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Petrophysical characterization of shale reservoir based on nuclear magnetic resonance (NMR) experiment: A case study of Lower Cambrian Qiongzhusi Formation in eastern Yunnan Province, South China
Journal of Natural Gas Science and Engineering 37 (2017) 29e38
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Petrophysical characterization of shale reservoir based on nuclear magnetic resonance (NMR) experiment: A case study of Lower Cambrian Qiongzhusi Formation in eastern Yunnan Province, South China Ang Li a, b, c, Wenlong Ding a, b, c, *, Ruyue Wang a, b, c, Jianhua He a, b, c, Xinghua Wang a, b, c, Yaxiong Sun a, b, c, Yang Gu a, b, c, Nailin Jiao a, b, c a
School of Energy Resources, China University of Geosciences (Beijing), Beijing 100083, China Key Laboratory for Marine Reservoir Evolution and Hydrocarbon Abundance Mechanism, Ministry of Education, China University of Geosciences (Beijing), Beijing 100083, China c Key Laboratory for Shale Gas Exploration and Assessment, Ministry of Land and Resources, China University of Geosciences (Beijing), Beijing 100083, China b
a r t i c l e i n f o
a b s t r a c t
Article history: Received 17 May 2016 Received in revised form 13 November 2016 Accepted 15 November 2016 Available online 16 November 2016
In order to characterize the petrophysical properties of shale using NMR technique, eight shale samples from the Lower Cambrian Qiongzhusi Formation in the eastern Yunnan province were measured by porosity and permeability tests, field emission scanning electron microscopy (FE-SEM) and NMR experiment. Pore types were obtained from the shape and distribution of transverse relaxation time (T2) spectrum. Residual porosity and movable porosity could be well estimated based on T2 spectrum area fraction. On the basis of Coates model, we proposed a regional Coates model to calculate the NMR permeability of shale. A method for determining T2cutoff of shale samples was also expounded. Moreover, the specific surface area distributions and pore size distributions could be obtained based on the mathematical equation of T2. Results show that T2 spectrums of shale samples at water-saturated condition can be divided into unimodal and bimodal T2 spectrums. Continuous bimodal T2 spectrums reflect the samples with good connectivity between small pores and large pores, whereas discontinuous bimodal T2 spectrums reflect that the connectivity between small pores and large pores is poor. Shale samples with higher bound water content have a greater T2cutoff. The NMR permeability is close to gas log permeability, which proves the applicability of regional Coates model. In eight shale samples, transition pores account for the largest proportion, followed by mesopores, indicating that transition pores and mesopores are the major sites for the accumulation of shale gas. © 2016 Elsevier B.V. All rights reserved.
Keywords: Shale reservoir Nuclear magnetic resonance (NMR) Petrophysical characterization T2 spectrums Pore characteristics
1. Introduction The large-scale commercial development of shale gas in North America has changed significantly the pattern of world oil and gas supply. A growing number of countries around the world have strengthened the exploration and development of this new energy (Bowker, 2007; Clarkson et al., 2012; Tang et al., 2014; Ding et al., 2015; Li et al., 2016). Shale as a reservoir of natural gas is characterized by low porosity, low permeability and strong heterogeneity.
* Corresponding author. School of Energy Resources, China University of Geosciences (Beijing), Beijing 100083, China. E-mail address: [email protected] (W. Ding). http://dx.doi.org/10.1016/j.jngse.2016.11.034 1875-5100/© 2016 Elsevier B.V. All rights reserved.
Reservoir quality has a significant influence on the accumulation of shale gas. As an important index for evaluating reservoir quality, shale pore structure has been widely studied by lots of scholars. Previous studies have indicated that pores in shale can be divided into four types on the basis of pore size: macropores (pore diameter > 1000 nm), mesopores (1000 nm < pore diameter > 100 nm), transition pores (100 nm > pore diameter > 10 nm), micropores (10 nm > pore diameter) (Zhong, 2012; Curtis, 2002; Caldwell, 2006). Shale gas existing in organicrich shale is primarily in two states of free gas and adsorbed gas. Free gas is preserved in natural fractures and intergranular pores, and adsorbed gas is stored in the organic matter and clay particle surfaces (Curtis, 2002). There are a mass of nano-sized and micron-
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sized pores in shale reservoir which has complicated pore structure, and some qualitative and quantitative techniques have been used to characterize shale pores. For example, nano-CT imaging and field emission scanning electron microscopy (FE-SEM) have been utilized qualitatively to characterize the structure and morphology of shale pores (Heath et al., 2011; Curtis et al., 2012; Tiwari et al., 2013). Low-pressure nitrogen adsorption, small-angle neutron scattering, high-pressure mercury intrusion and nuclear magnetic resonance have proven to be effective methods to obtain the quantitative parameters such as surface area, pore volume and pore-size distribution (Ross and Bustin, 2007, 2009; Chalmers and Bustin, 2007; Clarkson et al., 2013; Li et al., 2016). However, some of these methods have certain limitations. For example, N2 adsorption can not finely characterize parts of macropores and microfractures within shale. High-pressure mercury intrusion experiment is generally used to analyze mesopores and macropores. Besides, mercury intrusion may result in the damage of shale pore structure, thus affecting the reliability of measurement result. FE-SEM can only be used to observe the local pore characteristics of shale samples, but it cannot reflect the spatial distribution of pores and microfractures. Moreover, the primary structure of shale samples would be destroyed and a lot of pores and microfractures are produced artificially in the polishing process of rock section, which leads to great errors for the results. However, NMR has advantages in studying shale reservoirs with the characteristics of rapidity, undamage and accuracy. At present, a lot of scholars have extensively applied NMR technique to the evaluation of carbonate and sandstone reservoirs, but NMR experiments have not been used fully in the study of shale reservoirs (Yao et al., 2010a; Yu, 2013; Xu et al., 2015; Tan et al., 2015). When samples containing fluid are in a uniform static magnetic field, the hydrogen protons in fluid are polarized to generate a magnetic vector. At this time, hydrogen protons are stimulated by frequency pulse to produce nuclear magnetic resonance phenomenon. After removing frequency pulse, we can obtain a signal whose amplitude attenuates with time. Two parameters can be used to measure the attenuation rate of nuclear magnetic signal: longitudinal relaxation time (T1) and transverse relaxation time (T2). Generally, transverse relaxation time spectrum is utilized to study sample characteristics because the measurement of transverse relaxation time is fast. The major goals of this paper are to investigate the reservoir characteristics of the Lower Cambrian Qiongzhusi shale from the eastern Yunnan Province using NMR experiment. NMR T2 response characteristics, pore types, porosity, permeability and pore structure parameters were analyzed based on NMR transverse relaxation time distributions. Then we compared the difference between NMR permeability and routine permeability. Meanwhile, we calculated the NMR T2 cutoff value (T2cutoff) according to the T2 spectrums before and after centrifugation. The results could be helpful for the application of NMR technique in the study of shale reservoirs and provide a new way to characterize quantitatively the complicated pore structure of unconventional oil and gas reservoirs. 2. Materials and methods 2.1. Samples Eight shale core samples from the Lower Cambrian Qiongzhusi Formation were collected in eastern Yunnan Province, located in the southwestern margin of the Yangtze plate (Fig. 1). The TOC content of shale samples ranges from 1.42% to 2.51% with an average of 2.09%. All shale samples are over-mature with the vitrinite reflectance Ro ranging from 2.16% to 3.32%. Mineralogically,
quartz and clay minerals are the dominant minerals in shale samples. Quartz content is between 31.3% and 42.7%, with an average of 34.41%. Clay minerals content is in the range of 25.3%e36.4%, with a mean value of 31.44%. Lithologically, shale samples are dominated by silty shale based on the ternary diagram of mineral compositions (Fig. 2a). Moreover, the Lower Cambrian Qiongzhusi Formation was deposited in shallow-marine shelf, and quartz originated from terrigenous clast rather than siliceous organism. Therefore, TOC content has no apparent relationship with quartz content. Clay minerals mainly contain illite (average of 49.13%), illite/smectite mixed layer (average of 23%), chlorite (average of 20.5%) and a small amount of kaolinite (average of 7.38%). The parameters of eight samples are shown in Table 1. Several horizontal cylindrical core plugs with a diameter of 2.5 cm were prepared parallel to the bedding planes for each sample. 2.2. Porosity and permeability measurements The porosity and permeability of shale samples were analyzed by SGS Unconventional Petroleum Technical Testing Co., Ltd. following the Chinese Oil and Gas Industry Standard SY/T53362006 “Method of core routine analysis”. A KXD-II porometer was used to measure the porosity of samples with a helium expansion method, and permeability measurements were carried out using dry nitrogen as the medium with an instrument of permeameter (QT-2). Porosity and permeability experiments utilized a pressure of 96.8 kPa and a temperature of 25 C. 2.3. NMR core analyses After porosity and permeability measurements, all eight samples were dried in the drying oven for 24 h and were vacuumed till the weight of samples no longer changed. Subsequently, samples were saturated in the 8% KCl solution and weighed at regular intervals. Shale samples were fully saturated when their weight was no longer increased. NMR analyses were performed by SGS Unconventional Petroleum Technical Testing Co., Ltd. using a RecCore2500 instrument with a resonance frequency of 2.38 MHz and a magnetic field strength of 1200 G, and we obtained the T2 spectrums of eight samples at water-saturated condition. The samples were centrifuged at a centrifuge pressure of 300Psi to reach a perfect irreducible water state, and then NMR experiments were conducted again to obtain the T2 spectrums of all samples at irreducible water condition. The measurement parameters used in NMR experiments were as follows: echo spacing, 0.2 ms; waiting time, 6s; numbers of scans, 64; echo numbers, 1024; experiment temperature, 25 C and humidity, 55%. 2.4. FE-SEM observation The FE-SEM imaging of shale samples was performed using the Quanta 200F field emission scanning electron microscopy at the China University of Petroleum (Beijing). Before FE-SEM observation, one surface of each sample was polished using an argon-ion crosssection polisher “TechnoorgSC-100”. After polishing, the polishing surface was coated with gold film at a thickness of 10 nm to enhance the conductivity. Back-scattering electron model was chosen to observe the micromorphology of shale sample at various magnification scales. 3. Results 3.1. Porosity and permeability of shale The porosity and permeability of eight shale samples are
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Fig. 1. Sampling site (a) and stratigraphic column of Qiongzhusi Formation in eastern Yunnan Province (b).
Fig. 2. Ternary diagrams of the mineral compositions of Qiongzhusi shale. (a): Relationship between TOC and mineral compositions; (b): Relationship between porosity and mineral compositions.
presented in Table 1. The shale is characterized by tight lithology and complicated pore structure, so the porosity and permeability are significantly low. Additionally, porosity is affected by various geological factors (such as mineralogy, sedimentation, diagenesis and tectonism), which may result in the inapparent relationship
between porosity and mineral compositions (Fig. 2b). Helium porosity of samples ranges from 1.40% to 3.90% with a mean value of 3.17%. The permeability varies from 0.0023 to 0.0061mD with an average of 0.0042mD. Fig. 3 shows that permeability has no apparent relationship with porosity (R2 ¼ 0.0302).
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Table 1 Characteristic parameters of the shale samples from Qiongzhusi Formation in eastern Yunnan Province. Sample TOC (wt.%)
Ro (%)
Quartz (wt.%)
Clay (wt.%)
Potash Feldspar (wt.%)
Plagioclase (wt.%)
Calcite (wt.%)
Dolomite (wt.%)
Pyrite (wt.%)
I/S It C Kao (wt.%) (wt.%) (wt.%) (wt.%)
Porosity Permeability (%) (mD)
YN-1 YN-2 YN-3 YN-4 YN-5 YN-6 YN-7 YN-8
3.30 2.26 2.23 2.16 2.45 2.31 3.32 2.44
35.7 32.2 31.8 31.7 35.0 31.3 34.9 42.7
25.3 33.7 33.1 35.5 27.6 32.6 27.3 36.4
4.0 4.6 4.3 5.1 5.5 5.0 5.7 /
24.3 22.3 21.7 22.7 24.4 23.8 21.1 4.2
3.8 3.1 5.4 / 4.5 3.4 6.5 /
/ / / / / / / 13.4
6.9 4.1 3.7 5.0 3.0 3.9 4.5 3.3
24 23 12 32 20 28 28 17
3.76 2.78 3.15 2.96 1.40 3.61 3.90 3.80
2.23 2.25 1.78 1.42 2.22 1.86 2.42 2.51
49 54 55 53 52 40 38 52
20 19 29 9 18 24 26 19
7 4 4 6 10 8 8 12
0.0050 0.0023 0.0031 0.0039 0.0044 0.0061 0.0036 0.0048
lines in Fig. 5). T2 spectrums of shale cores are divided into two types: unimodal and bimodal T2 spectrums. Unimodal T2 spectrum (YN-2, YN-3, etc.) has an isolated peak in a short relaxation time which normally ranges from 0.01 ms to 10 ms. Bimodal T2 spectrum has two peaks, and the amplitude of right peak ranging from 10 ms to 100 ms is obviously smaller than that of left peak ranging from 0.01 ms to 10 ms. It suggests that not only small pores but also some relatively large pores or fractures are well-developed in shale. In addition, we divide the bimodal T2 spectrum into continuous bimodal T2 spectrum (YN-6) and discontinuous bimodal T2 spectrum (YN-1 and YN-8) based on the shape of T2 spectrum. The former indicates that the samples have good connectivity between small pores and large pores (or fractures). While the latter indicates that pores in shale are relatively closed, and the connectivity between small pores and large pores is poor. Fig. 3. Relationship between porosity and permeability of shale samples.
3.2. NMR T2 distributions of shale T2 distributions can be obtained by the mathematical inversion of T2 decay curves, namely, raw NMR signals (Fig. 4). According to the basic principle of NMR, there is a positive correlation between the transverse relaxation time of hydrogen protons in the pore water and pore diameter (Sun et al., 2012; Huang et al., 2015). Namely, the T2 distributions reflect the pore size distributions: small pores have short relaxation time and large pores or microfractures have long relaxation time (Yao et al., 2010a,b; Tan et al., 2015). The amplitude of T2 spectrum reflects the proportion of pores with different diameters in shale. Therefore, shale with stronger T2 spectrum amplitude at the long relaxation time would have more large pores and better reservoir quality. 3.2.1. NMR T2 spectrum at water-saturated condition The NMR measurements for 8 shale samples were performed to obtain the T2 spectrums at water-saturated condition (yellow solid
3.2.2. NMR T2 spectrum at irreducible water condition NMR measurements were performed again after centrifugation and the T2 spectrums (red dotted line) at irreducible water condition are illustrated in Fig. 5. For some samples (e.g., YN-1 and YN-2), the peaks at 0.01e10 ms have no apparent changes, but those of some samples reduce (e.g., YN-5 and YN-6). The right peak of bimodal T2 spectrum reduces and even disappears after centrifugation. The reason for this case is that fluid in some small closed pores can not flow out freely by centrifugation, so the signal strength of hydrogen protons and the shape of T2 spectrum do not change significantly. Whereas fluid in some large pores with appropriate connectivity can flow out freely by centrifugation, which results in the signal strength decaying and the amplitude of T2 spectrum reducing. The right peak of YN-6 disappears after centrifugation, indicating that microfractures could be welldeveloped in shale sample, benefiting fluid migration. 4. Discussion In this section, the characteristics of the transverse relaxation
Fig. 4. T2 decay curves of shale samples by NMR measurements.
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Fig. 5. NMR T2 spectrums of shale samples.
time distributions of NMR technique are used to evaluate the reservoir properties of shale. T2 distribution characteristics include the number, area, shape, and position of T2 spectrum peaks, which can be used to analyze pore types, residual porosity, movable porosity, reservoir permeability and pore structure parameters of
the shale. Moreover, we also calculate the NMR T2 cutoff value. 4.1. Pore types T2 spectrum and pore size distribution have the similar trend in
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NMR measurements, where each transverse relaxation time represents a pore diameter: long transverse relaxation time represents large pores and short transverse relaxation time represents small pores in shale (Kleinberg et al., 1993; Dunn et al., 2002; Yao et al., 2010a,b). The minimal pore radius allowed fluid to flow out under the action of centrifugal force can be calculated based on the capillary pressure equation:
r¼
2scos q P
(1)
where r is the minimal pore radius, which allows water to flow out at the pressure P, mm; P is the centrifuging pressure, MPa; s is the interfacial tension of shale and water; and q is the contact angle between pore surface and water molecule. Although different shale samples have different contact angles q, in order to simplify the analysis, we use the contact angle between water molecule and pore surface measured by Yakov (2001) in this study, about 73 . At room temperature, the interfacial tension of water is 0.072 N/m. Therefore, the minimal pore radius for water to discharge is 20.33 nm under the action of centrifugal pressure of 2.07 MPa, that is to say, the irreducible water includes not only the clay bound water but also the water remained in the pores and throats of radius less than 20.33 nm. The peaks of eight shale samples distributed from 0.01 ms to 10 ms have small changes after centrifugation except sample YN-5, suggesting that this kind of peak is associated to the small pores which have large capillary pressure to prevent the discharge of fluid. Whereas the peaks in the range of 10e100 ms decrease or disappear by centrifuging, indicating that the increase of pore diameter weakens the bound effect of capillary pressure on fluid. As shown in Fig. 5, whether unimodal or bimodal T2 spectrum, pores in shale samples are mainly adsorption pores, and seepage pores with bimodal T2 spectrum are more developed than those in unimodal shale. Through SEM observation, microfractures are relatively well-developed in bimodal shale (Fig. 6). Microfractures in YN-6 and YN-8 increase the connectivity among pores and improve the permeability of shale. Especially YN-6, microfractures connect small pores and large pores, which contribute to the seepage of fluid. In contrast, for YN-1 with discontinuous bimodal T2 spectrum, although there are a certain number of macropores or microfractures in shale samples, poor connectivity makes the fluid difficult to flow out. However, for YN-5, shale has good connectivity among small pores, so the fluid in seepage pores can be expelled by centrifuging. Given the above, shale with high porosity may not have high permeability, and shale with low porosity may have good permeability. It indicates that porosity has no apparent relationship with permeability in eight groups of shale samples, which is just in accordance with Fig. 3. 4.2. Residual porosity and movable porosity Previous studies have indicated that the estimation of porosity can be obtained accurately regardless of the mineralogy based on NMR measurements because of the relaxation signal from the hydrogen-containing fluid rather than the matrix in rock (Straley et al., 1997; Coates et al., 1999). The signal strength of T2 spectrums at water-saturated condition is converted into NMR porosity 4N of shale, and the results are presented in Table 2. Yao et al. (2010b) have proved that the NMR porosity represents the proportion of pore volume occupied by bound water and free water, so NMR porosity includes residual porosity 4R, corresponding to the proportion of bound water and movable porosity 4M, corresponding to the proportion of free water. The 4R and 4M can be calculated by the following two equations:
4R ¼ 4N BVIðBVI þ FFIÞ
(2)
4M ¼ 4N FFIðBVI þ FFIÞ
(3)
where BVI is the bound fluid index obtained by the T2 spectrum area at irreducible water condition; FFI is the free fluid index; BVI þ FFI represents the sum of bound fluid and free fluid that can be determined by the T2 spectrum area at water-saturated condition. As illustrated in Fig. 5, BVI and FFI are presented with the lightgray area in T2 spectrum at irreducible water condition and the dark-gray area in T2 spectrum at water-saturated condition, respectively. Residual porosity and movable porosity calculated by Eq. (2) and Eq. (3) are shown in Table 2. Residual porosity is between 1.81% and 3.21% with a mean value of 2.58%. Movable porosity ranging from 0.40% to 2.07% with an average of 0.90% is much lower than residual porosity, and the movable porosities of all samples except YN-6 and YN-8 are less than 1%. The proportions of BVI and FFI are obvious in Fig. 5, BVI corresponding to light-gray area in the irreducible water spectrum accounts for a larger proportion, while FFI corresponding to dark-gray area in the 100% water spectrum occupies a small area. It suggests that adsorption pores are dominated in shale reservoir of study area, and the development of adsorption pores is unfavorable for seepage.
4.3. T2 cutoff values NMR T2 cutoff value (T2cutoff) is a relaxation time boundary that divides bound fluid and free fluid in the T2 spectrum at watersaturated condition. The part on the left of T2cutoff in the T2 spectrum represents the bound fluid in the adsorption pores, while the part on the right of T2cutoff in T2 spectrum corresponds to the free fluid in the seepage pores. Some scholars have conducted a large number of tests to obtain the T2cutoff of sandstones and carbonates. Schlumberger recommended that the T2cutoff of mid-high permeability sandstone reservoir and carbonate reservoir approximated 33 ms and 92 ms, respectively (Xiao, 1998; Sun et al., 2012; Ding et al., 2012). The results of Wang et al. (2001) demonstrated that the T2cutoff of low-permeability sandstone reservoir had a wide range of distribution with a mean value of 12.85 ms, which is only about one-third of that of mid-high permeability sandstone reservoir. Accurate calculation of T2cutoff helps to apply NMR technique in the evaluation of shale reservoir. The method for determining T2cutoff of shale samples is shown in Fig. 7. First, two T2 spectrums at water-saturated condition and irreducible water condition respectively are converted into two accumulative T2 spectrums. Maximum cumulative amplitudes of T2 spectrums at water-saturated condition and irreducible water condition are calibrated as total porosity and residual porosity respectively, so the difference between total porosity and residual porosity means movable porosity, which corresponds to the segment “H” in Fig. 7. Second, a horizontal line through residual porosity is drawn, and this line and accumulative T2 spectrum at water-saturated condition intersect at one point “A”. Third, a vertical line is made through the point “A”, and the T2 value at the intersection projected on the time axis is the T2cutoff. The T2cutoff of samples calculated by the above method is shown in Table 2, and T2cutoff is in the range of 0.54e4.28 ms with an average of 1.45 ms. The T2cutoff of YN-1 is significantly greater than that of other samples, and the T2cutoff of other seven samples is all less than 4 ms. As shown in Fig. 7, the higher the content of bound water in shale is, the greater the T2cutoff is, namely, shale with higher bound water content has a greater T2cutoff.
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Fig. 6. FE-SEM images of microfractures in shale samples. Netted microfractures and continuous microfractures increase the connectivity among pores, thus improving the permeability of shale (aee). However, some small microfractures with poor extensibility cannot connect the pores, which are unfavorable for seepage (f). a and b: sample YN-6; c and d: sample YN-8; e and f: sample YN-1.
Table 2 NMR porosity, residual porosity, movable porosity, NMR permeability and T2cutoff of shale samples. Sample
4N (%)
BVI(BVI þ FFI)
FFI(BVI þ FFI)
4R (%)
4M (%)
T2cutoff (ms)
kNMR (mD)
YN-1 YN-2 YN-3 YN-4 YN-5 YN-6 YN-7 YN-8
3.96 3.11 3.44 2.77 2.59 4.23 3.42 4.35
0.81 0.87 0.87 0.68 0.70 0.51 0.79 0.73
0.19 0.13 0.13 0.32 0.30 0.49 0.21 0.27
3.21 2.71 2.99 1.88 1.81 2.16 2.70 3.18
0.75 0.40 0.45 0.89 0.78 2.07 0.72 1.17
4.28 1.13 1.10 0.54 0.83 0.73 1.04 1.93
0.0042 0.0014 0.0021 0.0035 0.0024 0.0070 0.0039 0.0045
4.4. Permeability Shale as a tight reservoir of natural gas is characterized by low permeability. Estimating shale permeability is another important function of NMR core experiment (Shao et al., 2009; Ding et al., 2014). Based on the bound fluid volume, free fluid volume and NMR porosity, constructing a model among these parameters provides a good method to calculate the permeability of shale. A new regional model is built on the basis of Coates permeability model in this study. The parameters in this new model vary with different regions, so the results are more reasonable. On the basis of Timur formula, Coates established the Coates permeability model through a large number of experiments in 1991 (Arnold et al., 2006; Tian, 2010). Coates model can be described as follows: Fig. 7. T2 cutoff value calculation for sample YN-6 with the NMR measurements at water-saturated condition (Sw) and irreducible water condition (Sir).
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kNMR ¼
4 4 FFI 2 N
C
(4)
BVI
kNMR is the permeability estimated by NMR experiment, C is a constant. Coates model is a basic model for calculating permeability using NMR. In order to make this model suitable for different regions, it is changed in the form of “y ¼ mx þ b”. Regional Coates model is as follows:
FFI BVI
0:5
0:25 k ¼ m NMR 4N
! þb
(5)
m and b are the slope and intercept of straight-line respectively, which are determined by fitting analysis. (FFI/BVI)0.5 and (k0.25 NMR/4N) are regarded as the “x” and “y” of linear equation respectively to carry on linear fitting. (FFI/BVI)0.5 has a strongly linear correlation with (k0.25 NMR/4N), with a correlation coefficient of 0.883. Therefore, the Coates model of study area is:
FFI BVI
0:5
0:25 k ¼ 11:62 NMR 4N
! 0:336
(6)
The NMR permeability of shale samples calculated by Eq. (6) is in the range of 0.0014e0.0070mD with an average of 0.0036mD (Table 2). Fig. 8 is the cross-plot of NMR permeability and gas log permeability of samples. All sample points except YN-5 are close to the bisector, indicating that the regional Coates model is effective for permeability calculation and NMR permeability satisfies the demands of research and production. 4.5. Pore structure The transverse relaxation characteristics of fluid in porous media can be described by the following mathematical equation:
1 1 1 1 ¼ þ þ T2 T2B T2D T2S
pore surface and hydrogen-containing fluid (e.g. water and kerosene), so total relaxation time T2 is almost determined by the surface relaxation time T2S (Coates et al., 1999; Wang, 2009; Sun et al., 2012). When the bulk relaxation and diffusion relaxation of pore fluid are not considered, Eq. (7) can be simplified as:
1 S ¼ r2 T2 V
(8)
where r2 is the transverse surface relaxivity of rock, mm/ms. The r2 of shale as a constant is about 0.05 mm/ms based on the work of Sondergeld et al. (2012); S and V are the surface area and volume of rock pores, respectively, mm2 and mm3. S/V is the specific surface area that relates to the pore size, smaller pores have greater S/V values and shorter T2. On the contrary, the more large pores in the rock, the lower the value of S/V is, and the longer the T2 is. Hence, the interaction between water molecules and pore surface can be characterized by T2 relaxation of water-saturated samples. For simplification purpose, the pore geometry of shale is regarded as cylinder because of the connectivity among shale pores, so S/V equals 2/r. Eq. (8) can be further transformed into the relationship between T2 and shale pore size.
r ¼ 0:1T2
(9)
Therefore, the specific surface area distributions of samples can be obtained according to Eq. (8). The specific surface area distribution of YN-1 is shown in Fig. 9. The specific surface area ranges from 0.34 mm1 to 433.33 mm1 with an average of 48.31 mm1 and is mainly distributed in the range of 0.5e1.79 mm1 and 8.3e93.72 mm1. Moreover, the T2 distributions of eight samples are converted into the pore size distributions based on Eq. (9) (Fig. 10). In eight shale samples, transition pores account for the largest proportion, followed by mesopores, indicating that transition pores and mesopores are the major sites for the accumulation of shale gas. Furthermore, the well-developed macropores and microfractures in shale samples with bimodal T2 spectrums also provide more reservoir space for shale gas.
(7)
where 1/T2 is the total relaxation rate of pore fluid, ms1. T2B, T2D, and T2S are bulk relaxation time, diffusion relaxation time and surface relaxation time, respectively. In low-field NMR experiment, the applied magnetic field from NMR instrument is uniform, so the diffusion relaxation rate 1/T2D approaches zero. Moreover, the bulk relaxation time of pore fluid T2B is so long that it contributes little to total relaxation rate. Porous media are generally characterized by complicated surface structure with strong interaction between
Fig. 8. The cross-plot of NMR permeability and gas log permeability of samples.
5. Conclusions In this paper, the petrophysical properties of 8 shale samples from the Lower Cambrian Qiongzhusi Formation in eastern Yunnan Province are investigated using NMR experiments. Additionally, the method for determining T2cutoff of shale has also been discussed based on the physical significance of T2cutoff. The following conclusions can be made:
Fig. 9. The specific surface area distribution of sample YN-1 based on NMR measurements.
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Fig. 10. Pore size distributions of eight shale samples by NMR measurements.
(1) T2 spectrums of shale samples at water-saturated condition can be divided into two types: unimodal and bimodal T2 spectrums. Whether unimodal or bimodal T2 spectrum, pores in shale samples are mainly adsorption pores, and seepage pores in bimodal shale are more developed than those in unimodal shale. Continuous bimodal T2 spectrum reflects the samples with good connectivity between small pores and large pores. (2) According to the T2 spectrum area fraction, residual porosity and movable porosity can be well estimated. The results show that movable porosity is much lower than residual porosity. Adsorption pores are dominated in shale reservoir of study area, and the development of adsorption pores is unfavorable for seepage.
(3) The method for determining T2cutoff of shale has been discussed based on the physical significance of T2cutoff. The T2cutoff of shale samples is less than 4 ms except for YN-1. The higher the content of bound water in shale is, the greater the T2cutoff is. On the basis of Coates model, a regional Coates model is proposed to calculate the permeability of shale samples. The NMR permeability is close to gas log permeability, so the regional Coates model is effective for permeability calculation. (4) The specific surface area distribution and pore size distribution can be obtained based on the mathematical equation of T2. The specific surface area is mainly distributed in the range of 0.5e1.79 mm1 and 8.3e93.72 mm1. Transition pores and mesopores contribute the major sites for the
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accumulation of shale gas. Moreover, the well-developed macropores and microfractures in shale with bimodal T2 spectrum also provide more reservoir space for shale gas. Acknowledgements This research was supported by the National Natural Science Foundation of China (Project Nos. 41072098 and 41372139) and the Important National Science and Technology Specific Projects of China (Nos. 2016ZX05046-003, 2011ZX05018-001-002 and 2011ZX05009-002-205). The authors would like to thank the staff of all of the laboratories that cooperated in performing the tests and analyses. We are also grateful to the reviewers, whose comments improved the quality of this manuscript. References Arnold, J., Clauser, C., Pechnig, R., Anferova, S., Anferov, V., Blümich, B., 2006. Porosity and permeability from mobile NMR core-scanning. Petrophysics 47, 306e314. Bowker, K.A., 2007. Barnett shale gas production, Fort Worth Basin: issues and discussion. AAPG Bull. 91 (4), 523e533. Caldwell, R., 2006. Unconventional resources: are they for real. Scotia Newsl. 3, 1e2. Chalmers, G.R.L., Bustin, R.M., 2007. The organic matter distribution and methane capacity of the Lower Cretaceous strata of Northeastern British Columbia. Can. Int. J. Coal Geol. 70, 223e339. Clarkson, C.R., Jensen, J.L., Chipperfield, S., 2012. Unconventional gas reservoir evaluation: what do we have to consider? J. Nat. Gas. Sci. Eng. 8, 9e33. Clarkson, C.R., Solano, N., Bustin, R.M., Bustin, A.M.M., Chalmers, G.R.L., He, L., ski, A.P., Blach, T.P., 2013. Pore structure characterMelnichenko, Y.B., Radlin ization of North American shale gas reservoirs using USANS/SANS, gas adsorption, and mercury intrusion. Fuel 103, 606e616. Coates, G.R., Xiao, L.Z., Prammer, M.G., 1999. NMR Logging Principles and Applications. Gulf Publishing Company, Houston, Texas. Curtis, J.B., 2002. Fractured shale-gas system. AAPG Bull. 86 (11), 1921e1938. Curtis, M.E., Ambrose, R.J., Sondergeld, C.H., Rai, C.S., 2012. Microstructural investigation of gas shales in two and three dimensions using nanometer-scale resolution imaging. AAPG Bull. 96, 665e677. Ding, W.L., Dai, P., Zhu, D.W., Zhang, Y.Q., He, J.H., Li, A., Wang, R.Y., 2015. Fractures in continental shale reservoirs: a case study of the Upper Triassic strata in the SE Ordos Basin. Cent. China. Geol. Mag. 153 (4), 663e680. Ding, W.L., Fan, T.L., Yu, B.S., Huang, X.B., Liu, C., 2012. Ordovician carbonate reservoir fracture characteristics and fracture distribution forecasting in the Tazhong Area of Tarim Basin, Northwest China. J. Pet. Sci. Eng. 86e87, 62e70. Ding, Y.J., Guo, B.H., Yan, X.R., Li, J., Lu, Q., 2014. On identification shale reservoirs validity and physical parameters quantitative evaluation method. Well Logging Technol. 38 (3), 297e303. Dunn, K.J., Bergman, D.J., Latorraca, G.A., 2002. Petrophysical NMR measurements. Handb. Geophys Explor Seismol. Explor 32, 71e127. Heath, J.E., Dewers, T.A., McPherson, B.J., Petrusak, R., Chidsey, T.C., Rinehart, A.J., Mozley, P.S., 2011. Pore networks in continental and marine mudstones: characteristics and controls on sealing behavior. Geosphere 7 (2), 429e454. Huang, J.G., Xu, K.M., Guo, S.B., Guo, H.W., 2015. Comprehensive study on pore
structures of shale reservoirs based on SEM, NMR and X-CT. Geoscience 29 (1), 199e205. Kleinberg, R.L., Straley, C., Kenyon, W.E., Akkurt, R., Farooqui, S.A., 1993. Nuclear magnetic resonance of rocks: T1 vs. T2. SPE Pap. 26470, 555e563. Li, A., Ding, W.L., He, J.H., Dai, P., Yin, S., Xie, F., 2016. Investigation of pore structure and fractal characteristics of organic-rich shale reservoirs: a case study of Lower Cambrian Qiongzhusi formation in Malong block of eastern Yunnan Province, South China. Mar. Pet. Geol. 70, 46e57. Ross, D.J.K., Bustin, R.M., 2007. Shale gas potential of the lower Jurassic gordondale member, northeastern British Columbia, Canada. Bull. Can. Pet. Geol. 55 (1), 51e75. Ross, D.J.K., Bustin, R.M., 2009. The importance of shale composition and pore structure upon gas storage potential of shale gas reservoirs. Mar. Pet. Geol. 26, 916e927. Shao, W.Z., Ding, Y.J., Liu, Y., Liu, S.Q., Li, Y.Q., Zhao, J.H., 2009. The application of NMR log data in evaluation of reservoir pore structure. Well Logging Technol. 33 (1), 52e56. Sondergeld, C.H., Ambrose, R.J., Rai, C.S., Moncrieff, J., 2012. Micro-structure studies of gas shales: SPE Paper 131771 presented at the SPE Unconventional Gas Conference and Exhibition, Pittsburgh, Pennsylvania, February 23-25, 1e17. Straley, C., Rossini, D., Vinegar, H., Tutunjian, P., Morriss, C., 1997. Core analysis by low field NMR. Log. Anal. 38, 84e93. Sun, J.C., Chen, J.P., Yang, Z.M., Liu, X.W., Liu, Y.J., 2012. Experimental study of the NMR characteristics of shale reservoir rock. Sci. Technol. Rev. 30 (14), 25e30. Tan, M.J., Mao, K.Y., Song, X.D., Yang, X., Xu, J.J., 2015. NMR petrophysical interpretation method of gas shale based on core NMR experiment. J. Pet. Sci. Eng. 136, 100e111. Tang, X., Zhang, J.C., Wang, X.Z., Yu, B.S., Ding, W.L., Xiong, J.Y., Yang, Y.T., Wang, L., Yang, C., 2014. Shale characteristics in the Southeastern Ordos Basin, China: implications for hydrocarbon accumulation conditions and the potential of continental shales. Int. J. Coal Geol. 128e129, 32e46. Tian, Y., 2010. NMR logging permeability models and application. Inn. Mong. Petrochem. Ind. 10, 25e27. Tiwari, P., Deo, M., Lin, C.L., Miller, J.D., 2013. Characterization of oil shale pore structure before and after pyrolysis by using X-ray micro CT. Fuel 107, 547e554. Wang, S., 2009. Analysis of rock pore structural characteristic by nuclear magnetic resonance. Xinjiang Pet. Geol. 30 (6), 768e770. Wang, W.M., Ye, Z.H., Guo, H.K., 2001. Experimental studies of NMR properties of continental sedimentary rocks. Chin. J. Magn. Reson 18 (2), 113e121. Xiao, L.Z., 1998. NMR Imaging Logging and Rock NMR Properties and its Applications. Scientific Press, Beijing. Xu, H., Tang, D.Z., Zhao, J.L., Li, S., 2015. A precise measurement method for shale porosity with low-field nuclear magnetic resonance: a case study of the CarboniferousePermian strata in the Linxing area, eastern Ordos Basin, China. Fuel 143, 47e54. Yakov, V., 2001. A practical approach to obtain primary drainage capillary pressure curves from NMR core and log data. Petrophysics 42, 334e343. Yao, Y.B., Liu, D.M., Cai, Y.D., Li, J.Q., 2010a. Advanced characterization of pores and fractures in coals by nuclear magnetic resonance and X-ray computed tomography. Sci. China-Earth Sci. 53, 854e862. Yao, Y.B., Liu, D.M., Yao, C., Tang, D.Z., Tang, S.H., Huang, W.H., 2010b. Petrophysical characterization of coals by low-field nuclear magnetic resonance (NMR). Fuel 89, 1371e1380. Yu, B.S., 2013. Classification and characterization of gas shale pore system. Earth Sci. Front. 20 (4), 211e220. Zhong, T.X., 2012. Characteristics of pore structure of marine shales in South China. Nat. Gas. Ind. 32 (9), 1e4.
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Petrophysical characterization of shale reservoir based on nuclear magnetic resonance (NMR) experiment: A case study of Lower Cambrian Qiongzhusi Formation in eastern Yunnan Province, South China Ang Li a, b, c, Wenlong Ding a, b, c, *, Ruyue Wang a, b, c, Jianhua He a, b, c, Xinghua Wang a, b, c, Yaxiong Sun a, b, c, Yang Gu a, b, c, Nailin Jiao a, b, c a
School of Energy Resources, China University of Geosciences (Beijing), Beijing 100083, China Key Laboratory for Marine Reservoir Evolution and Hydrocarbon Abundance Mechanism, Ministry of Education, China University of Geosciences (Beijing), Beijing 100083, China c Key Laboratory for Shale Gas Exploration and Assessment, Ministry of Land and Resources, China University of Geosciences (Beijing), Beijing 100083, China b
a r t i c l e i n f o
a b s t r a c t
Article history: Received 17 May 2016 Received in revised form 13 November 2016 Accepted 15 November 2016 Available online 16 November 2016
In order to characterize the petrophysical properties of shale using NMR technique, eight shale samples from the Lower Cambrian Qiongzhusi Formation in the eastern Yunnan province were measured by porosity and permeability tests, field emission scanning electron microscopy (FE-SEM) and NMR experiment. Pore types were obtained from the shape and distribution of transverse relaxation time (T2) spectrum. Residual porosity and movable porosity could be well estimated based on T2 spectrum area fraction. On the basis of Coates model, we proposed a regional Coates model to calculate the NMR permeability of shale. A method for determining T2cutoff of shale samples was also expounded. Moreover, the specific surface area distributions and pore size distributions could be obtained based on the mathematical equation of T2. Results show that T2 spectrums of shale samples at water-saturated condition can be divided into unimodal and bimodal T2 spectrums. Continuous bimodal T2 spectrums reflect the samples with good connectivity between small pores and large pores, whereas discontinuous bimodal T2 spectrums reflect that the connectivity between small pores and large pores is poor. Shale samples with higher bound water content have a greater T2cutoff. The NMR permeability is close to gas log permeability, which proves the applicability of regional Coates model. In eight shale samples, transition pores account for the largest proportion, followed by mesopores, indicating that transition pores and mesopores are the major sites for the accumulation of shale gas. © 2016 Elsevier B.V. All rights reserved.
Keywords: Shale reservoir Nuclear magnetic resonance (NMR) Petrophysical characterization T2 spectrums Pore characteristics
1. Introduction The large-scale commercial development of shale gas in North America has changed significantly the pattern of world oil and gas supply. A growing number of countries around the world have strengthened the exploration and development of this new energy (Bowker, 2007; Clarkson et al., 2012; Tang et al., 2014; Ding et al., 2015; Li et al., 2016). Shale as a reservoir of natural gas is characterized by low porosity, low permeability and strong heterogeneity.
* Corresponding author. School of Energy Resources, China University of Geosciences (Beijing), Beijing 100083, China. E-mail address: [email protected] (W. Ding). http://dx.doi.org/10.1016/j.jngse.2016.11.034 1875-5100/© 2016 Elsevier B.V. All rights reserved.
Reservoir quality has a significant influence on the accumulation of shale gas. As an important index for evaluating reservoir quality, shale pore structure has been widely studied by lots of scholars. Previous studies have indicated that pores in shale can be divided into four types on the basis of pore size: macropores (pore diameter > 1000 nm), mesopores (1000 nm < pore diameter > 100 nm), transition pores (100 nm > pore diameter > 10 nm), micropores (10 nm > pore diameter) (Zhong, 2012; Curtis, 2002; Caldwell, 2006). Shale gas existing in organicrich shale is primarily in two states of free gas and adsorbed gas. Free gas is preserved in natural fractures and intergranular pores, and adsorbed gas is stored in the organic matter and clay particle surfaces (Curtis, 2002). There are a mass of nano-sized and micron-
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sized pores in shale reservoir which has complicated pore structure, and some qualitative and quantitative techniques have been used to characterize shale pores. For example, nano-CT imaging and field emission scanning electron microscopy (FE-SEM) have been utilized qualitatively to characterize the structure and morphology of shale pores (Heath et al., 2011; Curtis et al., 2012; Tiwari et al., 2013). Low-pressure nitrogen adsorption, small-angle neutron scattering, high-pressure mercury intrusion and nuclear magnetic resonance have proven to be effective methods to obtain the quantitative parameters such as surface area, pore volume and pore-size distribution (Ross and Bustin, 2007, 2009; Chalmers and Bustin, 2007; Clarkson et al., 2013; Li et al., 2016). However, some of these methods have certain limitations. For example, N2 adsorption can not finely characterize parts of macropores and microfractures within shale. High-pressure mercury intrusion experiment is generally used to analyze mesopores and macropores. Besides, mercury intrusion may result in the damage of shale pore structure, thus affecting the reliability of measurement result. FE-SEM can only be used to observe the local pore characteristics of shale samples, but it cannot reflect the spatial distribution of pores and microfractures. Moreover, the primary structure of shale samples would be destroyed and a lot of pores and microfractures are produced artificially in the polishing process of rock section, which leads to great errors for the results. However, NMR has advantages in studying shale reservoirs with the characteristics of rapidity, undamage and accuracy. At present, a lot of scholars have extensively applied NMR technique to the evaluation of carbonate and sandstone reservoirs, but NMR experiments have not been used fully in the study of shale reservoirs (Yao et al., 2010a; Yu, 2013; Xu et al., 2015; Tan et al., 2015). When samples containing fluid are in a uniform static magnetic field, the hydrogen protons in fluid are polarized to generate a magnetic vector. At this time, hydrogen protons are stimulated by frequency pulse to produce nuclear magnetic resonance phenomenon. After removing frequency pulse, we can obtain a signal whose amplitude attenuates with time. Two parameters can be used to measure the attenuation rate of nuclear magnetic signal: longitudinal relaxation time (T1) and transverse relaxation time (T2). Generally, transverse relaxation time spectrum is utilized to study sample characteristics because the measurement of transverse relaxation time is fast. The major goals of this paper are to investigate the reservoir characteristics of the Lower Cambrian Qiongzhusi shale from the eastern Yunnan Province using NMR experiment. NMR T2 response characteristics, pore types, porosity, permeability and pore structure parameters were analyzed based on NMR transverse relaxation time distributions. Then we compared the difference between NMR permeability and routine permeability. Meanwhile, we calculated the NMR T2 cutoff value (T2cutoff) according to the T2 spectrums before and after centrifugation. The results could be helpful for the application of NMR technique in the study of shale reservoirs and provide a new way to characterize quantitatively the complicated pore structure of unconventional oil and gas reservoirs. 2. Materials and methods 2.1. Samples Eight shale core samples from the Lower Cambrian Qiongzhusi Formation were collected in eastern Yunnan Province, located in the southwestern margin of the Yangtze plate (Fig. 1). The TOC content of shale samples ranges from 1.42% to 2.51% with an average of 2.09%. All shale samples are over-mature with the vitrinite reflectance Ro ranging from 2.16% to 3.32%. Mineralogically,
quartz and clay minerals are the dominant minerals in shale samples. Quartz content is between 31.3% and 42.7%, with an average of 34.41%. Clay minerals content is in the range of 25.3%e36.4%, with a mean value of 31.44%. Lithologically, shale samples are dominated by silty shale based on the ternary diagram of mineral compositions (Fig. 2a). Moreover, the Lower Cambrian Qiongzhusi Formation was deposited in shallow-marine shelf, and quartz originated from terrigenous clast rather than siliceous organism. Therefore, TOC content has no apparent relationship with quartz content. Clay minerals mainly contain illite (average of 49.13%), illite/smectite mixed layer (average of 23%), chlorite (average of 20.5%) and a small amount of kaolinite (average of 7.38%). The parameters of eight samples are shown in Table 1. Several horizontal cylindrical core plugs with a diameter of 2.5 cm were prepared parallel to the bedding planes for each sample. 2.2. Porosity and permeability measurements The porosity and permeability of shale samples were analyzed by SGS Unconventional Petroleum Technical Testing Co., Ltd. following the Chinese Oil and Gas Industry Standard SY/T53362006 “Method of core routine analysis”. A KXD-II porometer was used to measure the porosity of samples with a helium expansion method, and permeability measurements were carried out using dry nitrogen as the medium with an instrument of permeameter (QT-2). Porosity and permeability experiments utilized a pressure of 96.8 kPa and a temperature of 25 C. 2.3. NMR core analyses After porosity and permeability measurements, all eight samples were dried in the drying oven for 24 h and were vacuumed till the weight of samples no longer changed. Subsequently, samples were saturated in the 8% KCl solution and weighed at regular intervals. Shale samples were fully saturated when their weight was no longer increased. NMR analyses were performed by SGS Unconventional Petroleum Technical Testing Co., Ltd. using a RecCore2500 instrument with a resonance frequency of 2.38 MHz and a magnetic field strength of 1200 G, and we obtained the T2 spectrums of eight samples at water-saturated condition. The samples were centrifuged at a centrifuge pressure of 300Psi to reach a perfect irreducible water state, and then NMR experiments were conducted again to obtain the T2 spectrums of all samples at irreducible water condition. The measurement parameters used in NMR experiments were as follows: echo spacing, 0.2 ms; waiting time, 6s; numbers of scans, 64; echo numbers, 1024; experiment temperature, 25 C and humidity, 55%. 2.4. FE-SEM observation The FE-SEM imaging of shale samples was performed using the Quanta 200F field emission scanning electron microscopy at the China University of Petroleum (Beijing). Before FE-SEM observation, one surface of each sample was polished using an argon-ion crosssection polisher “TechnoorgSC-100”. After polishing, the polishing surface was coated with gold film at a thickness of 10 nm to enhance the conductivity. Back-scattering electron model was chosen to observe the micromorphology of shale sample at various magnification scales. 3. Results 3.1. Porosity and permeability of shale The porosity and permeability of eight shale samples are
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31
Fig. 1. Sampling site (a) and stratigraphic column of Qiongzhusi Formation in eastern Yunnan Province (b).
Fig. 2. Ternary diagrams of the mineral compositions of Qiongzhusi shale. (a): Relationship between TOC and mineral compositions; (b): Relationship between porosity and mineral compositions.
presented in Table 1. The shale is characterized by tight lithology and complicated pore structure, so the porosity and permeability are significantly low. Additionally, porosity is affected by various geological factors (such as mineralogy, sedimentation, diagenesis and tectonism), which may result in the inapparent relationship
between porosity and mineral compositions (Fig. 2b). Helium porosity of samples ranges from 1.40% to 3.90% with a mean value of 3.17%. The permeability varies from 0.0023 to 0.0061mD with an average of 0.0042mD. Fig. 3 shows that permeability has no apparent relationship with porosity (R2 ¼ 0.0302).
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Table 1 Characteristic parameters of the shale samples from Qiongzhusi Formation in eastern Yunnan Province. Sample TOC (wt.%)
Ro (%)
Quartz (wt.%)
Clay (wt.%)
Potash Feldspar (wt.%)
Plagioclase (wt.%)
Calcite (wt.%)
Dolomite (wt.%)
Pyrite (wt.%)
I/S It C Kao (wt.%) (wt.%) (wt.%) (wt.%)
Porosity Permeability (%) (mD)
YN-1 YN-2 YN-3 YN-4 YN-5 YN-6 YN-7 YN-8
3.30 2.26 2.23 2.16 2.45 2.31 3.32 2.44
35.7 32.2 31.8 31.7 35.0 31.3 34.9 42.7
25.3 33.7 33.1 35.5 27.6 32.6 27.3 36.4
4.0 4.6 4.3 5.1 5.5 5.0 5.7 /
24.3 22.3 21.7 22.7 24.4 23.8 21.1 4.2
3.8 3.1 5.4 / 4.5 3.4 6.5 /
/ / / / / / / 13.4
6.9 4.1 3.7 5.0 3.0 3.9 4.5 3.3
24 23 12 32 20 28 28 17
3.76 2.78 3.15 2.96 1.40 3.61 3.90 3.80
2.23 2.25 1.78 1.42 2.22 1.86 2.42 2.51
49 54 55 53 52 40 38 52
20 19 29 9 18 24 26 19
7 4 4 6 10 8 8 12
0.0050 0.0023 0.0031 0.0039 0.0044 0.0061 0.0036 0.0048
lines in Fig. 5). T2 spectrums of shale cores are divided into two types: unimodal and bimodal T2 spectrums. Unimodal T2 spectrum (YN-2, YN-3, etc.) has an isolated peak in a short relaxation time which normally ranges from 0.01 ms to 10 ms. Bimodal T2 spectrum has two peaks, and the amplitude of right peak ranging from 10 ms to 100 ms is obviously smaller than that of left peak ranging from 0.01 ms to 10 ms. It suggests that not only small pores but also some relatively large pores or fractures are well-developed in shale. In addition, we divide the bimodal T2 spectrum into continuous bimodal T2 spectrum (YN-6) and discontinuous bimodal T2 spectrum (YN-1 and YN-8) based on the shape of T2 spectrum. The former indicates that the samples have good connectivity between small pores and large pores (or fractures). While the latter indicates that pores in shale are relatively closed, and the connectivity between small pores and large pores is poor. Fig. 3. Relationship between porosity and permeability of shale samples.
3.2. NMR T2 distributions of shale T2 distributions can be obtained by the mathematical inversion of T2 decay curves, namely, raw NMR signals (Fig. 4). According to the basic principle of NMR, there is a positive correlation between the transverse relaxation time of hydrogen protons in the pore water and pore diameter (Sun et al., 2012; Huang et al., 2015). Namely, the T2 distributions reflect the pore size distributions: small pores have short relaxation time and large pores or microfractures have long relaxation time (Yao et al., 2010a,b; Tan et al., 2015). The amplitude of T2 spectrum reflects the proportion of pores with different diameters in shale. Therefore, shale with stronger T2 spectrum amplitude at the long relaxation time would have more large pores and better reservoir quality. 3.2.1. NMR T2 spectrum at water-saturated condition The NMR measurements for 8 shale samples were performed to obtain the T2 spectrums at water-saturated condition (yellow solid
3.2.2. NMR T2 spectrum at irreducible water condition NMR measurements were performed again after centrifugation and the T2 spectrums (red dotted line) at irreducible water condition are illustrated in Fig. 5. For some samples (e.g., YN-1 and YN-2), the peaks at 0.01e10 ms have no apparent changes, but those of some samples reduce (e.g., YN-5 and YN-6). The right peak of bimodal T2 spectrum reduces and even disappears after centrifugation. The reason for this case is that fluid in some small closed pores can not flow out freely by centrifugation, so the signal strength of hydrogen protons and the shape of T2 spectrum do not change significantly. Whereas fluid in some large pores with appropriate connectivity can flow out freely by centrifugation, which results in the signal strength decaying and the amplitude of T2 spectrum reducing. The right peak of YN-6 disappears after centrifugation, indicating that microfractures could be welldeveloped in shale sample, benefiting fluid migration. 4. Discussion In this section, the characteristics of the transverse relaxation
Fig. 4. T2 decay curves of shale samples by NMR measurements.
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Fig. 5. NMR T2 spectrums of shale samples.
time distributions of NMR technique are used to evaluate the reservoir properties of shale. T2 distribution characteristics include the number, area, shape, and position of T2 spectrum peaks, which can be used to analyze pore types, residual porosity, movable porosity, reservoir permeability and pore structure parameters of
the shale. Moreover, we also calculate the NMR T2 cutoff value. 4.1. Pore types T2 spectrum and pore size distribution have the similar trend in
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NMR measurements, where each transverse relaxation time represents a pore diameter: long transverse relaxation time represents large pores and short transverse relaxation time represents small pores in shale (Kleinberg et al., 1993; Dunn et al., 2002; Yao et al., 2010a,b). The minimal pore radius allowed fluid to flow out under the action of centrifugal force can be calculated based on the capillary pressure equation:
r¼
2scos q P
(1)
where r is the minimal pore radius, which allows water to flow out at the pressure P, mm; P is the centrifuging pressure, MPa; s is the interfacial tension of shale and water; and q is the contact angle between pore surface and water molecule. Although different shale samples have different contact angles q, in order to simplify the analysis, we use the contact angle between water molecule and pore surface measured by Yakov (2001) in this study, about 73 . At room temperature, the interfacial tension of water is 0.072 N/m. Therefore, the minimal pore radius for water to discharge is 20.33 nm under the action of centrifugal pressure of 2.07 MPa, that is to say, the irreducible water includes not only the clay bound water but also the water remained in the pores and throats of radius less than 20.33 nm. The peaks of eight shale samples distributed from 0.01 ms to 10 ms have small changes after centrifugation except sample YN-5, suggesting that this kind of peak is associated to the small pores which have large capillary pressure to prevent the discharge of fluid. Whereas the peaks in the range of 10e100 ms decrease or disappear by centrifuging, indicating that the increase of pore diameter weakens the bound effect of capillary pressure on fluid. As shown in Fig. 5, whether unimodal or bimodal T2 spectrum, pores in shale samples are mainly adsorption pores, and seepage pores with bimodal T2 spectrum are more developed than those in unimodal shale. Through SEM observation, microfractures are relatively well-developed in bimodal shale (Fig. 6). Microfractures in YN-6 and YN-8 increase the connectivity among pores and improve the permeability of shale. Especially YN-6, microfractures connect small pores and large pores, which contribute to the seepage of fluid. In contrast, for YN-1 with discontinuous bimodal T2 spectrum, although there are a certain number of macropores or microfractures in shale samples, poor connectivity makes the fluid difficult to flow out. However, for YN-5, shale has good connectivity among small pores, so the fluid in seepage pores can be expelled by centrifuging. Given the above, shale with high porosity may not have high permeability, and shale with low porosity may have good permeability. It indicates that porosity has no apparent relationship with permeability in eight groups of shale samples, which is just in accordance with Fig. 3. 4.2. Residual porosity and movable porosity Previous studies have indicated that the estimation of porosity can be obtained accurately regardless of the mineralogy based on NMR measurements because of the relaxation signal from the hydrogen-containing fluid rather than the matrix in rock (Straley et al., 1997; Coates et al., 1999). The signal strength of T2 spectrums at water-saturated condition is converted into NMR porosity 4N of shale, and the results are presented in Table 2. Yao et al. (2010b) have proved that the NMR porosity represents the proportion of pore volume occupied by bound water and free water, so NMR porosity includes residual porosity 4R, corresponding to the proportion of bound water and movable porosity 4M, corresponding to the proportion of free water. The 4R and 4M can be calculated by the following two equations:
4R ¼ 4N BVIðBVI þ FFIÞ
(2)
4M ¼ 4N FFIðBVI þ FFIÞ
(3)
where BVI is the bound fluid index obtained by the T2 spectrum area at irreducible water condition; FFI is the free fluid index; BVI þ FFI represents the sum of bound fluid and free fluid that can be determined by the T2 spectrum area at water-saturated condition. As illustrated in Fig. 5, BVI and FFI are presented with the lightgray area in T2 spectrum at irreducible water condition and the dark-gray area in T2 spectrum at water-saturated condition, respectively. Residual porosity and movable porosity calculated by Eq. (2) and Eq. (3) are shown in Table 2. Residual porosity is between 1.81% and 3.21% with a mean value of 2.58%. Movable porosity ranging from 0.40% to 2.07% with an average of 0.90% is much lower than residual porosity, and the movable porosities of all samples except YN-6 and YN-8 are less than 1%. The proportions of BVI and FFI are obvious in Fig. 5, BVI corresponding to light-gray area in the irreducible water spectrum accounts for a larger proportion, while FFI corresponding to dark-gray area in the 100% water spectrum occupies a small area. It suggests that adsorption pores are dominated in shale reservoir of study area, and the development of adsorption pores is unfavorable for seepage.
4.3. T2 cutoff values NMR T2 cutoff value (T2cutoff) is a relaxation time boundary that divides bound fluid and free fluid in the T2 spectrum at watersaturated condition. The part on the left of T2cutoff in the T2 spectrum represents the bound fluid in the adsorption pores, while the part on the right of T2cutoff in T2 spectrum corresponds to the free fluid in the seepage pores. Some scholars have conducted a large number of tests to obtain the T2cutoff of sandstones and carbonates. Schlumberger recommended that the T2cutoff of mid-high permeability sandstone reservoir and carbonate reservoir approximated 33 ms and 92 ms, respectively (Xiao, 1998; Sun et al., 2012; Ding et al., 2012). The results of Wang et al. (2001) demonstrated that the T2cutoff of low-permeability sandstone reservoir had a wide range of distribution with a mean value of 12.85 ms, which is only about one-third of that of mid-high permeability sandstone reservoir. Accurate calculation of T2cutoff helps to apply NMR technique in the evaluation of shale reservoir. The method for determining T2cutoff of shale samples is shown in Fig. 7. First, two T2 spectrums at water-saturated condition and irreducible water condition respectively are converted into two accumulative T2 spectrums. Maximum cumulative amplitudes of T2 spectrums at water-saturated condition and irreducible water condition are calibrated as total porosity and residual porosity respectively, so the difference between total porosity and residual porosity means movable porosity, which corresponds to the segment “H” in Fig. 7. Second, a horizontal line through residual porosity is drawn, and this line and accumulative T2 spectrum at water-saturated condition intersect at one point “A”. Third, a vertical line is made through the point “A”, and the T2 value at the intersection projected on the time axis is the T2cutoff. The T2cutoff of samples calculated by the above method is shown in Table 2, and T2cutoff is in the range of 0.54e4.28 ms with an average of 1.45 ms. The T2cutoff of YN-1 is significantly greater than that of other samples, and the T2cutoff of other seven samples is all less than 4 ms. As shown in Fig. 7, the higher the content of bound water in shale is, the greater the T2cutoff is, namely, shale with higher bound water content has a greater T2cutoff.
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35
Fig. 6. FE-SEM images of microfractures in shale samples. Netted microfractures and continuous microfractures increase the connectivity among pores, thus improving the permeability of shale (aee). However, some small microfractures with poor extensibility cannot connect the pores, which are unfavorable for seepage (f). a and b: sample YN-6; c and d: sample YN-8; e and f: sample YN-1.
Table 2 NMR porosity, residual porosity, movable porosity, NMR permeability and T2cutoff of shale samples. Sample
4N (%)
BVI(BVI þ FFI)
FFI(BVI þ FFI)
4R (%)
4M (%)
T2cutoff (ms)
kNMR (mD)
YN-1 YN-2 YN-3 YN-4 YN-5 YN-6 YN-7 YN-8
3.96 3.11 3.44 2.77 2.59 4.23 3.42 4.35
0.81 0.87 0.87 0.68 0.70 0.51 0.79 0.73
0.19 0.13 0.13 0.32 0.30 0.49 0.21 0.27
3.21 2.71 2.99 1.88 1.81 2.16 2.70 3.18
0.75 0.40 0.45 0.89 0.78 2.07 0.72 1.17
4.28 1.13 1.10 0.54 0.83 0.73 1.04 1.93
0.0042 0.0014 0.0021 0.0035 0.0024 0.0070 0.0039 0.0045
4.4. Permeability Shale as a tight reservoir of natural gas is characterized by low permeability. Estimating shale permeability is another important function of NMR core experiment (Shao et al., 2009; Ding et al., 2014). Based on the bound fluid volume, free fluid volume and NMR porosity, constructing a model among these parameters provides a good method to calculate the permeability of shale. A new regional model is built on the basis of Coates permeability model in this study. The parameters in this new model vary with different regions, so the results are more reasonable. On the basis of Timur formula, Coates established the Coates permeability model through a large number of experiments in 1991 (Arnold et al., 2006; Tian, 2010). Coates model can be described as follows: Fig. 7. T2 cutoff value calculation for sample YN-6 with the NMR measurements at water-saturated condition (Sw) and irreducible water condition (Sir).
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A. Li et al. / Journal of Natural Gas Science and Engineering 37 (2017) 29e38
kNMR ¼
4 4 FFI 2 N
C
(4)
BVI
kNMR is the permeability estimated by NMR experiment, C is a constant. Coates model is a basic model for calculating permeability using NMR. In order to make this model suitable for different regions, it is changed in the form of “y ¼ mx þ b”. Regional Coates model is as follows:
FFI BVI
0:5
0:25 k ¼ m NMR 4N
! þb
(5)
m and b are the slope and intercept of straight-line respectively, which are determined by fitting analysis. (FFI/BVI)0.5 and (k0.25 NMR/4N) are regarded as the “x” and “y” of linear equation respectively to carry on linear fitting. (FFI/BVI)0.5 has a strongly linear correlation with (k0.25 NMR/4N), with a correlation coefficient of 0.883. Therefore, the Coates model of study area is:
FFI BVI
0:5
0:25 k ¼ 11:62 NMR 4N
! 0:336
(6)
The NMR permeability of shale samples calculated by Eq. (6) is in the range of 0.0014e0.0070mD with an average of 0.0036mD (Table 2). Fig. 8 is the cross-plot of NMR permeability and gas log permeability of samples. All sample points except YN-5 are close to the bisector, indicating that the regional Coates model is effective for permeability calculation and NMR permeability satisfies the demands of research and production. 4.5. Pore structure The transverse relaxation characteristics of fluid in porous media can be described by the following mathematical equation:
1 1 1 1 ¼ þ þ T2 T2B T2D T2S
pore surface and hydrogen-containing fluid (e.g. water and kerosene), so total relaxation time T2 is almost determined by the surface relaxation time T2S (Coates et al., 1999; Wang, 2009; Sun et al., 2012). When the bulk relaxation and diffusion relaxation of pore fluid are not considered, Eq. (7) can be simplified as:
1 S ¼ r2 T2 V
(8)
where r2 is the transverse surface relaxivity of rock, mm/ms. The r2 of shale as a constant is about 0.05 mm/ms based on the work of Sondergeld et al. (2012); S and V are the surface area and volume of rock pores, respectively, mm2 and mm3. S/V is the specific surface area that relates to the pore size, smaller pores have greater S/V values and shorter T2. On the contrary, the more large pores in the rock, the lower the value of S/V is, and the longer the T2 is. Hence, the interaction between water molecules and pore surface can be characterized by T2 relaxation of water-saturated samples. For simplification purpose, the pore geometry of shale is regarded as cylinder because of the connectivity among shale pores, so S/V equals 2/r. Eq. (8) can be further transformed into the relationship between T2 and shale pore size.
r ¼ 0:1T2
(9)
Therefore, the specific surface area distributions of samples can be obtained according to Eq. (8). The specific surface area distribution of YN-1 is shown in Fig. 9. The specific surface area ranges from 0.34 mm1 to 433.33 mm1 with an average of 48.31 mm1 and is mainly distributed in the range of 0.5e1.79 mm1 and 8.3e93.72 mm1. Moreover, the T2 distributions of eight samples are converted into the pore size distributions based on Eq. (9) (Fig. 10). In eight shale samples, transition pores account for the largest proportion, followed by mesopores, indicating that transition pores and mesopores are the major sites for the accumulation of shale gas. Furthermore, the well-developed macropores and microfractures in shale samples with bimodal T2 spectrums also provide more reservoir space for shale gas.
(7)
where 1/T2 is the total relaxation rate of pore fluid, ms1. T2B, T2D, and T2S are bulk relaxation time, diffusion relaxation time and surface relaxation time, respectively. In low-field NMR experiment, the applied magnetic field from NMR instrument is uniform, so the diffusion relaxation rate 1/T2D approaches zero. Moreover, the bulk relaxation time of pore fluid T2B is so long that it contributes little to total relaxation rate. Porous media are generally characterized by complicated surface structure with strong interaction between
Fig. 8. The cross-plot of NMR permeability and gas log permeability of samples.
5. Conclusions In this paper, the petrophysical properties of 8 shale samples from the Lower Cambrian Qiongzhusi Formation in eastern Yunnan Province are investigated using NMR experiments. Additionally, the method for determining T2cutoff of shale has also been discussed based on the physical significance of T2cutoff. The following conclusions can be made:
Fig. 9. The specific surface area distribution of sample YN-1 based on NMR measurements.
A. Li et al. / Journal of Natural Gas Science and Engineering 37 (2017) 29e38
37
Fig. 10. Pore size distributions of eight shale samples by NMR measurements.
(1) T2 spectrums of shale samples at water-saturated condition can be divided into two types: unimodal and bimodal T2 spectrums. Whether unimodal or bimodal T2 spectrum, pores in shale samples are mainly adsorption pores, and seepage pores in bimodal shale are more developed than those in unimodal shale. Continuous bimodal T2 spectrum reflects the samples with good connectivity between small pores and large pores. (2) According to the T2 spectrum area fraction, residual porosity and movable porosity can be well estimated. The results show that movable porosity is much lower than residual porosity. Adsorption pores are dominated in shale reservoir of study area, and the development of adsorption pores is unfavorable for seepage.
(3) The method for determining T2cutoff of shale has been discussed based on the physical significance of T2cutoff. The T2cutoff of shale samples is less than 4 ms except for YN-1. The higher the content of bound water in shale is, the greater the T2cutoff is. On the basis of Coates model, a regional Coates model is proposed to calculate the permeability of shale samples. The NMR permeability is close to gas log permeability, so the regional Coates model is effective for permeability calculation. (4) The specific surface area distribution and pore size distribution can be obtained based on the mathematical equation of T2. The specific surface area is mainly distributed in the range of 0.5e1.79 mm1 and 8.3e93.72 mm1. Transition pores and mesopores contribute the major sites for the
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accumulation of shale gas. Moreover, the well-developed macropores and microfractures in shale with bimodal T2 spectrum also provide more reservoir space for shale gas. Acknowledgements This research was supported by the National Natural Science Foundation of China (Project Nos. 41072098 and 41372139) and the Important National Science and Technology Specific Projects of China (Nos. 2016ZX05046-003, 2011ZX05018-001-002 and 2011ZX05009-002-205). The authors would like to thank the staff of all of the laboratories that cooperated in performing the tests and analyses. We are also grateful to the reviewers, whose comments improved the quality of this manuscript. References Arnold, J., Clauser, C., Pechnig, R., Anferova, S., Anferov, V., Blümich, B., 2006. Porosity and permeability from mobile NMR core-scanning. Petrophysics 47, 306e314. Bowker, K.A., 2007. Barnett shale gas production, Fort Worth Basin: issues and discussion. AAPG Bull. 91 (4), 523e533. Caldwell, R., 2006. Unconventional resources: are they for real. Scotia Newsl. 3, 1e2. Chalmers, G.R.L., Bustin, R.M., 2007. The organic matter distribution and methane capacity of the Lower Cretaceous strata of Northeastern British Columbia. Can. Int. J. Coal Geol. 70, 223e339. Clarkson, C.R., Jensen, J.L., Chipperfield, S., 2012. Unconventional gas reservoir evaluation: what do we have to consider? J. Nat. Gas. Sci. Eng. 8, 9e33. Clarkson, C.R., Solano, N., Bustin, R.M., Bustin, A.M.M., Chalmers, G.R.L., He, L., ski, A.P., Blach, T.P., 2013. Pore structure characterMelnichenko, Y.B., Radlin ization of North American shale gas reservoirs using USANS/SANS, gas adsorption, and mercury intrusion. Fuel 103, 606e616. Coates, G.R., Xiao, L.Z., Prammer, M.G., 1999. NMR Logging Principles and Applications. Gulf Publishing Company, Houston, Texas. Curtis, J.B., 2002. Fractured shale-gas system. AAPG Bull. 86 (11), 1921e1938. Curtis, M.E., Ambrose, R.J., Sondergeld, C.H., Rai, C.S., 2012. Microstructural investigation of gas shales in two and three dimensions using nanometer-scale resolution imaging. AAPG Bull. 96, 665e677. Ding, W.L., Dai, P., Zhu, D.W., Zhang, Y.Q., He, J.H., Li, A., Wang, R.Y., 2015. Fractures in continental shale reservoirs: a case study of the Upper Triassic strata in the SE Ordos Basin. Cent. China. Geol. Mag. 153 (4), 663e680. Ding, W.L., Fan, T.L., Yu, B.S., Huang, X.B., Liu, C., 2012. Ordovician carbonate reservoir fracture characteristics and fracture distribution forecasting in the Tazhong Area of Tarim Basin, Northwest China. J. Pet. Sci. Eng. 86e87, 62e70. Ding, Y.J., Guo, B.H., Yan, X.R., Li, J., Lu, Q., 2014. On identification shale reservoirs validity and physical parameters quantitative evaluation method. Well Logging Technol. 38 (3), 297e303. Dunn, K.J., Bergman, D.J., Latorraca, G.A., 2002. Petrophysical NMR measurements. Handb. Geophys Explor Seismol. Explor 32, 71e127. Heath, J.E., Dewers, T.A., McPherson, B.J., Petrusak, R., Chidsey, T.C., Rinehart, A.J., Mozley, P.S., 2011. Pore networks in continental and marine mudstones: characteristics and controls on sealing behavior. Geosphere 7 (2), 429e454. Huang, J.G., Xu, K.M., Guo, S.B., Guo, H.W., 2015. Comprehensive study on pore
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