- Email: [email protected]
NMR petrophysical interpretation method of gas shale based on core NMR experiment
Journal of Petroleum Science and Engineering 136 (2015) 100–111
Contents lists available at ScienceDirect
Journal of Petroleum Science and Engineering journal homepage: www.elsevier.com/locate/petrol
NMR petrophysical interpretation method of gas shale based on core NMR experiment Maojin Tan a,b,n, Keyu Mao c, Xiaodong Song b, Xuan Yang c, Jingjing Xu b a
Key Laboratory of Geo-detection of Ministry of Education, China University of Geosciences, Beijing 100083, China School of Geophysics and Information Technology, China University of Geosciences, 29 Xueyuan Road, Haidian District, Beijing 100083, China c Sinopec Oilfield Service Corporation Shengli Well Logging Company, Dongying 257096, China b
art ic l e i nf o
a b s t r a c t
Article history: Received 10 June 2015 Received in revised form 1 November 2015 Accepted 2 November 2015 Available online 4 November 2015
Nuclear magnetic resonance (NMR) characteristics provide critical information to study the storage of organic shale. Based on NMR petrophysical experiment of Haynesville Shale, the response characteristics of transverse relaxation time (T2) are analyzed. According to the shape and amplitude of T2 distribution of shale, Haynesville Shale can be categorized into two types: continuous and discontinuous T2 spectrums. Compared with routine density porosity, NMR porosity is generally underestimated for organic shale. Therefore, the controlling factors are investigated in detail. Through constructing the correlations of NMR porosity and clay volume, total organic carbon (TOC) content, kerogen content and pyrite volumetric concentration, the pyrite and kerogen are finally regarded as dominant factors. Subsequently, a new NMR porosity correction model is proposed. Moreover, the permeability model based on NMR bin porosity is built, and water, gas and oil saturation models are also established through building some correlations between gas saturation and NMR bin porosity. NMR experiment indicates that the kerogen and adsorbed gas are located in bound fluid part of NMR T2 distribution, and the gas storage is dominant as absorbed gas rather than free gas. The study provides a novel petrophysical interpretation method for gas-bearing shale. & 2015 Elsevier B.V. All rights reserved.
Keywords: Gas shale Nuclear magnetic resonance (NMR) T2 distribution Kerogen Gas saturation Petrophysical interpretation method
1. Introduction Gas-bearing shale is one of the most valuable unconventional reservoirs. Comparing with conventional sandstone resourses, the mineralogy of shale gas reservoir is extremely complex, and its pore space mostly includes both sub-micron and nano-scale matrix pore, sometimes, relatively large aperture fractures are developed (Curtis, 2002; Rick, 2004; Glorioso and Rattia, 2012; Sondergeld et al., 2012). In gas-bearing shale, occurrence of gas is complex, gas is partly free in matrix and kerogen, and partly absorbed on the surface of pores and in kerogen. A small portion of gas is dissolved in fluids of shale (Sondergeld et al., 2012). So, organic shale is a typical “self-generating and self-storage” system (Curtis et al., 2012). Routine petrophysical experiment is the most effective method to study the properties of organic shale, which can measure resistivity, porosity, permeability, saturation, and is also an n Corresponding author at: School of Geophysics and Information Technology, China University of Geosciences, 29 Xueyuan Road, Haidian District, Beijing 100083, China. E-mail address: [email protected] (M. Tan).
http://dx.doi.org/10.1016/j.petrol.2015.11.007 0920-4105/& 2015 Elsevier B.V. All rights reserved.
indispensable bridge between logs response and logs interpretation (Luffel et al., 1992; Guidry et al., 1990; Ross and Bustin, 2007, 2008; Xu and Torres-Verdín, 2013; Yang and Torres-Verdín, 2013). In recent years, Nuclear magnetic resonance (NMR) is only sensitive to hydrogen fluids in pores of saturated rocks, and has been widely used in petrophysical experiments and geophysical logging (Coats et al., 1999; Dunn et al., 2002). NMR instrument can not only measure the total porosity, movable fluid volume, and bound fluid volume, it can even calculate the permeability based on NMR bin porosity. Furthermore, NMR experimental results of core plugs can be used to calibrate NMR logging. In last two decades, core NMR experiment and NMR logging were extensively applied to sandstone and carbonate reservoirs, some experimental results of core plugs can provide the guidance for logs interpretation and help to construct the petrophysical models (Rick, 2004; Luffel et al., 1992; Decker et al., 1990; Arvie et al., 2005; Passey et al., 1990; Akkurt et al., 1996), which indicates that NMR applications in conventional reservoirs were successful. Unfortunately, NMR core measurement and NMR logging were not fully utilized in gas shales. So far, NMR logging technique for natural gas was mainly on the bulk and unrestricted gas (Akkurt et al., 1996; Daniels, 2004;
M. Tan et al. / Journal of Petroleum Science and Engineering 136 (2015) 100–111
101
Hook et al., 2011). Recently, Sigal and Odusina (2011) published NMR laboratory work on methane-saturated cores from Barnett Shale. The limitation remains with overlapping T2 signals between water and gas/oil in the complex pore systems, which makes it difficult to separate different fluids from the T2 distribution. Rylander et al. (2010) studied the NMR T2 distribution in the Eagle Forward Shale, and analyzed the NMR characteristics of bound oil and free oil, and compared the difference of lower and upper Eagle ford Shale. In this study, based on NMR core experiment of Haynesville Shale, we analyzed the NMR T2 response characteristics. Then, we compared NMR porosity with routine density porosity, analyzed the influence factors, and investigated contributions of free gas, kerogen, adsorbed gas to T2 distribution. Finally, we established NMR petrophysical interpretation models including corrected NMR porosity model, permeability model, and saturation models. Moreover, the specific area and throat radius are estimated from NMR T2 distribution. Therefore, this NMR experiment of shale core plugs provides an important alternate for log interpretations of gas-bearing shale.
2. NMR T2 relaxation mechanism 2.1. Petrophysical model of organic shale Organic shale has complex pore types, and the pore morphology is also diverse. According to different types of shales, the reservoir space can be divided into two types including matrix pore and fracture, and the former includes intergranular pore, intragranular pores, and voids within the organic plasmids (Fig. 1), which is mostly nano-level. The connectivity between these voids is so poor (Fig. 1a, Curtis et al., 2010). In gas-bearing shale, occurrence of gas is various, gas is partly free in matrix and kerogen, and some are absorbed on the surface of pores and in kerogen (Fig. 1b). A small portion of gas is dissolved in fluids of shale (Kausic et al., 2011). Organic shale is rock matrix and fluids. The rock matrix usually consists of dry clay, quartz, feldspar, plagioclase, calcite, pyrite and kerogen. For Haynesville Shale, X-ray diffraction (XRD) results of shale are shown as below (Fig. 2a), which demonstrates that the clay content is dominating. So, the petrophysical model is set as Fig. 2(b). 2.2. NMR T2 relaxation mechanism As we all know, NMR is the interaction between the hydrogen nucleus and the magnetic field. Total NMR relaxation includes surface relaxation, bulk relaxation of fluid precession, and diffusion relaxation caused by gradient field. Therefore, total NMR relaxation in porous media can be described by the following mathematical formula:
1 S 1 D (Gγ TE) 2 = ρ2 + + T2 V T2b 12 G = Gexternal + Ginternal , Δχ Ginternal ≈ B0 r
(1)
where ρ2 is the surface relaxivity, μs/m; D is the free diffusion coefficient for the fluid, μs/cm2; γ is the gyromagnetic ratio; TE is the echo spacing of the measurement sequence, ms; and S/V is the surface area to volume ratio (specific surface area) of the pores, μs 1; G is the magnetic field gradient, which includes the external and interior magnetic field gradient, Gauss/cm; B0 is the applied magnetic field strength, Gauss/cm; r refers to the distance from
Fig. 1. All kinds of pores in organic shale (a) and gas occurrences in different pores (b).
the magnetic field changes, m; Δχ is the susceptibility difference between rock matrix particles and pore fluid, 10 6 SI. From Eq. (1), total T2 relaxation time is related to specific surface area, fluids in rocks as well as diffusion coefficient and echo spacing. Usually, the measurement of NMR experiment is under uniform magnetic field, and the diffusion relaxation term could be ignored, so the specific surface area can reflect the pore size distribution in the sandstone and carbonate formations. But, for organic shale, the total clay content is so high, and small pores or throats may induce an internal magnetic gradient in terms of Eq. (1) even though in homogenous magnetic field. So, it will also decrease the T2 relaxation time and make T2 spectrum shift leftward. The pattern and amplitude of NMR T2 distribution reflect the characteristics of different reservoirs. Generally, the higher amplitude of T2 distribution at short relaxation time indicates larger bound fluid volume, whereas, the higher amplitude of T2
102
M. Tan et al. / Journal of Petroleum Science and Engineering 136 (2015) 100–111
5.9% 4.9%
2.8%4.7%
Total Clay Quartz
0.3%
K Feldspar
47.5%
Plagioclase 34.2%
Calcite Pyrite Kerogen
the temperature is about 28 °C. The equipment used is MARANDRX, which is a low-field NMR instrument with uniform magnitude field. The number of echoes is 1024 and the echo spacing is 0.2 ms. The number of scanning and stacking is 128. Because little fractures were observed in these shale cores, the waiting time of NMR experiments was set as 5.0 s through comparison of testing. In addition, some routine experimental measurements such as density porosity and permeability, are carried out, and total organic carbon content (TOC) and kerogen volume were measured in core plugs. The porosity and matrix permeability were calculated from measured pressure-decay data using a fresh crushed (20/35 mesh size) sample including all interconnected pore space. Gas saturations were determined by Dean-Stark technique using a crushed sample and dried at 110°, and oil saturation was computed using an ambient oil density of 0.80 g/cm3; water saturation was computed using a brine concentration of 30,000 ppm with an ambient density of 1.018 g/cm3. The clay minerals content was from X-ray diffraction (XRD) analysis. Table 1 shows NMR and routine experimental results of 11 shale core plugs, including NMR porosity, clay bound water volume, effective porosity, density porosity, air permeability, and TOC content. T2 geometric mean value was calculated. The NMR porosity of 11 cores averages about 4.35% and the routine density porosity is about 5.70%, so, the former is lower than the latter. The measured air permeability is in the range of (0.07–816) 103nd, with an average of 268.7 103nd. The TOC content ranges from 0.45 wt% to 2.69 wt%. 3.2. NMR T2 distribution of gas shale
Fig. 2. Mineral compositions and petrophysical model in different pores.
distribution at long relaxation demonstrates larger movable fluid volume.
3. NMR characteristics and interpretation method in shale 3.1. NMR experiments of core plugs In our experiment, 11 core plugs of Haynesville Shale were selected from the drilling cores and stored for laboratory measurement. The size of all core samples is about 1.0 in. diameter, and the bulk volume ranges from 13.53 cm3 to 33.02 cm3, which is listed in Table 1. The measurement pressure is about 800 psi, and
T2 distribution of water-saturated conventional rocks such as sandstone and carbonate usually describes pore sizes distribution. The more developed the large pores in reservoir rock are, the stronger the NMR T2 spectrum signal at the long relaxation time will be; instead, if the small pores in the rock are poorly developed, the NMR T2 spectrum at short relaxation time is dominated. Thus, according to the characteristics of T2 spectrum, we can analyze quantitatively the storage and flow properties of fluids in shale. Fig. 3 shows the NMR T2 distributions of 11 shale core samples. In terms of the characteristic of T2 spectrum and morphology, NMR T2 distributions of shale cores are categorized into two types: continuous bimodal T2 spectrum (Type I) and discontinuous bimodal T2 spectrum (Type II). Fig. 3(a) shows that NMR T2 distribution (H6) has a significant unimodal, and the left peak of T2 spectral is much more significant than the right one, whose corresponding relaxation time is about 1 ms. Moreover, the right part of T2 spectrum has much lower amplitude with the relaxation time ranging from about 8 ms to 80 ms. Generally, the whole T2 distribution is discontinuous
Table 1 NMR experiment results and conventional petrophysical measurement. No. Sample ID Bulk volume of sample (cm3)
1 2 3 4 5 6 7 8 9 10 11
H6 H7 H9 H17 H19 H21 H29 H33 H39 H42 H48
29.44 28.90 29.03 31.61 33.02 21.45 24.36 13.53 29.73 29.61 31.33
NMR data
Conventional data
Porosity (%) Clay bound water volume Effective porosity (%) (%)
T2 log mean (ms)
Porosity Permeability 103nd TOC (wt%)
6.7 6.9 7.2 2.5 3.8 5.4 4.5 3.5 3.3 2.2 1.9
0.3 0.3 0.3 1.2 0.4 0.3 0.3 0.5 0.3 0.8 0.5
6.27 6.57 6.57 5.96 5.92 6.65 6.69 7.60 5.52 2.80 2.12
6.6 6.9 7.1 1.8 3.4 5.1 4.2 2.9 3.1 1.8 1.7
0.1 0.1 0.1 0.7 0.4 0.3 0.3 0.4 0.2 0.4 0.2
2.25 0.07 1.52 790 75.9 190 346 816 589 130 15.1
0.87 0.45 0.91 2.67 2.31 2.14 2.36 2.69 1.77 0.95 0.61
M. Tan et al. / Journal of Petroleum Science and Engineering 136 (2015) 100–111
10
0.5 H6 H7 H9
8
NMR porosity (%)
Incremental porosity(%)
0.4
0.3
0.2
0.1
0.0 0.01
0.1
1
10
100
1000
4
0.4
0.3
0.2
0.1
1
2
4
6
8
10
Core porosity (%) H17 H19 H21 H29 H33 H39 H42 H48
0.1
0 0
0.5
Icremental porosity(%)
6
2
T2 (ms)
0.0 0.01
103
10
100
1000
T2 (ms) Fig. 3. NMR response characteristics of shale. Shale is classified into two types: Type I (a) and Type II (b).
with logarithmic mean of about 0.3 ms. In this NMR experiment, three of the 11 cores are classified as Type I. Different from NMR T2 spectrum of Type I, NMR T2 spectrum of Type II (H12) is continuous, and the amplitude difference between left and right peaks is much smaller than that of Type I shale (Fig. 3b). The relaxation time of left peak ranges from about 0.8 ms to 2.0 ms; in contrast, the relaxation time of right peak ranges from about 2.0 ms to 200 ms with wider distribution. It reflects that the latter has a relatively wider pore size than the former, which indicates that some relatively large pores, fractures, and small caves can be well developed. Furthermore, from Table 1, the permeability of Type II shale is also much higher than Type I shale. 3.3. Analysis on NMR porosity Theoretical studies of NMR suggest that, for hydrogen-containing fluid saturated rock, NMR measures the relaxation signal from the fluid rather than matrix, so NMR measurement can provide accurate porosity estimation independent of mineralogy (Coats et al., 1999). Experimental study from a large number of scholars confirmed that NMR porosity is accurate in sandstone and carbonate reservoirs, and the absolute error between NMR porosity and density porosity is generally less than l.0% (Dunn et al., 2002). So, the measured NMR porosity is usually the same as routine density porosity.
Fig. 4. Comparison of NMR porosity and routine density porosity of shale. NMR porosity of some cores is worse underestimated.
Fig. 4 shows comparison of NMR porosity and routine density porosity of shale, which indicates that NMR porosity of some cores with medium porosity is underestimated. To investigate the influence factors, we built some crossplots (Fig. 5). Fig. 5(a) shows the correlation of NMR porosity and clay minerals content, which indicates that the fine clay pore in shale increases porosity rather than decreasing porosity even though the internal magnetic field gradient is induced by micro-pore in the clay. Therefore, the clay mineral's content is not the main reason causing NMR porosity reduction. To investigate this problem, we calculated the porosity difference between the density porosity and NMR porosity. Fig. 5 (b) and (c) shows the correlations of the porosity difference and clay content, and gas saturation, which indicates neither the clay nor fluid in gas shale is one of the impact factors. Fig. 5(d) shows that, for these cores whose NMR porosity is lower than density porosity, NMR porosity decreases with increasing clay content, but the porosity difference increases (Fig. 5e). Moreover, Fig. 5 (f) illustrates that TOC content also increases with the pyrite content rising, which indicates NMR porosity is related to pyrite, and that is, NMR porosity decreases with the increasing pyrite. Furthermore, Fig. 5(g) and (h) also illustrates that the kerogen volume is related to the pyrite content, which further verifies that the pyrite is associated with the kerogen. As we all know, pyrite is rich in iron (Fe) and has high relaxavity, which will accelerate NMR echo decaying, so, it is difficult to capture the fluid signal in NMR experiments of shale cores, and the measured porosity is sure to be lower. Therefore, the pyrite or other ferromagnetic minerals contained in organic shale mainly contributed to the decreasing porosity. To correct the NMR porosity, it is possible to build the correction,
ϕNMR, Corr = ϕNMR + ϕdifference
(2)
where ϕNMR, Corr is the corrected porosity, % and ϕdifference is the porosity difference between NMR porosity and core density porosity, which can be gained from Fig. 5(e) as
ϕdifference = 1.498⋅TOC − 0.8824 R2 = 0.7309, where TOC is the total organic carbon content, wt%.
(3)
M. Tan et al. / Journal of Petroleum Science and Engineering 136 (2015) 100–111
10
5
8
4
Porosity difference (%)
NMR porosity (%)
104
6
4
2
3
2
1
0
0 0
10
20
30
40
0
50
10
5
10
4
8
NMR porosity (%)
Porosity differnce (%)
20
30
40
50
4
5
Clay content (%)
Clay content (%)
3
2
1
6
no porosity difference
4
2
0 0
20
40
60
0
80
0
1
Gas saturation (%)
2
3
Clay content (%)
3
5
2
TOC (wt.%)
Porosity difference (%)
4
1
3
2
1
0
0
0
1
2
3
4
5
0
1
TOC (wt.%)
3
4
5
3
4
5
Pyrite (%)
8
5 y=0.4459x-0.0119 R =0.9994
4
6
3
Kerogen (%)
TOC (wt.%)
2
2
4
2
1
0
0
0
2
4
Kerogen (%)
6
8
0
1
2
Pyrite (%)
Fig. 5. Crossplots for analysis on impact factors of NMR porosity. NMR porosity increases with the clay content rising (a); porosity difference is not related to clay content (b) and gas saturation (c); NMR porosity decreases with TOC content rising when TOC larger than 1.0 wt% (d), and porosity difference increases with TOC content rising (e). In addition, TOC content is proportional to pyrite volume (f) and kerogen volume (g), and kerogen is negative related to pyrite volume (h).
10000
10000
1000
1000 Permeability (1000xnd)
Permeability (1000xnd)
M. Tan et al. / Journal of Petroleum Science and Engineering 136 (2015) 100–111
100
10
1
100
10
1
0.1
0.1
0.1
1
10
0.1
1
10
Core density porosity (%)
10000
10000
1000
1000
Permeability (1000xnd)
Permeability (1000xnd)
Porosity (%)
100
10
1
100
10
1
0.1
0.1 0.01
105
0.1
1
0.1
Effective Porosity (%)
1
10
Clay water volume (%)
Fig. 6. Relationships between permeability and NMR component porosities. The permeability is worse related to NMR porosity (a) and routine density porosity (b), but it is positively related to NMR effective porosity (c) and negatively related to clay water volume (d).
3.4. Permeability model from NMR bin porosity The permeability is an important parameter for deliverability evaluation of gas-bearing shale. Typically, the permeability of shale is so small, and it is difficult to predict accurately. NMR measurements can provide some component porosities, such as effective porosity, clay bound water volume, and free fluid volume, which is the advantage of NMR measurement over the routine laboratory measurement. Therefore, constructing the relationship between permeability and NMR component porosities may provide a good alternative way for the permeability prediction of shale. Fig. 6 shows the crossplots of permeability and routine density porosity, and NMR porosity. These correlations are both poor, which indicates that the permeability does not tend to increase with both porosities increasing (Fig. 6a and b). On the right-bottom of Fig. 6(a), three cores with large porosity have extremely low permeability, which indicates that this type of pores has no contribution to permeability of organic shale even though they have high porosity. For these fine pores, they could be isolated in kerogen or among mineral particles, and not connected with the outside, which hinders the penetration of the fluid, so they cannot elevate the permeability of shale. These three cores are verified as H6, H7 and H9 shown in Table 1, and they belong to Type I shale. Their bound fluid volumes are extremely high; their movable fluid volumes are extremely small, and TOC content is so low. It indicates that the water in pores among fine clay particles in such shale is absolutely irreducible and affects the permeability. For this, we build the correlations of permeability and NMR effective porosity, and clay bound water volume, which are illustrated in
Fig. 6(c) and (d), respectively. The absolute permeability is proportional to NMR effective porosity and it decreases with the bound fluid volume increasing. Therefore, in terms of Fig. 6(c) and (d), we built the following formula to calculate the absolute permeability of shale by data fitting method:
⎛ ϕ ⎞1.79 KNMR = 89.35 ⎜ E ⎟ , ⎝ ϕCBW ⎠ R2 = 0.8091
(4)
where ϕCBW is the clay water volume, %; ϕE is NMR effective porosity, %; and KNMR is absolute permeability from NMR measurement, 10 6 μm2. 3.5. Saturation model from NMR bin porosity Except for NMR, TOC, kerogen, and others measurements, gas saturation was measured for 11 core plugs of Haynesville Shale, and the relationship between gas saturation and various NMR bin porosities is established, which can be used to study the mode of occurrence of shale gas. First, we built crossplots of gas saturation and NMR porosity, and total porosity, but the correlations are both so poor. However, the correlations of gas saturation and NMR effective porosity, TOC content, and kerogen volume are so excellent (Fig. 7). Fig. 7(a) indicates that the gas saturation increases with NMR effective porosity rising. Moreover, Fig. 7(b) and (c) indicates gas saturation has positive correlations with TOC content and kerogen volume, which means that natural gas of organic shale is related to kerogen. As we know, free gas, and adsorbed gas or dissolved gas all contribute to total gas
M. Tan et al. / Journal of Petroleum Science and Engineering 136 (2015) 100–111
80
80
60
60
Gas saturation (%)
Gas saturation (%)
106
40
20
40
20
0
0 1E-3
0.01
0.1
0
1
1
2
80
60
60
Gas saturation (%)
Gas saturation (%)
80
40
20
0 0
2
4
4
5
6
40
20
0
8
0
20
Kerogen (%)
40
60
80
Effective porosity index (%)
80
80
Difference of gas saturation (%)
Difference of gas saturation (%)
3
TOC (wt.%)
Free fluid volume (%)
60
40
20
60
40
20
0
0 0
20
40
60
80
0
2
Clay content (%)
4
6
8
10
Kerogen (%)
Fig. 7. Relationships between gas saturation and NMR porosity, TOC content, and kerogen volume. Gas saturation is positively related to NMR porosity (a), TOC content (b) and kerogen volume (c).
saturation, but what are the response characteristics of these gas components above in NMR measurement? In order to study the contribution of NMR effective porosity to gas saturation, we construct an index ( Seff , NMR ), namely, NMR effective porosity index, which indicates the proportion of NMR effective porosity volume throughout the pore space
Seff , NMR =
ϕE , ϕNMR
(5)
where ϕNMR is the NMR porosity, % and ϕE is the NMR effective porosity, %. Fig. 7(d) shows the relationship between gas saturation and NMR effective porosity index, which indicates that the measured
gas saturation is higher than NMR effective porosity index. This clearly shows that NMR effective porosity only includes a little amount of free gas with long relaxation time, and most of gas is from “clay bound water” part of T2 distribution with fast relaxation. To further analyze the relationship between gas saturation and NMR bin porosity, we construct another parameter, Sgasdiff , which is named as gas saturation difference between really measured gas saturation and NMR effective porosity index
Sgasdiff = Sgas − Seff , NMR,
(6)
where ϕNMR is the NMR porosity, % and Seff , NMR is the NMR effective porosity index, %.
M. Tan et al. / Journal of Petroleum Science and Engineering 136 (2015) 100–111
ϕCBW , ϕNMR
(7)
where ϕCBW is the NMR “clay bound water” volume, %; and Swirr is the NMR “clay bound water” or irreducible water saturation, %. Fig. 8(a) is the crossplot of the measured water saturation, Swater , and Swirr . As can be seen, Swater is far lower than Swirr , which shows that NMR “clay bound water” volume also includes other fluids besides true clay water. To this end, we construct a parameter, Swaterdiff , named as fluid saturation difference, which describes the difference between Swirr and Swater ,
S waterdiff = S wirr − S water ,
(8)
where Swaterdiff is the water saturation difference, %. Fig. 8(b) and (c) are crossplots of Swaterdiff and TOC content, and kerogen volume, respectively. The water saturation difference increases with TOC content and kerogen volume rising, and especially, it has an excellent linear relationship with kerogen volume. This proves the kerogen or adsorbed gas in kerogen is located in short relaxation part of T2 distribution, and their NMR T2 distributions overlap together with that of NMR “clay bound water” volume. Therefore, NMR “clay bound water” in T2 distribution includes the contributions of both kerogen and its absorbed gas. Therefore, based on the above analysis, NMR response in organic shale includes movable fluid, that is, free gas, true clay bound water, kerogen, and its absorbed gas. Therefore, we built a gas saturation model based on NMR bin porosity
Sgas = − 0.5624 R2 = 0.9938
ϕCBW ϕ + 0.5551 E + 14.35Vkero, ϕNMR ϕNMR (9)
where Vkero is the kerogen volume, %. Since there is no movable water in organic shale, and the water comes from bound fluid part of T2 distribution with fast relaxation time. Therefore, we constructed a water saturation formula as follows:
"Bound fluid" saturation (%)
80
60
40
20
0 0
20
40
60
80
100
Water saturation (%)
100
Water saturation difference (%)
S wirr =
100
80
60
40
20
0 0
1
2
3
4
5
TOC (wt.%)
80
Water saturation difference (%)
If Sgasdiff value is relatively small, it suggests that the contribution to gas saturation is mainly from the effective pores of NMR T2 distribution. On the contrary, if Sgasdiff value is relatively large, it shows that the adsorbed gas in small pores or kerogen dominantly contributes to gas saturation. In this study, Sgasdiff ranges from 8% to 82%, most are more than 70%, which indicates that a larger proportion of gas is absorbed in pores of kerogen or dissolved in small pores. To further illustrate this problem, we construct two crossplots of gas saturation difference and clay bound water volume, kerogen volume shown in Fig. 7(e) and (f). Fig. 7(e) indicates that gas saturation difference decreases with the increasing clay bound volume, which suggests that NMR “clay water volume” is related to true clay water volume, and the gas saturation difference is little associated to the dissolve gas content. Fig. 7(f) shows that gas saturation difference increases with the kerogen volume rising and the linear relationship is very good. So, it is the kerogen volume that dominantly causes the saturation difference. Therefore, it is further pointed out that it is the adsorbed gas in the kerogen that contributes to this gas saturation rather than dissolved gas, and of course, the contribution of the adsorbed gas to gas saturation is greater than that of free gas. According to the basic principles of NMR and the mode of gas occurrence in organic shale, the water in organic shale is in bound status, and NMR T2 relaxation of kerogen and adsorbed gas are both very short (Sigal and Odusina, 2011). In order to analyze if T2 distributions of kerogen and adsorbed gas overlap together with clay bound water, we built an index
107
60
40
20
0 0
2
4
6
8
Kerogen (%) Fig. 8. Relationship between water saturation and NMR bin porosity. It indicates taht NMR bound water saturation includes true bound water as well as other fluids. Water saturation difference is proportional to both TOC content (a) and kerogen volume (b).
S water = 0.7965 R2 = 0.9019
ϕCBW ϕ + 0.1568 E . ϕNMR ϕNMR (10)
Otherwise, water saturation may be attained from correlation listed in Fig. 9,
108
M. Tan et al. / Journal of Petroleum Science and Engineering 136 (2015) 100–111
1.0
0.8
y=0.9863-0.9827x R2=0.9976 Component porosity (%)
Sgas(frac)
0.8
0.6
0.4
0.2
0.0 0.0
0.2
0.4
0.6
0.8
0.6
0.4
0.2
0.0 10
1.0
100
Swater (frac)
1000
10000 -1
Specific surface area (µ s )
Fig. 9. Correlation of water saturation and gas saturation of core plugs.
Micro
0.8
5
So, the fluid saturations evaluation of organic shale is completed by using some formulae above.
4
0.6 3
0.4 Methane molecule
2
0.2
Cumulative porosity (%)
(12)
Incremental porosity (%)
(11)
Thus, oil saturation can be obtained by the following formula:
Soil = 1 − S water − Sgas.
Macro
Incremental porosity Cumulative porosity
Sgas = 0. 9843 − 0. 9827S water . R 2 = 0. 9976
Meso
1
4. Pore structure of gas shale From the above Eq. (1), the fluid has long bulk relaxation time, and it contributes little to total T2 relaxation time. Moreover, in the low field NMR experiment, the magnetic field applied into the NMR instrument is uniform, so there is no diffusion relaxation (Xu and Torres-Verdín, 2013). If the pyrite volume of organic shale is little such as core H7 and H48, which could not induce internal magnetic field gratitude, the diffusion relaxation time may be ignored. Furthermore, the porous medium generally has large specific surface area with strong interactions between hydrogencontaining fluids (such as water, kerogen, and so on) and grains, and the surface relaxation is dominated in total T2 relaxation. So, the NMR porosity for such shale like H6 and H48 would approximate the real porosity. Therefore, Eq. (1) can be simplified as
1 S = ρ2 . T2 V
(13)
From Eq. (1), T2 relaxation time is related to the surface relaxivity ( ρ2) and the surface area to volume ratio (S/V). The surface relaxavity reflects transverse relaxation of protons, which is mainly related to minerals of the rock, and it is larger in classic carbonate than in classic rocks. Therefore, the echo decays quickly in classic rock, whereas it decays slowly in carbonate rocks. Ferromagnetic minerals such as chlorite and hematite have high magnetic susceptibility, which greatly accelerates the echo decaying. The size of pores also plays an important role in the T2 relaxation. Large pore has smaller S/V, less chance of collisions among the particles, and the T2 relaxation time is relatively longer; in contrast, small pore has larger S/V value, and the T2 relaxation time is much shorter. Therefore, NMR T2 relaxation of water-saturated rocks reflects substantial interaction between the fluid and surface of pores.
0.0 1E-4
0
1E-3
0.01
0.1
1
10
100
Pore throat radius (µ m) Fig. 10. NMR-based specific surface area and pore size distribution of shale. The specific surface area of shale is mainly in the range of 62.5–1960 μm 1 with an average value of about 9.8 μm 1 (a), and the pore radius averages is around 0.05 μm (b). These pores in such shale belong to micro-pores.
According to Eq. (13), NMR T2 spectrum in water-saturated rock mainly reflects the characteristics of the specific surface area (surface area to volume ratio) of the porous medium. The more developed fine pores in the rock, the larger the specific surface area, and the relaxation time will be shorter. Thus, according to NMR T2 experiments, the specific surface area from T2 distribution of shale can be a derived from Eq. (13), the specific surface area is
S 1 1 = ⋅ . V ρ2 T2
(14)
If the pore is spherical, S /V = 3/rc ; If the pore is ideal cylinder, S /V = 2/rc , where rc is the radius of pore. From the microscopic part of organic shale shown in Fig. 1, the pore geometric of shale can be regarded as cylinder. Therefore, Eq. (14) can be rewritten as
2 1 1 ≈ ⋅ . rC ρ2 T2
(15)
According to the results from Sondergeld et al. (2012), the transverse surface relaxivity ( ρ2 ) of shale is constant and about 0.051 μs/m. Therefore, the specific surface area distribution of H42 core was calculated from Eq. (13), which is shown in Fig. 10. The specific surface area of shale is mainly in the range of 62.5–1960 μm 1 with an average value of about 9.8 μm 1 (Fig. 10a), and the pore radius distribution is also calculated
M. Tan et al. / Journal of Petroleum Science and Engineering 136 (2015) 100–111
(Fig. 10b) with an average pore radius of around 0.05 μm. So, it seems that these pores in such shale belong to micro-pores, which agrees well with the descriptions from Sondergeld et al. (2012). Therefore, compared to other experimental methods, NMR experiment has higher efficiency and more explicit physical mechanism.
5. Case study The target formation of a case well is Haynesville Shale. In this well, Geophysical logs include: natural gamma ray (GR), spontaneous potential (SP), array induction logs (AIT), compensated acoustic log (DT), litho-density log (RHOB þPE), compensated neutron log (NPHI), natural gamma ray spectrometry logs including Uranium (U), Thorium (TH), and potassium (K), and CMR logs. At the same time, 49 cores samples were plugged in the target
109
organic shale, and the TOC measurement of core samples is made in the laboratory. To correct the NMR porosity, firstly, TOC content was predicted, which can be calculated from well logs by using Δ log R (Passey et al., 1990), experimental formula, or radial base function (RBF) method (Tan et al., 2013a, 2013b). Because the corrections of TOC and logs are all so poor, we used RBF method to calculate TOC content, named as TOCR, which is illustrated in Tack 5 of Fig. 11 and is better than that from experimental formula. Then, the kerogen volume was calculated by using the equation showed in Fig. 9(g), and the corrected NMR porosity was subsequently gained from Eqs. (2) and (3), named as TCMR. Next, the absolute permeability and water saturation are estimated from Eq. (4) and Eq. (10), respectively. All results are illustrated in Fig. 11. For verifying the interpretation results, another 3 core plugs at different positions were chosen for gas saturation measurement and Langmuir adsorption isotherm test. However, it is so regret
Fig. 11. NMR logs interpretation results of a case study including TOC content, NMR porosity, NMR permeability, and NMR saturation. They are in good agreement with the laboratory measurements of core plugs.
110
M. Tan et al. / Journal of Petroleum Science and Engineering 136 (2015) 100–111
differences between NMR and conventional porosity in shale were explained, and the corrected NMR porosity model is proposed after some influencing factors were analyzed. A specific permeability model based on NMR bin porosity was also built, and some saturation models of shale were also established. Some conclusions were drawn as follows:
Calculated gas saturation (Frac)
1.0
0.8
0.6
0.4
0.2
0.0 0
20
40
60
80
100
Measured gas storage capacity (scf/ton) Fig. 12. Relationship between the calculated gas saturation and gas storage capacity. It verifies the saturation calculation is correct.
that gas saturation of three cores were not measured successfully, and the gas storage capacity was obtained under formation pressure. Fig. 12 illustrates the relationship between gas storage capacity and the calculated gas saturation from NMR bin porosity. As is known to all, three states of gas in shale contribute to the gas storage capacity. The calculated gas saturation is mainly relative to free gas and desorb gas, and the higher the gas saturation, the larger the corresponding gas storage capacity, so both match well with echo other. From Fig. 12, the calculated gas saturation in three cores trend to the corresponding gas storage capacity, and this verifies the saturation calculation method is correct. Of course, it is not a strict proof but an auxiliary verification, and the laboratory measurement of gas saturation will be a strict proof for the saturation calculation model.
6. Discussions NMR technology can provide rich bin porosity information of rock, so it improves petrophysical interpretation models including porosity, permeability, and saturation evaluation. The case study also illustrates it is advantageous in the formation evaluation of shale. Furthermore, mud filtrate does not invade the organic shale, which is different from sandstone, and NMR depth of investigation is in the range of undisturbed shale. So, the NMR-based gas saturation method in organic shale is more applicable and more accurate than in other permeable formations. However, the T2 distribution of organic shale is still effected by kerogen and pyrite volumetric concentrations, and NMR porosity may be underestimated. Moreover, the T2 signals of absorbed gas and kerogen overlap together with clay bound water, and it is difficult to discriminate one from another. Therefore, two-dimensional NMR technology should be developed and applied into organic shale, which can not only observe T2 relaxation, but also measure the contribution of diffusion (Tan et al., 2013a, 2013b; Cao Minh et al., 2012; Chalmers and Bustin, 2008; Kausik et al., 2011), so it can greatly improve the fluid typing through integrated analysis of T2 relaxation and diffusion coefficient among oil, gas, and water. 7. Conclusions Based on NMR measurements of shale core plugs, NMR response characteristics of shale were investigated in detail, and the
(1) NMR T2 distributions of shale can be divided into two types: left peaks dominated discontinuous T2 distribution type (Type I) and continuous bimodal T2 distribution (Type II). The amplitude difference between the left and right peaks in Type I shale is more dominated than Type I shale. The T2 geometric mean of Type II is also larger, which indicates that different sizes of pores in Type II are developed and distribute relatively broadly. Moreover, the permeability of Type II is higher than that of Type I because of larger movable fluid volume. (2) NMR porosity of shale is usually lower than density porosity, and the porosity difference increases with TOC content rising. So, we deduce that the pyrite and kerogen leads to lower NMR porosity. (3) The permeability of shale is related to NMR effective porosity and NMR clay bound fluid volume rather than total porosity. The permeability model based on NMR effective porosity and clay bound water volume clearly reflects the penetration mechanism of fluids. (4) For the shale with a little pyrite, the measured NMR T2 distribution dominantly reflects the pore geometry. So, specific surface area can be calculated from NMR T2 distribution. For Haynesville Shale, specific surface area ranges mainly from 80.0 μm 1 to 1000 μm 1, and the pore radius averages about 0.05 μm. So, the pores of Haynesville Shale are classified as micro-pores. (5) Total gas contained in organic shale not only includes free gas in NMR movable fluid volume, but also the adsorbed gas in kerogen or pores among clays. NMR experiment indicates that the kerogen and adsorbed gas are located in clay bound water part of NMR T2 distribution, and they overlap together with clay bound water. Moreover, the adsorption gas contributes to total gas saturation more than free gas for Haynesville Shale.
Acknowledgments This paper is sponsored by the National Natural Science Foundation of China (41172130, U1403191), the Fundamental Research Funds for the Central Universities (2652015292), and National Major Projects “Development of Major Oil& Gas Fields and Coal Bed Methane (2011ZX05014-001). Furthermore, authors would like to acknowledge the support from the China Scholarship Council (No. 201206405002).
References Akkurt, R., Vingar, H.J.P., Tutunjian, N., Guillory, A.J., 1996. NMR logging of natural gas reservoirs. Log Anal. 7, 33–42. Arvie, D.M., Hill, R.J., Pollastro, R.M., 2005. Assessment of the Gas Potential and Yields from shales: The Barnett Shale Model: Oklahoma Geological Survey Circular. 110; , pp. 9–10. Cao Minh, M., Crary, S., Zielinski, L., Liu, C.B., Jacobsen, S., 2012. 2d-NMR application in unconventional reservoirs. SPE Paper 161578 Presented at the SPE Canadian Unconventional Resources Conferences, Calgary, Alberta, October 1–30, pp. 1– 17. Chalmers, G.R., Bustin, R.M., 2008. Lower Cretaceous gas shales in northeastern British Columbia, Part II: evaluation of regional potential gas resources. Bull. Can. Pet. Geol. 56, 22–61. Coats, G.R., Xiao, L.Z., Manfred, G.P., 1999. NMR Logging Principles and Applications. Sea Gulf Press, Houston, Texas. Curtis, J.B., 2002. Fractured shale-gas systems. AAPG Bull. 86, 1921–1938.
M. Tan et al. / Journal of Petroleum Science and Engineering 136 (2015) 100–111
Curtis, M.E., Sondergeld, C.H., Ambrose, R.J., Rai, C.S., 2012. Microstructural investigation of gas shales in two and three dimensions using nanometer-scale resolution imaging. AAPG Bull. 96, 665–667. Daniels, J., 2004. Evaluation of Hydrocarbon Generation and Storage in the Barnett Shale. Ft. Worth, Anonymous, Texas, p. 2004. Decker, A.D., Hill, D.G., Wicks, D.E., 1990. Log-based gas content and resource estimates for the Antrim shale, Michigan Basin. In: Proceedings of the Low Permeability Reservoirs Symposium, Denver, Colorado, April 26–28, 12p. Dunn, K.J.D., Bergman, J., LaTorraca, G.A., 2002. Nuclear Magnetic Resonance: Petrophysical and Logging Applications. Pergamon, an Imprint of Elsevier Science, p. 2002. Glorioso, J.C., Rattia, A., 2012. Unconventional reservoirs: basic petrophysical concepts for shale gas. SPE Paper 153004 Presented at the SPE/EAGE European Unconventional Resources Conference and Exhibition, Vienna, Austria, March 20–22, pp. 1–38. Guidry, F.K., Luffel, D.L., Olszewski, A.J., 1990. Devonian shale formation evaluation model based on logs, new core analysis methods, and production tests. In: Proceedings of the SPWLA 31th Annual Lagging Symposium, June 24–27, pp. 1– 12. Hook, P., Fairhurst, D., Rylander, E., Badry, B., Crary, N.S., Chatawanich K., Taylor, T., 2011. Improved precision magnetic resonance acquisition. SPE Paper 146883 Presented at the SPE Annual Technical Conference and Exhibition, Denver, Colorado, October 30–November 2, pp. 1–23. Kausik, R., Cao Minh, M., Zielinski, L., Vissapragada, B., Akkurt, R. Song, Y., Liu, C.B., Blair, E., 2011. Characterization of gas dynamics in kerogen nanopores by NMR. SPE Paper 147198 Presented at the SPE Annual Technical Conference and Exhibition, Denver, Colorado, October 30–November 2, pp. 1–16. Luffel, D.L., Guidry, F.K., Curtis, J.B., 1992. Evaluation of Devonian shale with new core and log analysis methods. J. Pet. Technol. 44, 1192–1197. Passey, Q.R., Creaney, S., Kulla, J.B., Moretti, F.J., Stroud, J.D., 1990. A practical model for organic richness from porosity and resistivity log. AAPG Bull. 74, 1777–1794. Rick, L.D., 2004. New evaluation techniques for gas shale reservoirs. In: Proceedings of the Reservoir Symposium, pp. 1–13. 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, 51–75.
111
Ross, D.J.K., Bustin, R.M., 2008. Characterizing the shale gas resource potential of Devonian–Mississippian strata in the Western Canada sedimentary basin: application of an integrated formation evaluation. AAPG Bull. 92, 87–125. Rylander, E., Jiang, T.M., Singer, P., Lewis, R., 2010. NMR T2 distribution in the Eagle Ford Shale: Reflections on Pore Size. SPE Paper 164554 Presented at the SPE Unconventional Gas Conference and Exhibition, Pittsburgh, Pennsylvania, February 23–25, pp. 1–15. Sigal, R.F., Odusina, E., 2011. Laboratory NMR measurement on methane saturated Barnett shale sample. Petrophysics 52, 1–10. 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, pp. 1–17. Tan, M.J., Liu, Q., Zhang, S.Y., 2013a. A dynamic adaptive radial basis function approach for the total organic carbon content prediction in organic shale. Geophysics 50 (6), 445–459. Tan, M.J., Zou, Y.L., Zhou, C.C., 2013b. A new inversion method for (T2, D) 2D NMR logging and fluid typing. Comput. Geosci. 51, 366–380. Xu, C.C., Torres-Verdín, C., 2013. Quantifying fluid distribution and phase connectivity with a simple 3D cubic pore network model constrained by NMR and MICP data. Comput. Geosci. 61, 94–103. Yang, Q.S., Torres-Verdín, C., 2013. Joint stochastic interpretation of conventional well logs acquired in hydrocarbon-bearing shale. In: Proceedings of the SPWLA 54th Annual Logging Symposium, New Orleans, Louisiana, June 22–26, pp. 1–15.
Maojin Tan is a professor in the China University of Geosciences (Beijing). He was granted Ph.D. in 2006, and worked as a postdoctoral fellow in Geophysics from 2006 to 2008, in the Graduate University of Chinese Academy of Sciences. His research interests focus on geophysical well logging with emphasis on new logging technologies and formation evaluation of complex reservoirs. So far, he has published 36 papers in technical journals such as “Geophysics”, “Journal of Applied Geophysics”, and “Computer and Geosciences”.
Contents lists available at ScienceDirect
Journal of Petroleum Science and Engineering journal homepage: www.elsevier.com/locate/petrol
NMR petrophysical interpretation method of gas shale based on core NMR experiment Maojin Tan a,b,n, Keyu Mao c, Xiaodong Song b, Xuan Yang c, Jingjing Xu b a
Key Laboratory of Geo-detection of Ministry of Education, China University of Geosciences, Beijing 100083, China School of Geophysics and Information Technology, China University of Geosciences, 29 Xueyuan Road, Haidian District, Beijing 100083, China c Sinopec Oilfield Service Corporation Shengli Well Logging Company, Dongying 257096, China b
art ic l e i nf o
a b s t r a c t
Article history: Received 10 June 2015 Received in revised form 1 November 2015 Accepted 2 November 2015 Available online 4 November 2015
Nuclear magnetic resonance (NMR) characteristics provide critical information to study the storage of organic shale. Based on NMR petrophysical experiment of Haynesville Shale, the response characteristics of transverse relaxation time (T2) are analyzed. According to the shape and amplitude of T2 distribution of shale, Haynesville Shale can be categorized into two types: continuous and discontinuous T2 spectrums. Compared with routine density porosity, NMR porosity is generally underestimated for organic shale. Therefore, the controlling factors are investigated in detail. Through constructing the correlations of NMR porosity and clay volume, total organic carbon (TOC) content, kerogen content and pyrite volumetric concentration, the pyrite and kerogen are finally regarded as dominant factors. Subsequently, a new NMR porosity correction model is proposed. Moreover, the permeability model based on NMR bin porosity is built, and water, gas and oil saturation models are also established through building some correlations between gas saturation and NMR bin porosity. NMR experiment indicates that the kerogen and adsorbed gas are located in bound fluid part of NMR T2 distribution, and the gas storage is dominant as absorbed gas rather than free gas. The study provides a novel petrophysical interpretation method for gas-bearing shale. & 2015 Elsevier B.V. All rights reserved.
Keywords: Gas shale Nuclear magnetic resonance (NMR) T2 distribution Kerogen Gas saturation Petrophysical interpretation method
1. Introduction Gas-bearing shale is one of the most valuable unconventional reservoirs. Comparing with conventional sandstone resourses, the mineralogy of shale gas reservoir is extremely complex, and its pore space mostly includes both sub-micron and nano-scale matrix pore, sometimes, relatively large aperture fractures are developed (Curtis, 2002; Rick, 2004; Glorioso and Rattia, 2012; Sondergeld et al., 2012). In gas-bearing shale, occurrence of gas is complex, gas is partly free in matrix and kerogen, and partly absorbed on the surface of pores and in kerogen. A small portion of gas is dissolved in fluids of shale (Sondergeld et al., 2012). So, organic shale is a typical “self-generating and self-storage” system (Curtis et al., 2012). Routine petrophysical experiment is the most effective method to study the properties of organic shale, which can measure resistivity, porosity, permeability, saturation, and is also an n Corresponding author at: School of Geophysics and Information Technology, China University of Geosciences, 29 Xueyuan Road, Haidian District, Beijing 100083, China. E-mail address: [email protected] (M. Tan).
http://dx.doi.org/10.1016/j.petrol.2015.11.007 0920-4105/& 2015 Elsevier B.V. All rights reserved.
indispensable bridge between logs response and logs interpretation (Luffel et al., 1992; Guidry et al., 1990; Ross and Bustin, 2007, 2008; Xu and Torres-Verdín, 2013; Yang and Torres-Verdín, 2013). In recent years, Nuclear magnetic resonance (NMR) is only sensitive to hydrogen fluids in pores of saturated rocks, and has been widely used in petrophysical experiments and geophysical logging (Coats et al., 1999; Dunn et al., 2002). NMR instrument can not only measure the total porosity, movable fluid volume, and bound fluid volume, it can even calculate the permeability based on NMR bin porosity. Furthermore, NMR experimental results of core plugs can be used to calibrate NMR logging. In last two decades, core NMR experiment and NMR logging were extensively applied to sandstone and carbonate reservoirs, some experimental results of core plugs can provide the guidance for logs interpretation and help to construct the petrophysical models (Rick, 2004; Luffel et al., 1992; Decker et al., 1990; Arvie et al., 2005; Passey et al., 1990; Akkurt et al., 1996), which indicates that NMR applications in conventional reservoirs were successful. Unfortunately, NMR core measurement and NMR logging were not fully utilized in gas shales. So far, NMR logging technique for natural gas was mainly on the bulk and unrestricted gas (Akkurt et al., 1996; Daniels, 2004;
M. Tan et al. / Journal of Petroleum Science and Engineering 136 (2015) 100–111
101
Hook et al., 2011). Recently, Sigal and Odusina (2011) published NMR laboratory work on methane-saturated cores from Barnett Shale. The limitation remains with overlapping T2 signals between water and gas/oil in the complex pore systems, which makes it difficult to separate different fluids from the T2 distribution. Rylander et al. (2010) studied the NMR T2 distribution in the Eagle Forward Shale, and analyzed the NMR characteristics of bound oil and free oil, and compared the difference of lower and upper Eagle ford Shale. In this study, based on NMR core experiment of Haynesville Shale, we analyzed the NMR T2 response characteristics. Then, we compared NMR porosity with routine density porosity, analyzed the influence factors, and investigated contributions of free gas, kerogen, adsorbed gas to T2 distribution. Finally, we established NMR petrophysical interpretation models including corrected NMR porosity model, permeability model, and saturation models. Moreover, the specific area and throat radius are estimated from NMR T2 distribution. Therefore, this NMR experiment of shale core plugs provides an important alternate for log interpretations of gas-bearing shale.
2. NMR T2 relaxation mechanism 2.1. Petrophysical model of organic shale Organic shale has complex pore types, and the pore morphology is also diverse. According to different types of shales, the reservoir space can be divided into two types including matrix pore and fracture, and the former includes intergranular pore, intragranular pores, and voids within the organic plasmids (Fig. 1), which is mostly nano-level. The connectivity between these voids is so poor (Fig. 1a, Curtis et al., 2010). In gas-bearing shale, occurrence of gas is various, gas is partly free in matrix and kerogen, and some are absorbed on the surface of pores and in kerogen (Fig. 1b). A small portion of gas is dissolved in fluids of shale (Kausic et al., 2011). Organic shale is rock matrix and fluids. The rock matrix usually consists of dry clay, quartz, feldspar, plagioclase, calcite, pyrite and kerogen. For Haynesville Shale, X-ray diffraction (XRD) results of shale are shown as below (Fig. 2a), which demonstrates that the clay content is dominating. So, the petrophysical model is set as Fig. 2(b). 2.2. NMR T2 relaxation mechanism As we all know, NMR is the interaction between the hydrogen nucleus and the magnetic field. Total NMR relaxation includes surface relaxation, bulk relaxation of fluid precession, and diffusion relaxation caused by gradient field. Therefore, total NMR relaxation in porous media can be described by the following mathematical formula:
1 S 1 D (Gγ TE) 2 = ρ2 + + T2 V T2b 12 G = Gexternal + Ginternal , Δχ Ginternal ≈ B0 r
(1)
where ρ2 is the surface relaxivity, μs/m; D is the free diffusion coefficient for the fluid, μs/cm2; γ is the gyromagnetic ratio; TE is the echo spacing of the measurement sequence, ms; and S/V is the surface area to volume ratio (specific surface area) of the pores, μs 1; G is the magnetic field gradient, which includes the external and interior magnetic field gradient, Gauss/cm; B0 is the applied magnetic field strength, Gauss/cm; r refers to the distance from
Fig. 1. All kinds of pores in organic shale (a) and gas occurrences in different pores (b).
the magnetic field changes, m; Δχ is the susceptibility difference between rock matrix particles and pore fluid, 10 6 SI. From Eq. (1), total T2 relaxation time is related to specific surface area, fluids in rocks as well as diffusion coefficient and echo spacing. Usually, the measurement of NMR experiment is under uniform magnetic field, and the diffusion relaxation term could be ignored, so the specific surface area can reflect the pore size distribution in the sandstone and carbonate formations. But, for organic shale, the total clay content is so high, and small pores or throats may induce an internal magnetic gradient in terms of Eq. (1) even though in homogenous magnetic field. So, it will also decrease the T2 relaxation time and make T2 spectrum shift leftward. The pattern and amplitude of NMR T2 distribution reflect the characteristics of different reservoirs. Generally, the higher amplitude of T2 distribution at short relaxation time indicates larger bound fluid volume, whereas, the higher amplitude of T2
102
M. Tan et al. / Journal of Petroleum Science and Engineering 136 (2015) 100–111
5.9% 4.9%
2.8%4.7%
Total Clay Quartz
0.3%
K Feldspar
47.5%
Plagioclase 34.2%
Calcite Pyrite Kerogen
the temperature is about 28 °C. The equipment used is MARANDRX, which is a low-field NMR instrument with uniform magnitude field. The number of echoes is 1024 and the echo spacing is 0.2 ms. The number of scanning and stacking is 128. Because little fractures were observed in these shale cores, the waiting time of NMR experiments was set as 5.0 s through comparison of testing. In addition, some routine experimental measurements such as density porosity and permeability, are carried out, and total organic carbon content (TOC) and kerogen volume were measured in core plugs. The porosity and matrix permeability were calculated from measured pressure-decay data using a fresh crushed (20/35 mesh size) sample including all interconnected pore space. Gas saturations were determined by Dean-Stark technique using a crushed sample and dried at 110°, and oil saturation was computed using an ambient oil density of 0.80 g/cm3; water saturation was computed using a brine concentration of 30,000 ppm with an ambient density of 1.018 g/cm3. The clay minerals content was from X-ray diffraction (XRD) analysis. Table 1 shows NMR and routine experimental results of 11 shale core plugs, including NMR porosity, clay bound water volume, effective porosity, density porosity, air permeability, and TOC content. T2 geometric mean value was calculated. The NMR porosity of 11 cores averages about 4.35% and the routine density porosity is about 5.70%, so, the former is lower than the latter. The measured air permeability is in the range of (0.07–816) 103nd, with an average of 268.7 103nd. The TOC content ranges from 0.45 wt% to 2.69 wt%. 3.2. NMR T2 distribution of gas shale
Fig. 2. Mineral compositions and petrophysical model in different pores.
distribution at long relaxation demonstrates larger movable fluid volume.
3. NMR characteristics and interpretation method in shale 3.1. NMR experiments of core plugs In our experiment, 11 core plugs of Haynesville Shale were selected from the drilling cores and stored for laboratory measurement. The size of all core samples is about 1.0 in. diameter, and the bulk volume ranges from 13.53 cm3 to 33.02 cm3, which is listed in Table 1. The measurement pressure is about 800 psi, and
T2 distribution of water-saturated conventional rocks such as sandstone and carbonate usually describes pore sizes distribution. The more developed the large pores in reservoir rock are, the stronger the NMR T2 spectrum signal at the long relaxation time will be; instead, if the small pores in the rock are poorly developed, the NMR T2 spectrum at short relaxation time is dominated. Thus, according to the characteristics of T2 spectrum, we can analyze quantitatively the storage and flow properties of fluids in shale. Fig. 3 shows the NMR T2 distributions of 11 shale core samples. In terms of the characteristic of T2 spectrum and morphology, NMR T2 distributions of shale cores are categorized into two types: continuous bimodal T2 spectrum (Type I) and discontinuous bimodal T2 spectrum (Type II). Fig. 3(a) shows that NMR T2 distribution (H6) has a significant unimodal, and the left peak of T2 spectral is much more significant than the right one, whose corresponding relaxation time is about 1 ms. Moreover, the right part of T2 spectrum has much lower amplitude with the relaxation time ranging from about 8 ms to 80 ms. Generally, the whole T2 distribution is discontinuous
Table 1 NMR experiment results and conventional petrophysical measurement. No. Sample ID Bulk volume of sample (cm3)
1 2 3 4 5 6 7 8 9 10 11
H6 H7 H9 H17 H19 H21 H29 H33 H39 H42 H48
29.44 28.90 29.03 31.61 33.02 21.45 24.36 13.53 29.73 29.61 31.33
NMR data
Conventional data
Porosity (%) Clay bound water volume Effective porosity (%) (%)
T2 log mean (ms)
Porosity Permeability 103nd TOC (wt%)
6.7 6.9 7.2 2.5 3.8 5.4 4.5 3.5 3.3 2.2 1.9
0.3 0.3 0.3 1.2 0.4 0.3 0.3 0.5 0.3 0.8 0.5
6.27 6.57 6.57 5.96 5.92 6.65 6.69 7.60 5.52 2.80 2.12
6.6 6.9 7.1 1.8 3.4 5.1 4.2 2.9 3.1 1.8 1.7
0.1 0.1 0.1 0.7 0.4 0.3 0.3 0.4 0.2 0.4 0.2
2.25 0.07 1.52 790 75.9 190 346 816 589 130 15.1
0.87 0.45 0.91 2.67 2.31 2.14 2.36 2.69 1.77 0.95 0.61
M. Tan et al. / Journal of Petroleum Science and Engineering 136 (2015) 100–111
10
0.5 H6 H7 H9
8
NMR porosity (%)
Incremental porosity(%)
0.4
0.3
0.2
0.1
0.0 0.01
0.1
1
10
100
1000
4
0.4
0.3
0.2
0.1
1
2
4
6
8
10
Core porosity (%) H17 H19 H21 H29 H33 H39 H42 H48
0.1
0 0
0.5
Icremental porosity(%)
6
2
T2 (ms)
0.0 0.01
103
10
100
1000
T2 (ms) Fig. 3. NMR response characteristics of shale. Shale is classified into two types: Type I (a) and Type II (b).
with logarithmic mean of about 0.3 ms. In this NMR experiment, three of the 11 cores are classified as Type I. Different from NMR T2 spectrum of Type I, NMR T2 spectrum of Type II (H12) is continuous, and the amplitude difference between left and right peaks is much smaller than that of Type I shale (Fig. 3b). The relaxation time of left peak ranges from about 0.8 ms to 2.0 ms; in contrast, the relaxation time of right peak ranges from about 2.0 ms to 200 ms with wider distribution. It reflects that the latter has a relatively wider pore size than the former, which indicates that some relatively large pores, fractures, and small caves can be well developed. Furthermore, from Table 1, the permeability of Type II shale is also much higher than Type I shale. 3.3. Analysis on NMR porosity Theoretical studies of NMR suggest that, for hydrogen-containing fluid saturated rock, NMR measures the relaxation signal from the fluid rather than matrix, so NMR measurement can provide accurate porosity estimation independent of mineralogy (Coats et al., 1999). Experimental study from a large number of scholars confirmed that NMR porosity is accurate in sandstone and carbonate reservoirs, and the absolute error between NMR porosity and density porosity is generally less than l.0% (Dunn et al., 2002). So, the measured NMR porosity is usually the same as routine density porosity.
Fig. 4. Comparison of NMR porosity and routine density porosity of shale. NMR porosity of some cores is worse underestimated.
Fig. 4 shows comparison of NMR porosity and routine density porosity of shale, which indicates that NMR porosity of some cores with medium porosity is underestimated. To investigate the influence factors, we built some crossplots (Fig. 5). Fig. 5(a) shows the correlation of NMR porosity and clay minerals content, which indicates that the fine clay pore in shale increases porosity rather than decreasing porosity even though the internal magnetic field gradient is induced by micro-pore in the clay. Therefore, the clay mineral's content is not the main reason causing NMR porosity reduction. To investigate this problem, we calculated the porosity difference between the density porosity and NMR porosity. Fig. 5 (b) and (c) shows the correlations of the porosity difference and clay content, and gas saturation, which indicates neither the clay nor fluid in gas shale is one of the impact factors. Fig. 5(d) shows that, for these cores whose NMR porosity is lower than density porosity, NMR porosity decreases with increasing clay content, but the porosity difference increases (Fig. 5e). Moreover, Fig. 5 (f) illustrates that TOC content also increases with the pyrite content rising, which indicates NMR porosity is related to pyrite, and that is, NMR porosity decreases with the increasing pyrite. Furthermore, Fig. 5(g) and (h) also illustrates that the kerogen volume is related to the pyrite content, which further verifies that the pyrite is associated with the kerogen. As we all know, pyrite is rich in iron (Fe) and has high relaxavity, which will accelerate NMR echo decaying, so, it is difficult to capture the fluid signal in NMR experiments of shale cores, and the measured porosity is sure to be lower. Therefore, the pyrite or other ferromagnetic minerals contained in organic shale mainly contributed to the decreasing porosity. To correct the NMR porosity, it is possible to build the correction,
ϕNMR, Corr = ϕNMR + ϕdifference
(2)
where ϕNMR, Corr is the corrected porosity, % and ϕdifference is the porosity difference between NMR porosity and core density porosity, which can be gained from Fig. 5(e) as
ϕdifference = 1.498⋅TOC − 0.8824 R2 = 0.7309, where TOC is the total organic carbon content, wt%.
(3)
M. Tan et al. / Journal of Petroleum Science and Engineering 136 (2015) 100–111
10
5
8
4
Porosity difference (%)
NMR porosity (%)
104
6
4
2
3
2
1
0
0 0
10
20
30
40
0
50
10
5
10
4
8
NMR porosity (%)
Porosity differnce (%)
20
30
40
50
4
5
Clay content (%)
Clay content (%)
3
2
1
6
no porosity difference
4
2
0 0
20
40
60
0
80
0
1
Gas saturation (%)
2
3
Clay content (%)
3
5
2
TOC (wt.%)
Porosity difference (%)
4
1
3
2
1
0
0
0
1
2
3
4
5
0
1
TOC (wt.%)
3
4
5
3
4
5
Pyrite (%)
8
5 y=0.4459x-0.0119 R =0.9994
4
6
3
Kerogen (%)
TOC (wt.%)
2
2
4
2
1
0
0
0
2
4
Kerogen (%)
6
8
0
1
2
Pyrite (%)
Fig. 5. Crossplots for analysis on impact factors of NMR porosity. NMR porosity increases with the clay content rising (a); porosity difference is not related to clay content (b) and gas saturation (c); NMR porosity decreases with TOC content rising when TOC larger than 1.0 wt% (d), and porosity difference increases with TOC content rising (e). In addition, TOC content is proportional to pyrite volume (f) and kerogen volume (g), and kerogen is negative related to pyrite volume (h).
10000
10000
1000
1000 Permeability (1000xnd)
Permeability (1000xnd)
M. Tan et al. / Journal of Petroleum Science and Engineering 136 (2015) 100–111
100
10
1
100
10
1
0.1
0.1
0.1
1
10
0.1
1
10
Core density porosity (%)
10000
10000
1000
1000
Permeability (1000xnd)
Permeability (1000xnd)
Porosity (%)
100
10
1
100
10
1
0.1
0.1 0.01
105
0.1
1
0.1
Effective Porosity (%)
1
10
Clay water volume (%)
Fig. 6. Relationships between permeability and NMR component porosities. The permeability is worse related to NMR porosity (a) and routine density porosity (b), but it is positively related to NMR effective porosity (c) and negatively related to clay water volume (d).
3.4. Permeability model from NMR bin porosity The permeability is an important parameter for deliverability evaluation of gas-bearing shale. Typically, the permeability of shale is so small, and it is difficult to predict accurately. NMR measurements can provide some component porosities, such as effective porosity, clay bound water volume, and free fluid volume, which is the advantage of NMR measurement over the routine laboratory measurement. Therefore, constructing the relationship between permeability and NMR component porosities may provide a good alternative way for the permeability prediction of shale. Fig. 6 shows the crossplots of permeability and routine density porosity, and NMR porosity. These correlations are both poor, which indicates that the permeability does not tend to increase with both porosities increasing (Fig. 6a and b). On the right-bottom of Fig. 6(a), three cores with large porosity have extremely low permeability, which indicates that this type of pores has no contribution to permeability of organic shale even though they have high porosity. For these fine pores, they could be isolated in kerogen or among mineral particles, and not connected with the outside, which hinders the penetration of the fluid, so they cannot elevate the permeability of shale. These three cores are verified as H6, H7 and H9 shown in Table 1, and they belong to Type I shale. Their bound fluid volumes are extremely high; their movable fluid volumes are extremely small, and TOC content is so low. It indicates that the water in pores among fine clay particles in such shale is absolutely irreducible and affects the permeability. For this, we build the correlations of permeability and NMR effective porosity, and clay bound water volume, which are illustrated in
Fig. 6(c) and (d), respectively. The absolute permeability is proportional to NMR effective porosity and it decreases with the bound fluid volume increasing. Therefore, in terms of Fig. 6(c) and (d), we built the following formula to calculate the absolute permeability of shale by data fitting method:
⎛ ϕ ⎞1.79 KNMR = 89.35 ⎜ E ⎟ , ⎝ ϕCBW ⎠ R2 = 0.8091
(4)
where ϕCBW is the clay water volume, %; ϕE is NMR effective porosity, %; and KNMR is absolute permeability from NMR measurement, 10 6 μm2. 3.5. Saturation model from NMR bin porosity Except for NMR, TOC, kerogen, and others measurements, gas saturation was measured for 11 core plugs of Haynesville Shale, and the relationship between gas saturation and various NMR bin porosities is established, which can be used to study the mode of occurrence of shale gas. First, we built crossplots of gas saturation and NMR porosity, and total porosity, but the correlations are both so poor. However, the correlations of gas saturation and NMR effective porosity, TOC content, and kerogen volume are so excellent (Fig. 7). Fig. 7(a) indicates that the gas saturation increases with NMR effective porosity rising. Moreover, Fig. 7(b) and (c) indicates gas saturation has positive correlations with TOC content and kerogen volume, which means that natural gas of organic shale is related to kerogen. As we know, free gas, and adsorbed gas or dissolved gas all contribute to total gas
M. Tan et al. / Journal of Petroleum Science and Engineering 136 (2015) 100–111
80
80
60
60
Gas saturation (%)
Gas saturation (%)
106
40
20
40
20
0
0 1E-3
0.01
0.1
0
1
1
2
80
60
60
Gas saturation (%)
Gas saturation (%)
80
40
20
0 0
2
4
4
5
6
40
20
0
8
0
20
Kerogen (%)
40
60
80
Effective porosity index (%)
80
80
Difference of gas saturation (%)
Difference of gas saturation (%)
3
TOC (wt.%)
Free fluid volume (%)
60
40
20
60
40
20
0
0 0
20
40
60
80
0
2
Clay content (%)
4
6
8
10
Kerogen (%)
Fig. 7. Relationships between gas saturation and NMR porosity, TOC content, and kerogen volume. Gas saturation is positively related to NMR porosity (a), TOC content (b) and kerogen volume (c).
saturation, but what are the response characteristics of these gas components above in NMR measurement? In order to study the contribution of NMR effective porosity to gas saturation, we construct an index ( Seff , NMR ), namely, NMR effective porosity index, which indicates the proportion of NMR effective porosity volume throughout the pore space
Seff , NMR =
ϕE , ϕNMR
(5)
where ϕNMR is the NMR porosity, % and ϕE is the NMR effective porosity, %. Fig. 7(d) shows the relationship between gas saturation and NMR effective porosity index, which indicates that the measured
gas saturation is higher than NMR effective porosity index. This clearly shows that NMR effective porosity only includes a little amount of free gas with long relaxation time, and most of gas is from “clay bound water” part of T2 distribution with fast relaxation. To further analyze the relationship between gas saturation and NMR bin porosity, we construct another parameter, Sgasdiff , which is named as gas saturation difference between really measured gas saturation and NMR effective porosity index
Sgasdiff = Sgas − Seff , NMR,
(6)
where ϕNMR is the NMR porosity, % and Seff , NMR is the NMR effective porosity index, %.
M. Tan et al. / Journal of Petroleum Science and Engineering 136 (2015) 100–111
ϕCBW , ϕNMR
(7)
where ϕCBW is the NMR “clay bound water” volume, %; and Swirr is the NMR “clay bound water” or irreducible water saturation, %. Fig. 8(a) is the crossplot of the measured water saturation, Swater , and Swirr . As can be seen, Swater is far lower than Swirr , which shows that NMR “clay bound water” volume also includes other fluids besides true clay water. To this end, we construct a parameter, Swaterdiff , named as fluid saturation difference, which describes the difference between Swirr and Swater ,
S waterdiff = S wirr − S water ,
(8)
where Swaterdiff is the water saturation difference, %. Fig. 8(b) and (c) are crossplots of Swaterdiff and TOC content, and kerogen volume, respectively. The water saturation difference increases with TOC content and kerogen volume rising, and especially, it has an excellent linear relationship with kerogen volume. This proves the kerogen or adsorbed gas in kerogen is located in short relaxation part of T2 distribution, and their NMR T2 distributions overlap together with that of NMR “clay bound water” volume. Therefore, NMR “clay bound water” in T2 distribution includes the contributions of both kerogen and its absorbed gas. Therefore, based on the above analysis, NMR response in organic shale includes movable fluid, that is, free gas, true clay bound water, kerogen, and its absorbed gas. Therefore, we built a gas saturation model based on NMR bin porosity
Sgas = − 0.5624 R2 = 0.9938
ϕCBW ϕ + 0.5551 E + 14.35Vkero, ϕNMR ϕNMR (9)
where Vkero is the kerogen volume, %. Since there is no movable water in organic shale, and the water comes from bound fluid part of T2 distribution with fast relaxation time. Therefore, we constructed a water saturation formula as follows:
"Bound fluid" saturation (%)
80
60
40
20
0 0
20
40
60
80
100
Water saturation (%)
100
Water saturation difference (%)
S wirr =
100
80
60
40
20
0 0
1
2
3
4
5
TOC (wt.%)
80
Water saturation difference (%)
If Sgasdiff value is relatively small, it suggests that the contribution to gas saturation is mainly from the effective pores of NMR T2 distribution. On the contrary, if Sgasdiff value is relatively large, it shows that the adsorbed gas in small pores or kerogen dominantly contributes to gas saturation. In this study, Sgasdiff ranges from 8% to 82%, most are more than 70%, which indicates that a larger proportion of gas is absorbed in pores of kerogen or dissolved in small pores. To further illustrate this problem, we construct two crossplots of gas saturation difference and clay bound water volume, kerogen volume shown in Fig. 7(e) and (f). Fig. 7(e) indicates that gas saturation difference decreases with the increasing clay bound volume, which suggests that NMR “clay water volume” is related to true clay water volume, and the gas saturation difference is little associated to the dissolve gas content. Fig. 7(f) shows that gas saturation difference increases with the kerogen volume rising and the linear relationship is very good. So, it is the kerogen volume that dominantly causes the saturation difference. Therefore, it is further pointed out that it is the adsorbed gas in the kerogen that contributes to this gas saturation rather than dissolved gas, and of course, the contribution of the adsorbed gas to gas saturation is greater than that of free gas. According to the basic principles of NMR and the mode of gas occurrence in organic shale, the water in organic shale is in bound status, and NMR T2 relaxation of kerogen and adsorbed gas are both very short (Sigal and Odusina, 2011). In order to analyze if T2 distributions of kerogen and adsorbed gas overlap together with clay bound water, we built an index
107
60
40
20
0 0
2
4
6
8
Kerogen (%) Fig. 8. Relationship between water saturation and NMR bin porosity. It indicates taht NMR bound water saturation includes true bound water as well as other fluids. Water saturation difference is proportional to both TOC content (a) and kerogen volume (b).
S water = 0.7965 R2 = 0.9019
ϕCBW ϕ + 0.1568 E . ϕNMR ϕNMR (10)
Otherwise, water saturation may be attained from correlation listed in Fig. 9,
108
M. Tan et al. / Journal of Petroleum Science and Engineering 136 (2015) 100–111
1.0
0.8
y=0.9863-0.9827x R2=0.9976 Component porosity (%)
Sgas(frac)
0.8
0.6
0.4
0.2
0.0 0.0
0.2
0.4
0.6
0.8
0.6
0.4
0.2
0.0 10
1.0
100
Swater (frac)
1000
10000 -1
Specific surface area (µ s )
Fig. 9. Correlation of water saturation and gas saturation of core plugs.
Micro
0.8
5
So, the fluid saturations evaluation of organic shale is completed by using some formulae above.
4
0.6 3
0.4 Methane molecule
2
0.2
Cumulative porosity (%)
(12)
Incremental porosity (%)
(11)
Thus, oil saturation can be obtained by the following formula:
Soil = 1 − S water − Sgas.
Macro
Incremental porosity Cumulative porosity
Sgas = 0. 9843 − 0. 9827S water . R 2 = 0. 9976
Meso
1
4. Pore structure of gas shale From the above Eq. (1), the fluid has long bulk relaxation time, and it contributes little to total T2 relaxation time. Moreover, in the low field NMR experiment, the magnetic field applied into the NMR instrument is uniform, so there is no diffusion relaxation (Xu and Torres-Verdín, 2013). If the pyrite volume of organic shale is little such as core H7 and H48, which could not induce internal magnetic field gratitude, the diffusion relaxation time may be ignored. Furthermore, the porous medium generally has large specific surface area with strong interactions between hydrogencontaining fluids (such as water, kerogen, and so on) and grains, and the surface relaxation is dominated in total T2 relaxation. So, the NMR porosity for such shale like H6 and H48 would approximate the real porosity. Therefore, Eq. (1) can be simplified as
1 S = ρ2 . T2 V
(13)
From Eq. (1), T2 relaxation time is related to the surface relaxivity ( ρ2) and the surface area to volume ratio (S/V). The surface relaxavity reflects transverse relaxation of protons, which is mainly related to minerals of the rock, and it is larger in classic carbonate than in classic rocks. Therefore, the echo decays quickly in classic rock, whereas it decays slowly in carbonate rocks. Ferromagnetic minerals such as chlorite and hematite have high magnetic susceptibility, which greatly accelerates the echo decaying. The size of pores also plays an important role in the T2 relaxation. Large pore has smaller S/V, less chance of collisions among the particles, and the T2 relaxation time is relatively longer; in contrast, small pore has larger S/V value, and the T2 relaxation time is much shorter. Therefore, NMR T2 relaxation of water-saturated rocks reflects substantial interaction between the fluid and surface of pores.
0.0 1E-4
0
1E-3
0.01
0.1
1
10
100
Pore throat radius (µ m) Fig. 10. NMR-based specific surface area and pore size distribution of shale. The specific surface area of shale is mainly in the range of 62.5–1960 μm 1 with an average value of about 9.8 μm 1 (a), and the pore radius averages is around 0.05 μm (b). These pores in such shale belong to micro-pores.
According to Eq. (13), NMR T2 spectrum in water-saturated rock mainly reflects the characteristics of the specific surface area (surface area to volume ratio) of the porous medium. The more developed fine pores in the rock, the larger the specific surface area, and the relaxation time will be shorter. Thus, according to NMR T2 experiments, the specific surface area from T2 distribution of shale can be a derived from Eq. (13), the specific surface area is
S 1 1 = ⋅ . V ρ2 T2
(14)
If the pore is spherical, S /V = 3/rc ; If the pore is ideal cylinder, S /V = 2/rc , where rc is the radius of pore. From the microscopic part of organic shale shown in Fig. 1, the pore geometric of shale can be regarded as cylinder. Therefore, Eq. (14) can be rewritten as
2 1 1 ≈ ⋅ . rC ρ2 T2
(15)
According to the results from Sondergeld et al. (2012), the transverse surface relaxivity ( ρ2 ) of shale is constant and about 0.051 μs/m. Therefore, the specific surface area distribution of H42 core was calculated from Eq. (13), which is shown in Fig. 10. The specific surface area of shale is mainly in the range of 62.5–1960 μm 1 with an average value of about 9.8 μm 1 (Fig. 10a), and the pore radius distribution is also calculated
M. Tan et al. / Journal of Petroleum Science and Engineering 136 (2015) 100–111
(Fig. 10b) with an average pore radius of around 0.05 μm. So, it seems that these pores in such shale belong to micro-pores, which agrees well with the descriptions from Sondergeld et al. (2012). Therefore, compared to other experimental methods, NMR experiment has higher efficiency and more explicit physical mechanism.
5. Case study The target formation of a case well is Haynesville Shale. In this well, Geophysical logs include: natural gamma ray (GR), spontaneous potential (SP), array induction logs (AIT), compensated acoustic log (DT), litho-density log (RHOB þPE), compensated neutron log (NPHI), natural gamma ray spectrometry logs including Uranium (U), Thorium (TH), and potassium (K), and CMR logs. At the same time, 49 cores samples were plugged in the target
109
organic shale, and the TOC measurement of core samples is made in the laboratory. To correct the NMR porosity, firstly, TOC content was predicted, which can be calculated from well logs by using Δ log R (Passey et al., 1990), experimental formula, or radial base function (RBF) method (Tan et al., 2013a, 2013b). Because the corrections of TOC and logs are all so poor, we used RBF method to calculate TOC content, named as TOCR, which is illustrated in Tack 5 of Fig. 11 and is better than that from experimental formula. Then, the kerogen volume was calculated by using the equation showed in Fig. 9(g), and the corrected NMR porosity was subsequently gained from Eqs. (2) and (3), named as TCMR. Next, the absolute permeability and water saturation are estimated from Eq. (4) and Eq. (10), respectively. All results are illustrated in Fig. 11. For verifying the interpretation results, another 3 core plugs at different positions were chosen for gas saturation measurement and Langmuir adsorption isotherm test. However, it is so regret
Fig. 11. NMR logs interpretation results of a case study including TOC content, NMR porosity, NMR permeability, and NMR saturation. They are in good agreement with the laboratory measurements of core plugs.
110
M. Tan et al. / Journal of Petroleum Science and Engineering 136 (2015) 100–111
differences between NMR and conventional porosity in shale were explained, and the corrected NMR porosity model is proposed after some influencing factors were analyzed. A specific permeability model based on NMR bin porosity was also built, and some saturation models of shale were also established. Some conclusions were drawn as follows:
Calculated gas saturation (Frac)
1.0
0.8
0.6
0.4
0.2
0.0 0
20
40
60
80
100
Measured gas storage capacity (scf/ton) Fig. 12. Relationship between the calculated gas saturation and gas storage capacity. It verifies the saturation calculation is correct.
that gas saturation of three cores were not measured successfully, and the gas storage capacity was obtained under formation pressure. Fig. 12 illustrates the relationship between gas storage capacity and the calculated gas saturation from NMR bin porosity. As is known to all, three states of gas in shale contribute to the gas storage capacity. The calculated gas saturation is mainly relative to free gas and desorb gas, and the higher the gas saturation, the larger the corresponding gas storage capacity, so both match well with echo other. From Fig. 12, the calculated gas saturation in three cores trend to the corresponding gas storage capacity, and this verifies the saturation calculation method is correct. Of course, it is not a strict proof but an auxiliary verification, and the laboratory measurement of gas saturation will be a strict proof for the saturation calculation model.
6. Discussions NMR technology can provide rich bin porosity information of rock, so it improves petrophysical interpretation models including porosity, permeability, and saturation evaluation. The case study also illustrates it is advantageous in the formation evaluation of shale. Furthermore, mud filtrate does not invade the organic shale, which is different from sandstone, and NMR depth of investigation is in the range of undisturbed shale. So, the NMR-based gas saturation method in organic shale is more applicable and more accurate than in other permeable formations. However, the T2 distribution of organic shale is still effected by kerogen and pyrite volumetric concentrations, and NMR porosity may be underestimated. Moreover, the T2 signals of absorbed gas and kerogen overlap together with clay bound water, and it is difficult to discriminate one from another. Therefore, two-dimensional NMR technology should be developed and applied into organic shale, which can not only observe T2 relaxation, but also measure the contribution of diffusion (Tan et al., 2013a, 2013b; Cao Minh et al., 2012; Chalmers and Bustin, 2008; Kausik et al., 2011), so it can greatly improve the fluid typing through integrated analysis of T2 relaxation and diffusion coefficient among oil, gas, and water. 7. Conclusions Based on NMR measurements of shale core plugs, NMR response characteristics of shale were investigated in detail, and the
(1) NMR T2 distributions of shale can be divided into two types: left peaks dominated discontinuous T2 distribution type (Type I) and continuous bimodal T2 distribution (Type II). The amplitude difference between the left and right peaks in Type I shale is more dominated than Type I shale. The T2 geometric mean of Type II is also larger, which indicates that different sizes of pores in Type II are developed and distribute relatively broadly. Moreover, the permeability of Type II is higher than that of Type I because of larger movable fluid volume. (2) NMR porosity of shale is usually lower than density porosity, and the porosity difference increases with TOC content rising. So, we deduce that the pyrite and kerogen leads to lower NMR porosity. (3) The permeability of shale is related to NMR effective porosity and NMR clay bound fluid volume rather than total porosity. The permeability model based on NMR effective porosity and clay bound water volume clearly reflects the penetration mechanism of fluids. (4) For the shale with a little pyrite, the measured NMR T2 distribution dominantly reflects the pore geometry. So, specific surface area can be calculated from NMR T2 distribution. For Haynesville Shale, specific surface area ranges mainly from 80.0 μm 1 to 1000 μm 1, and the pore radius averages about 0.05 μm. So, the pores of Haynesville Shale are classified as micro-pores. (5) Total gas contained in organic shale not only includes free gas in NMR movable fluid volume, but also the adsorbed gas in kerogen or pores among clays. NMR experiment indicates that the kerogen and adsorbed gas are located in clay bound water part of NMR T2 distribution, and they overlap together with clay bound water. Moreover, the adsorption gas contributes to total gas saturation more than free gas for Haynesville Shale.
Acknowledgments This paper is sponsored by the National Natural Science Foundation of China (41172130, U1403191), the Fundamental Research Funds for the Central Universities (2652015292), and National Major Projects “Development of Major Oil& Gas Fields and Coal Bed Methane (2011ZX05014-001). Furthermore, authors would like to acknowledge the support from the China Scholarship Council (No. 201206405002).
References Akkurt, R., Vingar, H.J.P., Tutunjian, N., Guillory, A.J., 1996. NMR logging of natural gas reservoirs. Log Anal. 7, 33–42. Arvie, D.M., Hill, R.J., Pollastro, R.M., 2005. Assessment of the Gas Potential and Yields from shales: The Barnett Shale Model: Oklahoma Geological Survey Circular. 110; , pp. 9–10. Cao Minh, M., Crary, S., Zielinski, L., Liu, C.B., Jacobsen, S., 2012. 2d-NMR application in unconventional reservoirs. SPE Paper 161578 Presented at the SPE Canadian Unconventional Resources Conferences, Calgary, Alberta, October 1–30, pp. 1– 17. Chalmers, G.R., Bustin, R.M., 2008. Lower Cretaceous gas shales in northeastern British Columbia, Part II: evaluation of regional potential gas resources. Bull. Can. Pet. Geol. 56, 22–61. Coats, G.R., Xiao, L.Z., Manfred, G.P., 1999. NMR Logging Principles and Applications. Sea Gulf Press, Houston, Texas. Curtis, J.B., 2002. Fractured shale-gas systems. AAPG Bull. 86, 1921–1938.
M. Tan et al. / Journal of Petroleum Science and Engineering 136 (2015) 100–111
Curtis, M.E., Sondergeld, C.H., Ambrose, R.J., Rai, C.S., 2012. Microstructural investigation of gas shales in two and three dimensions using nanometer-scale resolution imaging. AAPG Bull. 96, 665–667. Daniels, J., 2004. Evaluation of Hydrocarbon Generation and Storage in the Barnett Shale. Ft. Worth, Anonymous, Texas, p. 2004. Decker, A.D., Hill, D.G., Wicks, D.E., 1990. Log-based gas content and resource estimates for the Antrim shale, Michigan Basin. In: Proceedings of the Low Permeability Reservoirs Symposium, Denver, Colorado, April 26–28, 12p. Dunn, K.J.D., Bergman, J., LaTorraca, G.A., 2002. Nuclear Magnetic Resonance: Petrophysical and Logging Applications. Pergamon, an Imprint of Elsevier Science, p. 2002. Glorioso, J.C., Rattia, A., 2012. Unconventional reservoirs: basic petrophysical concepts for shale gas. SPE Paper 153004 Presented at the SPE/EAGE European Unconventional Resources Conference and Exhibition, Vienna, Austria, March 20–22, pp. 1–38. Guidry, F.K., Luffel, D.L., Olszewski, A.J., 1990. Devonian shale formation evaluation model based on logs, new core analysis methods, and production tests. In: Proceedings of the SPWLA 31th Annual Lagging Symposium, June 24–27, pp. 1– 12. Hook, P., Fairhurst, D., Rylander, E., Badry, B., Crary, N.S., Chatawanich K., Taylor, T., 2011. Improved precision magnetic resonance acquisition. SPE Paper 146883 Presented at the SPE Annual Technical Conference and Exhibition, Denver, Colorado, October 30–November 2, pp. 1–23. Kausik, R., Cao Minh, M., Zielinski, L., Vissapragada, B., Akkurt, R. Song, Y., Liu, C.B., Blair, E., 2011. Characterization of gas dynamics in kerogen nanopores by NMR. SPE Paper 147198 Presented at the SPE Annual Technical Conference and Exhibition, Denver, Colorado, October 30–November 2, pp. 1–16. Luffel, D.L., Guidry, F.K., Curtis, J.B., 1992. Evaluation of Devonian shale with new core and log analysis methods. J. Pet. Technol. 44, 1192–1197. Passey, Q.R., Creaney, S., Kulla, J.B., Moretti, F.J., Stroud, J.D., 1990. A practical model for organic richness from porosity and resistivity log. AAPG Bull. 74, 1777–1794. Rick, L.D., 2004. New evaluation techniques for gas shale reservoirs. In: Proceedings of the Reservoir Symposium, pp. 1–13. 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, 51–75.
111
Ross, D.J.K., Bustin, R.M., 2008. Characterizing the shale gas resource potential of Devonian–Mississippian strata in the Western Canada sedimentary basin: application of an integrated formation evaluation. AAPG Bull. 92, 87–125. Rylander, E., Jiang, T.M., Singer, P., Lewis, R., 2010. NMR T2 distribution in the Eagle Ford Shale: Reflections on Pore Size. SPE Paper 164554 Presented at the SPE Unconventional Gas Conference and Exhibition, Pittsburgh, Pennsylvania, February 23–25, pp. 1–15. Sigal, R.F., Odusina, E., 2011. Laboratory NMR measurement on methane saturated Barnett shale sample. Petrophysics 52, 1–10. 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, pp. 1–17. Tan, M.J., Liu, Q., Zhang, S.Y., 2013a. A dynamic adaptive radial basis function approach for the total organic carbon content prediction in organic shale. Geophysics 50 (6), 445–459. Tan, M.J., Zou, Y.L., Zhou, C.C., 2013b. A new inversion method for (T2, D) 2D NMR logging and fluid typing. Comput. Geosci. 51, 366–380. Xu, C.C., Torres-Verdín, C., 2013. Quantifying fluid distribution and phase connectivity with a simple 3D cubic pore network model constrained by NMR and MICP data. Comput. Geosci. 61, 94–103. Yang, Q.S., Torres-Verdín, C., 2013. Joint stochastic interpretation of conventional well logs acquired in hydrocarbon-bearing shale. In: Proceedings of the SPWLA 54th Annual Logging Symposium, New Orleans, Louisiana, June 22–26, pp. 1–15.
Maojin Tan is a professor in the China University of Geosciences (Beijing). He was granted Ph.D. in 2006, and worked as a postdoctoral fellow in Geophysics from 2006 to 2008, in the Graduate University of Chinese Academy of Sciences. His research interests focus on geophysical well logging with emphasis on new logging technologies and formation evaluation of complex reservoirs. So far, he has published 36 papers in technical journals such as “Geophysics”, “Journal of Applied Geophysics”, and “Computer and Geosciences”.