an approach of Katz and Thompson.
The sedimentary rock samples were grouped into three main broader groups of mudstone, sandstone and conglomerate and their overall porosity, pore size distribution and permeability were compared. It was found that overall porosity and permeability increase with grain size from mudstone to conglomerate and different S-shaped pore size distributions for the three main groups. Meanwhile, different dominant pore-size ranges were found for different lithological groups. Furthermore, the relation between porosity and permeability follows an exponential function with a high coefficient of determination.
Results of the present study can contribute to the enrichment of the basic geological database of the sedimentary reservoir rocks and thus will provide vital references for the scientific community.
ACKNOWLEDGMENTS:
This study was supported by the National Natural Science Foundation of China (Nos. 41502264, 51134005), the Specialized Research Fund for the Doctoral Program of Higher Education of China (No. 20130023120016), and the Fundamental Research Funds for the Central Universities of China (No. 2010QL07). The final publication is available at Springer via http://dx.doi.org/10.1007/s12583-016-0662-z.
Bai, Y., Ma, L. B., Xu, S. Q., et al., 2015. A Geometric Morphometric Study of the Wing Shapes ofPieris Rapae (Lepidoptera: Pieridae) from the Qinling Mountains and Adjacent Regions: an Environmental and Distance-Based Consideration. Florida Entomologist, 98(1): 162–169. https://doi.org/10.1653/024.098.0128
Chen, S. C., Wang, Y. T., Hu, Q. Q., et al., 2018. Genetic Type and Geochronology of the Granodiorite Porphyry Dikes in the Fengtai Ore Concentration Area, West Qinling Orogen, and Their Geological Significance. Acta Geoscientica Sinica, 39 (1): 14–26. https://doi.org/10.3975/cagsb.2017.122902 (in Chinese with English Abstract)
Chen, Y. P., 2019. Significance and Strategies on the Ecological Civilization Construction at Qinling Mountains. Journal of Earth Environment, 10(1): 1–11 (in Chinese with English Abstract)
Cui, G. Y., Zhang, Y., Chao, Y., et al., 2023. Land Use Change and Eco Environmental Effects in Qinling Mountains in Recent 40 Years. Research of Soil and Water Conservation, 30(1): 319–326 (in Chinese with English Abstract)
Deng, C. H., Bai, H. Y., Gao, S., et al., 2018. Spatial-Temporal Variation of the Vegetation Coverage in Qinling Mountains and Its Dual Response to Climate Change and Human Activities. Journal of Natural Resources, 33(3): 425–438 (in Chinese with English Abstract)
Dong, Y. P., Sun, S. S., Santosh, M., et al., 2021. Central China Orogenic Belt and Amalgamation of East Asian Continents. Gondwana Research, 100(6): 131–194. https://doi.org/10.1016/j.gr.2021.03.006
Fei, X. T., 2017. The Formation and Development of the Chinese Nation with Multi-Ethnic Groups. International Journal of Anthropology and Ethnology, 1(1): 1–31. https://doi.org/10.1186/s41257-017-0001-z
Gao, P., Gao, S., Zhong, Z., 2019. Study on Geo-Culture and Translation Based on the Chinese Geography of Qinling Mountains-Huaihe River Line. Education Research and Social Science, 322(1): 294–296
Guo, S., Pei, Y. Q., Hu, S., et al., 2021. Risk Assessment of Geological Hazards of Qinling-Daba Mountain Area in Shaanxi Province Based on FAHP and GIS. In Journal of Physics: Conference Series, 1992(2): 22–53. https://doi.org/10.1088/1742-6596/1992/2/022053
He, H. M., Zhou, J., Peart, M. R., et al., 2012. Sensitivity of Hydrogeomorphological Hazards in the Qinling Mountains, China. Quaternary International, 282: 37–47. https://doi.org/10.1016/j.quaint.2012.06.002
Jia, Y., Wang, B., Zhang, S. L., 2023. Construction of Ecological Security Pattern of the Chinese Section of the "Silk Road Economic Belt" Based on the Minimum Cumulative Resistance Model. GeoJournal, 88(1): 409–424. https://doi.org/10.1007/s10708-022-10615-6
Li, J. Y., Fu, B. J., Sun, J. L., et al., 2021. Ecological Civilization Construction at Qinling Mountains in the New Era. Journal of Natural Resources, 36(10): 2449–2463. https://doi.org/10.31497/zrzyxb.20211001 (in Chinese with English Abstract)
Li, M. Y., Liang, D., Xia, J., et al., 2021. Evaluation of Water Conservation Function of Danjiang River Basin in Qinling Mountains, China Based on InVEST Model. Journal of Environmental Management, 286: 112212. https://doi.org/10.1016/j.jenvman.2021.112212
Ma, Q., Zhang, Y., Yuan, J. G., 2022. Identification of Prime Plants and Conservation Gap in Priority Protection Areas of Biodiversity in Qinling Mountains. Journal of Northwest Forestry University, 37(2): 180–185. https://doi.org/10.3969/j.issn.1001-7461.2022.02.25 (in Chinese with English Abstract)
Peng, J. B., Shen, Y. J., Jin, Z., et al., 2023. Key Thoughts on the Study of Ecgeological Environment System in Qinling Mountains. Acta Ecologica Sinica, 43(11): 4344–4358. https://doi.org/10.5846/stxb202303310631 (in Chinese with English Abstract)
Rogers, S., Chen, D., Jiang, H., et al., 2020. An Integrated Assessment of China's South-North Water Transfer Project. Geographical Research, 58(1): 49–63. https://doi.org/10.1111/1745-5871.12361
Shao, Y. T., Mu, X. M., He, Y., et al., 2019. Spatiotemporal Variations of Extreme Precipitation Events at Multi-Time Scales in the Qinling-Daba Mountains Region, China. Quaternary International, 525: 89–102. https://doi.org/10.1016/j.quaint.2019.07.029
Shen, Y. J., Chen, X., Peng, J. B., et al., 2024. Background Characteristics of Ecological Geological Environment System in Qinling Mountains and Assumption of Its Theoretical System. Earth Science, 49(6): 2103–2119. https://doi.org/10.3799/dqkx.2023.210 (in Chinese with English Abstract)
Sheir, F., Li, W., Zhang, L., et al., 2024. Structural Geology and Chronology of Shear Zones along the Shangdan Suture in Qinling Orogenic Belt, China: Implications for Late Mesozoic Intra-Continental Deformation of East Asia. Journal of Earth Science, 35(2): 376–393. https://doi.org/10.1007/s12583-022-1753-7
Wang, H. Y., Song, J. X., Wu, Q., 2022. Temporal and Spatial Variation Characteristics of Water Conservation Function of Qinling Mountains Under the Background of Climate Change. Journal of Soil and Water Conservation, 36(5): 212–219 (in Chinese with English Abstract)
Wang, L. Y., Chen, S. F., Zhu, W. B., et al., 2021. Spatiotemporal Variations of Extreme Precipitation and Its Potential Driving Factors in China's North-South Transition Zone during 1960–2017. Atmospheric Research, 252: 105429. https://doi.org/10.1016/j.atmosres.2020.105429
Wang, S., Mo, D. S., Wu, Q. Y., et al., 2023. Design and Analysis of Sustainable Models for Qinling Ecological Protection and Mining Development. Minerals Engineering, 204: 108446. https://doi.org/10.1016/j.mineng.2023.108446
Wang, X. F., Fu, X. X., Chu, B. Y., et al., 2021. Spatio-Temporal Variation of Water Yield and Its Driving Factors in Qinling Mountains Barrier Region. Journal of Natural Resources, 36(10): 2507–2521. https://doi.org/10.31497/zrzyxb.20211005
Wang, X. F., Lu, K. S., Wang, S., et al., 2017. Spatiotemporal Assessment of Rainstorm Hazard Risk in Qinling Mountains of China. Human and Ecological Risk Assessment, 23(2): 257–275. https://doi.org/10.1080/10807039.2016.1240609
Xie, W. Q., Yao, Y. H., 2024. Quantifying Human Activity Intensity in the Qinling-Daba Mountains. People and Nature, 6(4): 1486–1501. https://doi.org/10.1002/pan3.10649
Xue, Y. Y., Liu, H. Y., Wang, Z. Y., et al., 2022. Reworking of the Juvenile Crust in the Late Mesozoic in North Qinling, Central China. Journal of Earth Science, 33(3): 623–641. https://doi.org/10.1007/s12583-021-1521-0
Yu, F., Li, C. L., Yuan, Z. Q., et al., 2023. How Do Mountain Ecosystem Services Respond to Changes in Vegetation and Climate? An Evidence from the Qinling Mountains, China. Ecological Indicators, 154: 110922. https://doi.org/10.1016/j.ecolind.2023.110922
Yu, F. Q., Zhang, B. P., Yao, Y. H., et al., 2022. Identifying Connectivity Conservation Priorities among Protected Areas in Qinling-Daba Mountains, China. Sustainability, 14(8): 4377. https://doi.org/10.3390/su14084377
Zhang, G. W., Guo, A. L., Dong, Y. P., 2019. Rethinking of the Qinling Orogenic Belt. Journal of Geomechanies, 25(5): 746–768 (in Chinese with English Abstract)
Zhang, Z. P., Duan, K. Q., Liu, H. C., et al., 2022. Runoff Projections of the Qinling Mountains and Their Impact on Water Demand of Guanzhong Region in Northwest China. Journal of Mountain Science, 19(8): 2272–2285. https://doi.org/10.1007/s11629-022-7358-x
Weiji Sun, Jialong Li, Qiang Liu, et al. Complexity upgrade and triggering mechanism of mixed-wettability: Comparative study of CO2 displacement in different phases. Fuel, 2024, 369: 131798. doi:10.1016/j.fuel.2024.131798
2.
Andreas Leemann, Michał Góra, Barbara Lothenbach, et al. Alkali silica reaction in concrete - Revealing the expansion mechanism by surface force measurements. Cement and Concrete Research, 2024, 176: 107392. doi:10.1016/j.cemconres.2023.107392
3.
Hyunjun Oh, Sertaç Akar, Koenraad Beckers, et al. Techno-economic feasibility of geothermal energy production using inactive oil and gas wells for district heating and cooling systems in Tuttle, Oklahoma. Energy Conversion and Management, 2024, 308: 118390. doi:10.1016/j.enconman.2024.118390
4.
Chunsheng Yu, Hengchun Deng, Qi Jiang, et al. Mechanism of pore expansion and fracturing effect of high-temperature ScCO2 on shale. Fuel, 2024, 363: 130950. doi:10.1016/j.fuel.2024.130950
Jialiang Wang, Chen Chen, Youjia Hu, et al. Scheme design and flow performance analysis of double-bits combination wire-line coring technology for seafloor drill. Marine Georesources & Geotechnology, 2024. doi:10.1080/1064119X.2024.2309283
7.
Vynotdni Rathinasamy, Edy Tonnizam Mohamad, Ibrahim Komoo, et al. Groundwater exploitation in Southern Johor Bahru, Malaysia: Prospects and challenges while drilling and its mitigation measures. Physics and Chemistry of the Earth, Parts A/B/C, 2023, 129: 103300. doi:10.1016/j.pce.2022.103300
8.
Tengjiao Sun, Xiaoping Luo, Wentian Mi, et al. Characterization of ultra-deeply buried middle Triassic Leikoupo marine carbonate petroleum system (!) in the Western Sichuan depression, China. Marine and Petroleum Geology, 2023, 150: 106099. doi:10.1016/j.marpetgeo.2023.106099
9.
Yong Chen, Ronghua Liu, Gang Wang, et al. Pore and gas desorption characteristics of primary coal with different degrees of metamorphism. Energy Science & Engineering, 2023, 11(9): 3185. doi:10.1002/ese3.1513
10.
Tao Wang, Ze Deng, Haiyan Hu, et al. Pore structure and fractal characteristics of transitional shales with different lithofacies from the eastern margin of the Ordos Basin. Energy Science & Engineering, 2023, 11(11): 3979. doi:10.1002/ese3.1580
11.
Yuxuan Zhou, Shugang Li, Yang Bai, et al. Joint Characterization and Fractal Laws of Pore Structure in Low-Rank Coal. Sustainability, 2023, 15(12): 9599. doi:10.3390/su15129599
12.
Ruobing Liu, Zhihong Wei, Aoqi Jia, et al. 川东南地区五峰-龙马溪组深层超压富有机质页岩孔隙结构分形特征及其地质意义. Earth Science-Journal of China University of Geosciences, 2023, 48(4): 1496. doi:10.3799/dqkx.2022.177
13.
Mohamed K. Salah, Hammad Tariq Janjuhah, Josep Sanjuan. Analysis and Characterization of Pore System and Grain Sizes of Carbonate Rocks from Southern Lebanon. Journal of Earth Science, 2023, 34(1): 101. doi:10.1007/s12583-020-1057-8
14.
Huazhou Huang, Yuantao Sun, Xiantong Chang, et al. Experimental Investigation of Pore Characteristics and Permeability in Coal-Measure Sandstones in Jixi Basin, China. Energies, 2022, 15(16): 5898. doi:10.3390/en15165898
15.
Hongdan Yu, Chen Lu, Weizhong Chen, et al. Characterization of microstructural features of Tamusu mudstone. Journal of Rock Mechanics and Geotechnical Engineering, 2022, 14(6): 1923. doi:10.1016/j.jrmge.2022.05.017
16.
Chaozheng Li, Xiangbai Liu, Fuliang You, et al. Pore Size Distribution Characterization by Joint Interpretation of MICP and NMR: A Case Study of Chang 7 Tight Sandstone in the Ordos Basin. Processes, 2022, 10(10): 1941. doi:10.3390/pr10101941
17.
Bing Zhang, Hanpeng Wang, Peng Wang, et al. Macro- and microdamage characteristics and multiscale damage constitutive model of gas-bearing coal under loading. Journal of Petroleum Science and Engineering, 2022, 217: 110848. doi:10.1016/j.petrol.2022.110848
18.
Na Zhang, Shuaidong Wang, Fangfang Zhao, et al. Characterization of the Pore Structure and Fluid Movability of Coal-Measure Sedimentary Rocks by Nuclear Magnetic Resonance (NMR). ACS Omega, 2021, 6(35): 22831. doi:10.1021/acsomega.1c03247
19.
Songtao Wu, Shixiang Li, Xuanjun Yuan, et al. Fluid Mobility Evaluation of Tight Sandstones in Chang 7 Member of Yanchang Formation, Ordos Basin. Journal of Earth Science, 2021, 32(4): 850. doi:10.1007/s12583-020-1050-2
20.
Hao Lu, Hongming Tang, Meng Wang, et al. Pore Structure Characteristics and Permeability Prediction Model in a Cretaceous Carbonate Reservoir, North Persian Gulf Basin. Geofluids, 2021, 2021: 1. doi:10.1155/2021/8876679
21.
Bharath Melugiri Shankaramurthy, Kejin Wang, Franciszek Hasiuk. Influence of carbonate coarse aggregate properties on surface resistivity of high performance concrete. Construction and Building Materials, 2021, 312: 125402. doi:10.1016/j.conbuildmat.2021.125402
22.
Dengke Liu, Junwei Su, Zhaolin Gu, et al. Geochemical Properties and Pore Structure Control on Oil Extraction of Shale. Lithosphere, 2021, 2021(Special 1) doi:10.2113/2021/6646791
23.
Manguang Gan, Liwei Zhang, Xiuxiu Miao, et al. Application of computed tomography (CT) in geologic CO2 utilization and storage research: A critical review. Journal of Natural Gas Science and Engineering, 2020, 83: 103591. doi:10.1016/j.jngse.2020.103591
24.
Qinhong Hu, Ryan P. Quintero, Hesham F. El-Sobky, et al. Coupled nano-petrophysical and organic-geochemical study of the Wolfberry Play in Howard County, Texas U.S.A.. Marine and Petroleum Geology, 2020, 122: 104663. doi:10.1016/j.marpetgeo.2020.104663
25.
Binyu Ma, Qinhong Hu, Shengyu Yang, et al. Multiple Approaches to Quantifying the Effective Porosity of Lacustrine Shale Oil Reservoirs in Bohai Bay Basin, East China. Geofluids, 2020, 2020: 1. doi:10.1155/2020/8856620
26.
Xiaobo Zhang, Jing Guo, Qinhong Hu, et al. Effects of Fe-rich acid mine drainage on percolation features and pore structure in carbonate rocks. Journal of Hydrology, 2020, 591: 125571. doi:10.1016/j.jhydrol.2020.125571
27.
Qian Yin, Hongwen Jing, Richeng Liu, et al. Pore characteristics and nonlinear flow behaviors of granite exposed to high temperature. Bulletin of Engineering Geology and the Environment, 2020, 79(3): 1239. doi:10.1007/s10064-019-01628-6
28.
Yuxuan Xia, Jianchao Cai, Edmund Perfect, et al. Fractal dimension, lacunarity and succolarity analyses on CT images of reservoir rocks for permeability prediction. Journal of Hydrology, 2019, 579: 124198. doi:10.1016/j.jhydrol.2019.124198
29.
Antonio Rodríguez de Castro, Mehrez Agnaou, Azita Ahmadi-Sénichault, et al. Application of Non-toxic Yield Stress Fluids Porosimetry Method and Pore-Network Modelling to Characterize the Pore Size Distribution of Packs of Spherical Beads. Transport in Porous Media, 2019, 130(3): 799. doi:10.1007/s11242-019-01339-2
30.
Yiwen Gong, Ilham El-Monier. Microstructure diagnosis of the fractured tight sandstone using image analysis. Journal of Petroleum Science and Engineering, 2019, 183: 106449. doi:10.1016/j.petrol.2019.106449
31.
Junqian Li, Pengfei Zhang, Shuangfang Lu, et al. Scale-Dependent Nature of Porosity and Pore Size Distribution in Lacustrine Shales: An Investigation by BIB-SEM and X-Ray CT Methods. Journal of Earth Science, 2019, 30(4): 823. doi:10.1007/s12583-018-0835-z
32.
Jianguo Huang, Tao Ren, Haijun Zou. Genesis of Xinzhai Sandstone-Type Copper Deposit in Northern Laos: Geological and Geochemical Evidences. Journal of Earth Science, 2019, 30(1): 95. doi:10.1007/s12583-018-0861-x
33.
Yi Han, Chong Zhang, Zhan-song Zhang, et al. Pore structure classification and logging evaluation method for carbonate reservoirs: A case study from an oilfield in the Middle East. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 2019, 41(14): 1701. doi:10.1080/15567036.2018.1549145
34.
Yi Pan, Dong Hui, Pingya Luo, et al. Influences of subcritical and supercritical CO2 treatment on the pore structure characteristics of marine and terrestrial shales. Journal of CO2 Utilization, 2018, 28: 152. doi:10.1016/j.jcou.2018.09.016
35.
HEXIN HUANG, LEI CHEN, WEI SUN, et al. PORE-THROAT STRUCTURE AND FRACTAL CHARACTERISTICS OF SHIHEZI FORMATION TIGHT GAS SANDSTONE IN THE ORDOS BASIN, CHINA. Fractals, 2018, 26(02): 1840005. doi:10.1142/S0218348X18400054
36.
YUXUAN XIA, JIANCHAO CAI, WEI WEI, et al. A NEW METHOD FOR CALCULATING FRACTAL DIMENSIONS OF POROUS MEDIA BASED ON PORE SIZE DISTRIBUTION. Fractals, 2018, 26(01): 1850006. doi:10.1142/S0218348X18500068
37.
Na Zhang, Fangfang Zhao, Pingye Guo, et al. Nanoscale Pore Structure Characterization and Permeability of Mudrocks and Fine-Grained Sandstones in Coal Reservoirs by Scanning Electron Microscopy, Mercury Intrusion Porosimetry, and Low-Field Nuclear Magnetic Resonance. Geofluids, 2018, 2018: 1. doi:10.1155/2018/2905141
38.
Chun Liu, Daniela Lewin Mejia, Benjamin Chiang, et al. Hybrid collagen alginate hydrogel as a platform for 3D tumor spheroid invasion. Acta Biomaterialia, 2018, 75: 213. doi:10.1016/j.actbio.2018.06.003
39.
Qinhong Hu, Wen Zhou, Paul Huggins, et al. Pore Structure and Fluid Uptake of the Springer/Goddard Shale Formation in Southeastern Oklahoma, USA. Geofluids, 2018, 2018: 1. doi:10.1155/2018/5381735
40.
Md Golam Kibria, Qinhong Hu, Hong Liu, et al. Pore structure, wettability, and spontaneous imbibition of Woodford Shale, Permian Basin, West Texas. Marine and Petroleum Geology, 2018, 91: 735. doi:10.1016/j.marpetgeo.2018.02.001
41.
Cheng Feng, Yujiang Shi, Jiahong Li, et al. A new empirical method for constructing capillary pressure curves from conventional logs in low-permeability sandstones. Journal of Earth Science, 2017, 28(3): 516. doi:10.1007/s12583-016-0913-6
42.
Rui Yang, Fang Hao, Sheng He, et al. Experimental investigations on the geometry and connectivity of pore space in organic-rich Wufeng and Longmaxi shales. Marine and Petroleum Geology, 2017, 84: 225. doi:10.1016/j.marpetgeo.2017.03.033
43.
JIANCHAO CAI, WEI WEI, XIANGYUN HU, et al. FRACTAL CHARACTERIZATION OF DYNAMIC FRACTURE NETWORK EXTENSION IN POROUS MEDIA. Fractals, 2017, 25(02): 1750023. doi:10.1142/S0218348X17500232
44.
Rui Yang, Sheng He, Qinhong Hu, et al. Applying SANS technique to characterize nano-scale pore structure of Longmaxi shale, Sichuan Basin (China). Fuel, 2017, 197: 91. doi:10.1016/j.fuel.2017.02.005
45.
Mengdi Sun, Bingsong Yu, Qinhong Hu, et al. Pore connectivity and tracer migration of typical shales in south China. Fuel, 2017, 203: 32. doi:10.1016/j.fuel.2017.04.086
46.
Jianchao Cai, Wei Wei, Xiangyun Hu, et al. Electrical conductivity models in saturated porous media: A review. Earth-Science Reviews, 2017, 171: 419. doi:10.1016/j.earscirev.2017.06.013
47.
Yide Geng, Weiguo Liang, Jian Liu, et al. Evolution of Pore and Fracture Structure of Oil Shale under High Temperature and High Pressure. Energy & Fuels, 2017, 31(10): 10404. doi:10.1021/acs.energyfuels.7b01071
48.
Yiwen Gong, Ilham El-Monier. Quantification of Fracture Roughness and its Effects on the Grain and Pore Size Distribution of the Fractured Rock Using Image Analysis Technique. Day 4 Thu, November 15, 2018, doi:10.2118/193134-MS
49.
Yibo Zhao. Data Processing Techniques and Applications for Cyber-Physical Systems (DPTA 2019). Advances in Intelligent Systems and Computing, doi:10.1007/978-981-15-1468-5_92
Na Zhang, Manchao He, Bo Zhang, Fengchao Qiao, Hailong Sheng, Qinhong Hu. Pore Structure Characteristics and Permeability of Deep Sedimentary Rocks Determined by Mercury Intrusion Porosimetry. Journal of Earth Science, 2016, 27(4): 670-676. doi: 10.1007/s12583-016-0662-z
Na Zhang, Manchao He, Bo Zhang, Fengchao Qiao, Hailong Sheng, Qinhong Hu. Pore Structure Characteristics and Permeability of Deep Sedimentary Rocks Determined by Mercury Intrusion Porosimetry. Journal of Earth Science, 2016, 27(4): 670-676. doi: 10.1007/s12583-016-0662-z
Table
2.
Pore distribution frequency (averages±standard deviation, %) at ten different intervals of pore-throat diameter
Pore-throat diameter (μm)
Mudstone
Sandy mudstone
Siltstone
Fine sandstone
Medium sandstone
Coarse sandstone
Conglomerate
500-1 100
N/Aa
N/A
11.1±5.1
12.6±7.1
16.7±3.3
19.0±10.3
10.7±5.9
100-500
N/A
N/A
12.4±7.2
12.2±6.0
15.9±4.6
14.6±6.4
14.9±5.2
36-100
N/A
N/A
1.2±0.6
1.9±0.6
2.0±0.4
2.0±0.2
1.9±0.5
10-36
19.5±5.7
5.8±2.1
1.0±0.4
1.4±0.5
1.5±0.3
1.7±0.4
2.1±0.6
1-10
8.6±6.4
5.2±4.9
0.5±0.3
1.0±0.3
3.6±5.4
2.0±0.9
5.9±2.9
0.1-1
8.3±12.3
5.6±4.2
1.2±0.7
6.3±2.4
4.9±2.9
7.2±4.3
15.5±5.4
0.05-0.1
3.5±4.6
2.7±2.0
2.6±2.4
3.4±1.3
3.3±1.5
5.0±1.1
12.7±4.6
0.01-0.05
16.1±11.5
43.6±14.1
66.2±9.3
40.2±10.8
31.1±12.1
37.1±8.0
39.8±8.2
0.005-0.01
22.2±11.6
32.9±13.4
8.0±3.6
18.0±2.4
17.2±4.7
9.8±3.6
3.5±1.5
0.003-0.005
21.8±15.1
4.2±5.0
1.3±1.5
2.9±1.7
3.7±1.2
1.5±1.0
0.3±0.3
a N/A. Pore size ranges are not applicable to mudstone and sandy mudstoneone and sandmudstone due to different minimum intrusion pressures chosen for their MIP measurements according to their high tightness.
Figure 1. The important role of the Qinling Mountains. (a) The important location of the QM; (b) the important contribution of the QM to the Yangtze and Yellow Rivers; (c) the proportion of resources in the QM to China. Notes: Except for the QM where carbon sequestration and oxygen release are calculated based on the proportion of total amount, the rest are calculated based on the proportion of species.
Figure 2. Five major challenges faced by the Qinling Mountains.
DownLoad:
DownLoad:
DownLoad:
