Determination of pore structures and dynamics of fluids in hydrated cements and natural shales by various 1H and 129Xe NMR methods

Microporous and Mesoporous Materials 281 (2019) 66–74

Contents lists available at ScienceDirect

Microporous and Mesoporous Materials journal homepage: www.elsevier.com/locate/micromeso

Determination of pore structures and dynamics of fluids in hydrated cements and natural shales by various 1H and 129Xe NMR methods

T

Muhammad Asadullah Javeda, Sanna Komulainena, Hugh Daigleb, Boyang Zhangb, Juha Vaaraa, Bing Zhouc,∗∗, Ville-Veikko Telkkia,∗ a

NMR Research Unit, University of Oulu, P.O.Box 3000, FIN-90014, Finland Hildebrand Department of Petroleum and Geosystems Engineering, University of Texas at Austin, TX, USA c School of Materials Science and Engineering, Tongji University, Shanghai, 210000, China b

A R T I C LE I N FO

A B S T R A C T

Keywords: Porous structure of micro/mesoporous materials Cement Shale 129 Xe NMR NMR cryoporometry

Cements and shales play a vital role in the construction and energy sectors. Here, we use a set of advanced NMR methods to characterize the porous networks and dynamics of fluids in hydrated cement and shale samples. We compare the properties of cements from two different manufacturers, BASF and Portland, as well as shales brought from USA and China. 129Xe NMR spectra of xenon gas adsorbed in the samples indicate that the capillary mesopores are smaller and the exchange between free and confined gas is slower in the Portland than in the BASF cement samples. The pores probed by xenon in the shale samples from USA are significantly smaller than in the cement samples, partially in the micropore region. There is a substantial difference in between the 129Xe spectra of shales from USA and China. Whereas the latter show a clear signature of paramagnetic impurities by exhibiting large negative 129Xe chemical shifts (referenced to the free gas), the samples from USA lack the negative chemical shifts but feature large positive shift values, which may indicate the presence of micropores and/or paramagnetic defects. 1H NMR cryoporometry measurements using acetonitrile as probe liquid allowed the observation of mesopores in the shale samples as well, and T2-T2 relaxation exchange experiment enabled the quantification of the exchange rates between free and confined acetonitrile.

1. Introduction In cement-based materials, pore networks influence the strength, durability and permeability [1,2]. The pore sizes range from nanometers to millimeters. Natural shales are porous materials that act as hydrocarbon reservoirs and are composed of several clay minerals [3]. The heterogeneous pore structures of shales have a strong influence on the accumulation, migration and uses of shale gas [4,5]. Therefore, detailed knowledge of pore structures as well as dynamics of fluids in cements and shales is of paramount importance, particularly for the safety of oil well completions. Several techniques, such as mercury intrusion porosimetry (MIP), nitrogen adsorption/desorption (NAD), scanning electron microscopy (SEM), transmission electron microscopy (TEM), and small angle X-ray scattering (SAXS), have been exploited to study the porous materials, and they have their own strengths and limitations [6]. Nuclear magnetic resonance (NMR) spectroscopy has proven to be an excellent tool for gaining molecular-level chemical, dynamic and

∗

spatial information [7]. NMR allows investigation of fluids deep inside optically opaque materials, which is a significant strength as compared to many other methods listed above. NMR relaxation, diffusion, cryoporometry and magnetic resonance imaging (MRI) measurements of fluids contained in the pores provide versatile information about the porosity, pore size distribution as well as fluid transport and chemical exchange phenomena [8–13]. There is a long history of investigations of the porous structure of cements and shales with 1H NMR relaxometry [14–27]. Typically, fluid molecules are characterized by a distribution of relaxation times due to the heterogeneous environments existing inside porous materials. The relaxation time distribution can be extracted from the experimental data by the inverse Laplace transform [28] and, in the simplest case, it reflects the pore size distribution [29]. T2-T2 relaxation exchange experiments [30] allow one to quantify the exchange of fluid molecules between the pores of different sizes, by exploiting T2 relaxation contrast [31]. This is not possible using the traditional exchange spectroscopy (EXSY) method [32], because typically the fluid molecules in different

Corresponding author. Corresponding author. E-mail addresses: [email protected] (B. Zhou), ville-veikko.telkki@oulu.fi (V.-V. Telkki).

∗∗

https://doi.org/10.1016/j.micromeso.2019.02.034 Received 14 December 2018; Received in revised form 20 February 2019; Accepted 25 February 2019 Available online 01 March 2019 1387-1811/ © 2019 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/BY/4.0/).

Microporous and Mesoporous Materials 281 (2019) 66–74

M.A. Javed, et al.

Portland companies. Two samples were hydrated for four months with an initial water/cement ratio of 0.3 and 0.5. During the hydration period, the samples were stored in a closed chamber at a constant temperature (20 °C and 100% relative humidity). The BASF and Portland cement samples with initial water-to-cement ratios of 0.3 and 0.5, are referred to as B0.3, B0.5 and P0.3, P0.5, respectively. We investigated also two siliceous, oil-bearing shale samples from the northern Rocky Mountains and one carbonate-rich shale sample from the Eagle Ford Formation in southern Texas, USA. The samples were labeled as Sil3-14, Sil4-34 and EF1-223, and they were drilled from the following depths: 3594, 3612 and 3461 m, respectively. Detailed characterization of the porosity, pore size distribution and chemical composition of the same samples can be found from Ref. [59] (see also Tables S2 and S3). We compared the results with our previous experiments carried out for the black shale core samples that were collected from the CambrianSilurian strata at the Low Yangzi Plateau in China [57]. This source is well known for the sedimentary facies with abundant organic materials. One of the shale samples was from Hubei Province of China, while two other samples were from Chongqing. The three shale samples were labeled as HU234, CH2175 and CH3634, where HU and CH refers to Hubei and Chongqing, respectively, and the number corresponds to the depth in meter, from which the sample was drilled. All the cement and shale samples were ground into a fine powder. The shale samples were also sieved by passing through a 63 μm sieve. The sieving was not done for the cement samples.

pores have the same chemical shift. The off-diagonal cross peaks in the T2-T2 maps reveal unambiguously the exchange between the pores due to diffusion. The method has been used to study the exchange of water in cements [33], wood [34], soil [35], rocks [31], articular cartilage [36], silica, borosilicate and soda lime glass particles [37–40], as well as the proton exchange in water/urea system [41]. NMR cryoporometry is a technique for the determination of the pore size distribution via the observation of the solid-liquid phase transition temperature of a medium confined in the pores [10,42]. According to the Gibbs-Thomson equation [43], the melting point depression ΔTmp in a cylindrical pore is inversely proportional to the pore radius Rp: ΔTmp = Tmp (bulk) – Tmp (pore) = 2 σsl Tmp (bulk) / (ΔHf ρs Rp) = kp / Rp. (1) Here, Tmp (bulk) and Tmp (pore) are the melting temperatures of bulk and confined liquid, σsl is the surface energy of the solid-liquid interface, ΔHf is the specific bulk enthalpy of fusion, and ρs is the density of the solid. The constant kp is characteristic of each medium. Because usually the T2 relaxation time of frozen liquid is much shorter than that of unfrozen liquid, by using a proper echo time in a spin-echo pulse sequence, one can measure solely the signal of the unfrozen substance, with negligible contribution from the frozen component. The melting point distribution of the substance is determined by measuring the amplitude of the signal of the unfrozen liquid component as a function of temperature. This is converted into pore size distribution by using the Gibbs-Thomson equation. NMR cryoporometry has been used for the characterization of various materials [10,42], including cements [44] and shales [45,46]. Xenon is an inert gas, which has a129Xe isotope with a spin-½ nucleus and high natural abundance (26%). Because of its easily polarizable electron cloud, the chemical shift of 129Xe is extremely sensitive to its local environment [47]. Therefore, it is an ideal probe for the investigation of porous host media [48]. The chemical shift and shielding anisotropy of xenon provide information about the pore size and geometry, as well as surface interactions. Xenon has been exploited in the investigation of many media, including zeolites and clathrates [49], as well as silica [50,51], alumina [52] and carbonaceous [53] materials, membranes [54], and cages [55,56]. Recently we exploited, for the first time, 129Xe NMR for the characterization of cement and shale materials [57]. It is interesting to compare those materials because, on the one hand, there are similarities in their chemical and mineral composition as well as texture but, on the other hand, shales are more heterogeneous. From the BASF hydrated cement samples, we observed one 129Xe resonance implying the presence of nanopores and another from larger pores or free gas. In contrast, the shale samples received from China showed an extremely broad 129Xe signal, covering a range of about 600 ppm, due to paramagnetic substances present in the sample. In this work, we extend the previous study by comparing hydrated cements from two different manufactures, BASF and Portland, as well as shales received from China and USA. Xenon is a very interesting fluid for investigating the shale samples, because its size is similar to that of methane, and therefore it can be expected to probe similar environments and have similar dynamics as natural hydrocarbons in shales [58]. In addition to 129Xe NMR, we exploit 1H NMR cryoporometry and T2-T2 exchange measurements to estimate the pore size distribution and dynamics of acetonitrile in the shale samples. The NMR measurements are complemented with field-emission scanning electron microscope (FESEM) and X-ray diffraction (XRD) analysis.

2.2.

129

Xe NMR experiments

The ground cement and shale samples were added to 10 mm medium-wall NMR tubes. The samples were subsequently dried overnight at 70 °C in a vacuum line. Thereafter, 129Xe isotope-enriched (91%) xenon gas was condensed in the sample using liquid N2, and the samples were flame-sealed. The xenon pressure inside the glass tube was about 4 atm. Variable-temperature 129Xe NMR spectra were measured from 225 to 290 K in steps of 10 K using a Bruker Avance III 300 spectrometer with a magnetic field strength of 7.1 T (129Xe frequency of 83 MHz) and a 10-mm BBFO probe. The temperature stabilization time was 30 min. The spectra were measured with a spin-echo pulse sequence to avoid background distortions, which were present in the spectra measured using only a 90° excitation pulse. The lengths of 90° and 180° pulses were 26.5 and 53 μs. The number of accumulated scans was 512 and 4096 for the cement and shale samples, respectively. The τ delays between the pulses in the spin echo experiment were 1 and 15 μs for the cement and shale samples, respectively. For the cement samples, the relaxation delay was 8 s and the total experiment time about 1 h. For the shale samples, the relaxation delay was 3 s and experiment time approximately 3.5 h. The chemical shift scale of the 129Xe spectra was referenced so that the chemical shift of free 129Xe at zero pressure is 0 ppm. Two-dimensional (2D) 129Xe EXSY spectra of the cement samples were measured using Bruker Avance III 400 spectrometer at a magnetic field of 9.4 T (129Xe frequency of 110.7 MHz) and a 10-mm BBFO probe. The length of the 90° pulse was 36 μs and the number of accumulated scans was 128. The relaxation delay was 5 s and the total experiment time about 6 h. The mixing time τm was varied from 1 to 90 ms. 2.3. 1H NMR cryoporometry experiments Some ground and dried shale samples were immersed in an excess of acetonitrile in a 10 mm medium-wall sample tube to carry out NMR cryoporometry analysis. 1H NMR cryoporometry experiments were performed on a Bruker Avance III 300 spectrometer using the 10-mm BBFO probe. Variable temperature NMR spectra were measured using a CPMG pulse sequence with an echo time of 0.3 ms and 2048 echoes

2. Materials and methods 2.1. Materials Commercial cement samples were purchased from BASF and 67

Microporous and Mesoporous Materials 281 (2019) 66–74

M.A. Javed, et al.

measured in a single scan. The measurement temperatures varied from 180 to 240 K. The temperature step was 5 K from 180 to 210 K, 2 K from 212 to 224 K, 1 K from 225 to 236 K and then 2 K from 238 to 240 K. The heating rate in the experiments was 1 K per 7.5 min. The number of accumulated scans was 64 and the experiment time approximately 4 min. The integrals of signal for each temperature were multiplied by a factor of T/T0 to exclude the temperature dependence of magnetization given by the Curie law. This correction makes the integrals of signals proportional to the true amount of the unfrozen acetonitrile liquid. The pore size distribution was extracted by a least-squares fit of a model function to the integrals as described by Aksnes et al. [60]. Hansen et al. [61] demonstrated the presence of a non-freezing layer with thickness of about 0.3–0.8 nm. Therefore, 0.6 nm was added to the diameters for the calculation of the true pore size. The value of constant kp = 545 KÅ in Equation (1) for acetonitrile, was experimentally determined by Aksnes et al. [60] by using silica materials. We have tested with known silica materials that this value of the constant gives reliable results [62]. Because silica is one of the main components of the cement and shale materials studied here, it is justified to use this value in the analysis. 1 H T2 relaxation time distributions of acetonitrile in the shale samples were determined by performing Laplace inversion of the CPMG data. The Laplace inversion algorithm was received from the research group of late prof. Callaghan.

2.4. 1H T2-T2 exchange experiments 1

H T2-T2 relaxation exchange experiments for acetonitrile in the shale samples were carried out at room temperature using the Bruker Avance III 300 spectrometer and 10-mm BBFO probe. The relaxation delay was 3 s and the echo time 2 ms. The number of echoes in the direct and indirect dimensions were 32, and altogether 8 scans were acquired. The experiment time was 8 h for τm = 10 ms mixing-time experiment. The mixing time was varied from 10 ms to 1.5 s. The T2-T2 relaxation exchange maps of acetonitrile in the shale samples were determined by the Matlab-based Laplace inversion algorithm received from the research group of late prof. Callaghan.

3. Results and discussion 3.1. FESEM and XRD analysis The chemical composition of the cement samples, measured by XRD, is listed in Table S1. The compositions of the BASF and Portland cements were very similar. As explained in the SI, the main hydrated phases were calcium silicate hydrate (CSH, 3CaO2SiO2.4H2O) and calcium hydroxide [Ca(OH)2]. No paramagnetic minerals were observed in the cement samples. The results of XRD analysis of the shale samples from USA can be found from Tables S2 and S3 [59]. The samples consisted mainly of clay minerals (Sil3-14/Sil4-34/EF1-223: 38/33/19%), calcite (3/traces/ 60%) as well as fine-grained quartz (39/50/13%). The samples included also 1–5% of pyrite and traces of ankerite/siderite that contain the Fe(II) ion with the d6 configuration and, thereby, may in principle be magnetic. Both pyrite and the other polymorph of FeS2, marcasite, have been reported to possess non-magnetic ground states [63,64]. On the other hand, the shale samples from China displayed clear signature of paramagnetism [57], with siderite (FeCO3) as the most likely cause. Indeed, in normal pressure conditions, the Fe(II) centers of this mineral are found in the high-spin (S = 2), magnetic state [65]. The FESEM images of cement and shale samples at two different magnification scales are shown in Fig. 1. As expected, the cement samples appear more homogenous than shale samples. There are porous structures visible from nanometer to micrometer scale in both the cement and shale samples.

Fig. 1. FESEM images of BASF and Portland cement samples and EF1-223, Sil434, Sil3-14 shale samples at two different scales.

3.2.

129

Xe NMR analysis of the cement samples

129 Xe spin-echo spectra of the BASF and Portland cement samples measured at 225 and 290 K are shown in Fig. 2a and b, respectively. The spectra show two major signals. The stronger signal centered around 0–20 ppm is interpreted to arise from xenon gas in the larger (> 1 μm) pores visible in the FESEM images (Fig. 1) as well as interparticle voids, and it is called the free gas signal. The other, smaller signal centered around 20–50 ppm at 290 K and 50–130 ppm at 225 K highlights the xenon in mesopores. Terskikh et al. [50] defined the

68

Microporous and Mesoporous Materials 281 (2019) 66–74

M.A. Javed, et al.

Fig. 2. 129Xe spin echo spectra of four hydrated white cement samples measured at (a) 225 and (b) 290 K at 7.1 T. (c) Chemical shifts of free gas (denoted by f) and capillary pore gas (p) as a function of temperature. Fits of Equation (2) to the pore gas chemical shift values are shown by dashed lines.

correlation between the chemical shift values of 129Xe and pore size for silica-based materials. By applying the same correlation to the cement samples, the size of the mesopores was estimated to be in the 10–50 nm range, which is a typical size for capillary pores in hardened cement [66]. The chemical shift values of the mesopore signals of the Portland cement samples are higher than those of the BASF cement samples, suggesting that the capillary pores are smaller in the former samples. In the case of Portland cement, the chemical shift of the mesopore signal is significantly higher for the P0.5 sample than for P0.3 sample, indicating that the higher initial water-to-cement ratio resulted in the smaller capillary pores. The P0.5 cement sample showed another well-resolved, small mesopore peak centered around 130 ppm at 225 K and 80 ppm at 290 K. This implies the simultaneous presence of much smaller mesopores, with pore size of about 6 nm [50]. This is a typical size of gel pores in hardened cement. We note that, in addition to the pore size, there are many other factors contributing to chemical shift of 129Xe [48]. For example, the cement samples have Ca, Fe, Al and Si in their structure that can strongly affects the nature of -OH groups. Moreover, Fe ions can also affect the chemical shift. 129 Xe NMR spectra of the cement samples as a function of temperature are shown in Figs. S1–S4. The chemical shift values of the mesopore signals increase with decreasing temperature, as shown in Fig. 2c. Considering the chemical shifts of 129Xe in mesopores as weighted average contributions of free and adsorbed xenon, Terskikh et al. [67] derived the following formula:

δ=

for cylindrical pores), R is the universal gas constant, T is the temperature, K0 is a pre-exponential factor, and Q is the effective heat of adsorption. The model explains that the chemical shift values increase with decreasing temperature because the population of xenon adsorbed on the pore surface increases. The fits of Equation (2) to the capillary mesopore chemical shift values of cement samples are shown in Fig. 2c. The parameters obtained from fits are listed in Table 1. The values of the heat of adsorption are quite similar for different samples, ranging from 12.1 to 14.9 kJ/mol and being comparable to the heats of adsorption of the silica gels (8–21 kJ/mol) [67]. The chemical shift values of the 129Xe adsorbed on the surface of pores are also the same results within the experimental error, i.e., about 180 ppm, which is larger than for silica, alumina and MCM-41 materials (around 120 ppm) [50,52]. The difference in the chemical structure of the pore surface and heterogeneity of pore structure of the cement samples could be a reason behind these differences. The BASF cement samples have higher D/ƞK0R values as compared to Portland samples, reflecting the bigger capillary pore size. 2D 129Xe EXSY spectra of the P0.5 cement sample measured at room temperature with varying mixing time, are shown in Fig. 3. The offdiagonal cross peaks become clearly visible at the longer mixing time, Table 1 Parameters obtained from the fitting of Equation (2) to the chemical shift values of the pore gas signals of cement samples. Sample

Q (kJ/mol)

δs (ppm)

D/ƞK0R (K1/2)

BASF 0.3 BASF 0.5 Portland 0.3 Portland 0.5

13.9 14.9 12.9 12.1

180 140 180 180

30200 ± 8000 39000 ± 11000 17000 ± 3000 9000 ± 3000

δS 1+

D ηR T K 0exp(Q / RT )

(2)

Here, δS is the chemical shift of 129Xe adsorbed on the surface of the pore, D is the mean pore size, η is the pore geometry factor (equal to 4 69

± ± ± ±

1.0 0.9 0.8 1.1

± ± ± ±

30 20 30 30

Microporous and Mesoporous Materials 281 (2019) 66–74

M.A. Javed, et al.

Fig. 3. 129Xe EXSY spectra of the Portland 0.5 cement sample measured at room temperature using a mixing time of (a) 1 (b) 5 and (c) 30 ms. (d) The amplitudes of the diagonal free gas (f) and pore gas signals (p) as well as the cross peaks (fp) as a function of mixing times and the fit of the two-site exchange model to experimental data (solid lines).

centered around 1–2 nm and a broad peak extending from 3 to 200 nm; the siliceous samples had mainly clay-associated porosity with a peak around 2 nm (width of dehydrated illite-smectite interlayers) while the Eagle Ford samples had a broader pore size distribution with less of clay peak. In fact, similar bimodal features are visible also in the 129Xe NMR signal observed from the shale pores. The pore signal is slightly more intense for the siliceous samples than for the carbonate-rich sample, which may reflect their slightly higher porosity observed by 1H NMR [59]. Restricted motion accompanied by anisotropic interactions (chemical shift anisotropy or dipolar coupling) may have also some effect on the line shape. For the comparison, the 129Xe spin-echo spectra of the Hubei and Chongqing shale samples (from China) from various reservoir depths measured at 225 and 290 K are shown in Fig. 4c and d, respectively [57]. Because those samples included significant amount of paramagnetic Fe(II)-bearing minerals, i.e., siderite, they showed extremely broad signal extending from −300 to 300 ppm. The large negative chemical shifts proved that the paramagnetic interactions had a dominant effect on the spectral features in those samples. Because no such negative shifts with respect to the gaseous 129Xe reference were observed from the new samples from USA analyzed in this work, the effect of paramagnetic substances is significantly smaller in the shales from USA. One may expect as a consequence of the smaller content and/or different chemical structure of the paramagnetic substances. Indeed, the predominant impurity component of the shale samples from USA, pyrite, with the Fe(II) centers in the low-spin configuration, is not expected to show any paramagnetic effects at low temperature. However, Fe(II) systems are known to possess spin-crossover effects between the low-spin and high-spin paramagnetic states upon applying temperature [69] or pressure [65]. In the case of the present samples, extension of the measurements to significantly higher temperatures than presently used, may in principle lead to population of an excited S = 2 spin multiplet and, thereby, thermal onset of paramagnetism. This would show up as appearance of negative shifts, as well as an additional source of non-monotonic temperature dependence of the shifts, in the USA-based shales, too. Variable-temperature 129Xe NMR spectra of the shale samples are shown in Figs. S5–S7. The chemical shift values of the pore gas signals

Table 2 Parameters resulted from the fits of two-site exchange model [31] to the amplitudes of the EXSY signals of the cement samples. XF and XP are the relative populations of free and pore gas sites. kFP and kPF are the exchange rates from free gas site to pore gas site and vice versa, and k is the overall exchange rate. Sample

XF

BASF 0.3 BASF 0.5 Portland 0.3 Portland 0.5

0.87 0.82 0.84 0.83

XP ± ± ± ±

0.02 0.02 0.01 0.01

0.13 0.18 0.16 0.17

± ± ± ±

0.02 0.02 0.02 0.02

kFP (s−1)

kPF (s−1)

k (s−1)

54 80 26 21

350 370 140 110

400 460 170 130

± ± ± ±

9 13 4 4

± ± ± ±

70 70 30 15

± ± ± ±

70 70 30 20

showing a diffusion-driven exchange process between the free gas and capillary mesopore sites. The fits of two-site exchange model [32] to the experimentally determined amplitudes of the diagonal and cross peaks are shown in Figs. 3d and S8, and the resulting parameters in Table 2. The exchange rates of the BASF samples (BASF 0.3: 400 ± 70 s−1; BASF 0.5: 460 ± 70 s−1) are higher than those of the Portland samples (Portland 0.3: 170 ± 30 s−1; Portland 0.5: 130 ± 20 s−1). The exchange rates follow inversely the chemical shifts of the capillary micropore signals (see Fig. 2c); the larger chemical shift, the smaller exchange rate. This implies that the diffusion from the smaller pores to the free gas site is slower than from the larger pores. 3.3.

129

Xe NMR analysis of the shale samples

129

Xe spin-echo spectra of the shale samples from USA measured at 225 and 290 K are shown in Fig. 4a and b, respectively. Also in this case the spectra show two dominant signals: one from the free gas (xenon in large pores and in between the particles) around 0 ppm and another, very broad signal from the pores. The chemical shift range of the pore signals is about 70–300 ppm at 290 K and 90–330 ppm at 225K. The relationships between the chemical shift and pore size determined by Demarquay et al. [68] and Terskikh et al. [50] indicate that xenon gas explores a broad range of micropores and mesopores with different sizes in the shale samples, the pore sizes ranging at least from 1 to 10 nm. The nitrogen sorption isotherms of the same samples shown in Ref. [59] show a bimodal pore size distribution with a narrow peak 70

Microporous and Mesoporous Materials 281 (2019) 66–74

M.A. Javed, et al.

Fig. 4. 129Xe spin echo spectra of the shale samples from USA at (a) 225 K and (b) 290 K, as well as China at (c) 225 K and (d) 290 K. HU refers to Hubei, CH to Chongqing and the number after these abbreviations to sampling depth in units of meter.

acetonitrile. There is a gradual increase in the amplitude of the 1H signal with increasing temperature in the lower temperature region due to the melting of the liquid confined to the pores. The steep increase in the amplitude above 223 K indicates melting of acetonitrile in larger pores and spaces in between the particles. The pore size distributions resulted from the NMR cryoporometry analysis are shown in Fig. 5b. The pore sizes observed by NMR cryoporometry range from 10 to over 100 nm, and the amount and size of mesopores seem to be larger for the carbonate-rich sample than for the siliceous samples. However, there may be some relaxation weighting in the distributions, because in the very small pores in shales, 1H T2 relaxation time may be so short that the nuclei may not be observed in the experiment [26]. Therefore, some bias in the pore size distributions can remain. On the other hand, the distributions are in good agreement with the nitrogen sorption results,

slightly increase with decreasing temperature, but the change is not as significant as in the case of the cement samples. It turned out that Equation (2) was not able to explain the observed chemical shift behavior, implying that the simplified model is not valid for the shale samples because of the small pore sizes, complex pore structure, and heterogeneous pore surface chemistry. 3.4. 1H NMR cryoporometry and T2 relaxation analysis of the shale samples The first echo (echo time 0.3 ms) signal amplitude of the 1H CPMG data of acetonitrile confined in the shale samples as a function of temperature is shown in Fig. 5a. As explained in the Introduction, the amplitude is proportional to the amount of liquid (non-frozen)

Fig. 5. (a) 1H spin echo signal amplitude of liquid acetonitrile confined to shale samples. (b) Pore size distribution in the NMR cryoporometry analysis. (c) T2 distributions of shale samples at 223 K (solid lines) and 230 K (dashed lines), i.e., below and above the melting point of bulk acetonitrile. (d) T2 relaxation time distribution of the carbonate-rich EF1-223 shale sample as a function of temperature.

71

Microporous and Mesoporous Materials 281 (2019) 66–74

M.A. Javed, et al.

Fig. 6. 1H T2-T2 relaxation exchange maps for acetonitrile in the carbonate-rich EF1-223 shale sample at room temperature. (b) and (c): Amplitudes of the peaks as a function of mixing times as well as fits of the two-site exchange model. S is the average of S1 and S2 and F is an average of F1 and F2.

samples.

which also showed broad pore size distributions [59]. The 1H T2 relaxation time distributions of acetonitrile in the shale samples as a function of temperature are shown in Figs. 5c, 5d, S9 and S10. There is a broad distribution of relaxation times in the whole temperature range. The distributions seem to be divided into multiple discrete components, but most probably this is a result of the so-called pearling artefact typical for Laplace inversion [28], and the true distribution is more continuous. Below the melting point of bulk acetonitrile (227 K), the observed T2 values range from 0.1 to 100 ms for the carbonate-rich sample and from 0.5 to 100 ms for the siliceous sample, while above that temperature they range from 1 ms to 1 s for all the

3.5. T2-T2 relaxation exchange of the shale samples The diffusion-driven exchange process between the bulk and confined acetonitrile in the shale samples was observed by using T2-T2 relaxation exchange experiment. It is not possible to observe the exchange process with the traditional EXSY method, because both sites have the same 1H chemical shift. The T2-T2 maps are shown in Fig. 6a, S11a and S12a. The maps include from three to five diagonal peaks, labeled alphabetically in the figures, as well as some cross peaks. Again, 72

Microporous and Mesoporous Materials 281 (2019) 66–74

M.A. Javed, et al.

Table 3 Parameters resulted from the fits of two-site exchange model to the amplitudes of T2-T2 signals of acetonitrile in the shale samples. X1 and X2 are the relative populations of diagonal sites where the X1 population was kept constant during the fitting of data. k12and k21 are the exchange rates of between the sites 1 and 2 and vice versa, while k is the overall exchange rate. Sample

X1a

X2

EF1-223 (Fast) EF1-223 (Slow) Sil4-34 (Slow) Sil3-14 (Slow)

0.955 0.988 0.987 0.986

0.045 0.012 0.013 0.014

± ± ± ±

0.007 0.001 0.001 0.001

k12

k21

k (s−1)

0.28 ± 0.08 0.012 ± 0.003 0.017 ± 0.008 0.019 ± 0.009

6±2 1.1 ± 0.3 1.3 ± 0.7 1.3 ± 0.7

6±2 1.1 ± 0.3 1.3 ± 0.7 1.3 ± 0.7

a Because the peak A is close to the edge of the map, its amplitude fluctuated as a function of τm. Therefore, the relative population of diagonal site 1 was fixed during the processing.

material. NMR cryoporometry analysis showed also the presence of mesopores in the shale samples, but there may be some relaxation weighting in the distributions. Using T2-T2 relaxation exchange experiment, the diffusion-driven exchange process was observed between the bulk and confined acetonitrile in the pores of shale samples and the exchange rate constants were quantified. Overall, the combined 1H and 129 Xe NMR analysis reveal very versatile information about the structure of the cement and shale samples as well as dynamics of absorbed fluids that may be of interest also in the commercial applications of these materials.

at least some of the peak splitting is most probably due to the pearling artefact. For the carbonate-rich EF1-223 shale sample (Fig. 6a), we identified two groups of cross-peaks, labeled by F and S, which reflect slower and faster exchange rates. As the S cross peaks connect the intermediate and longest T2 values, we interpreted that it reflects the exchange between bulk acetonitrile and acetonitrile in the larger pores. The F cross peaks, in turn, connect the intermediate and small T2 values, and therefore may reflect the exchange between macro- and mesopores. The fits of two-site exchange model [32] to the amplitudes of diagonal and cross peaks as a function of mixing time are shown in Fig. 6b and c for slow and fast exchange processes, respectively. These fits resulted in the exchange rate of 6 ± 2 s−1 for the faster exchange process and 1.1 ± 0.3 s−1 for the slower exchange process. Contrary to the carbonate-rich sample, the T2-T2 maps for the siliceous shale samples (Figs. S11a and S12b) showed only the slower exchange process. The exchange rates for that process were similar to the carbonate-rich sample (see Table 3). We note that the exchange rates were much smaller than the R2 = 1/T2 relaxation rates, and therefore it is not needed to consider relaxation during the evolution period in the analysis [33]. According to the NMR cryoporometry analysis, the amount of mesopores is much larger in the carbonate-rich sample than in the siliceous samples. This may explain why the exchange between macro- and mesopores was separately observed in the carbonate-rich sample but not in the siliceous samples. On the other hand, the Sil3-14 and Sil4-34 samples include 39% and 50% quartz in contrast to 13% in EF1-223. At the same time, the amount of calcite is 60% in EF1-223, whereas in Sil3-14 and Sil4-34 samples it is less than 3%. These differences may affect the substitution of bound water, which was not removed from the sample during the drying process, by acetonitrile molecules. The nature of functional groups affects the adsorption-desorption processes, and it may be anticipated that water substitution might be lower in EF1-223 than that in Sil3-14 and Sil4-34 samples [70]. This might lower the relative proportion of small pores in the carbonate-rich sample observed by acetonitrile in relaxation and NMR cryoporometry experiments.

Acknowledgements B.Z. acknowledges financial support from an NSFC Grant No. 41572103. V.-V.T. acknowledges the financial support of the European Research Council under Horizon 2020 (H2020/2018–2022/ERC grant agreement no. 772110) and Academy of Finland (grants #289649, 294027 and 319216). JV is also grateful to the Academy of Finland (grant #296292) for financial support. The authors acknowledge financial support from the Kvantum institute (University of Oulu). We are thankful to Drs. Wu Yao and Dan Jin for providing the white cement samples. Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.micromeso.2019.02.034. References [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12]

4. Conclusions This article described a versatile 1H and 129Xe NMR analysis the cement and shale samples. The 129Xe analysis showed that the size of the capillary mesopores in both BASF and Portland cement samples vary in between 10 and 50 nm. The pore size is smaller for the Portland samples. The EXSY data implies that the diffusion between the free gas and capillary mesopores is slower in the Portland than BASF samples due to the smaller pore size. 129Xe spin-echo spectra of the shale samples from USA indicates the presence of micropores and mesopores with the pore sizes ranging from 1 to 10 nm. Contrary to the shale samples from China, the spectra in the shale samples from USA do not include signal at negative chemical shifts, and thereby lack the explicit fingerprint of paramagnetic effects. 1H T2 distributions of acetonitrile in the shale samples are very broad due to the heterogeneity of the

[13] [14] [15] [16] [17] [18] [19] [20] [21] [22] [23]

73

H.M. Jennings, Cement Concr. Res. 38 (2008) 275–289. H.F.W. Taylor, Cement Chemistry, second ed., Thomas Telford, London, 1997. M. Mehana, I. El-monier, Petroleum 2 (2016) 138–147. E. Fathi, I.Y. Akkutlu, Transport Porous Media 80 (2009) 81–304. W. Ding, C. Li, C. Xu, K. Jiu, W. Zeng, L. Wu, Geosci. Front 3 (1) (2012) 97–105. D.M. Jarvie, R.J. Hill, T.E. Ruble, R.M. Pollastro, AAPG Bull. 91 (2007) 475–499. P.T. Callaghan, Translational Dynamics and Magnetic Resonance: Principles of Pulsed Gradient Spin Echo NMR, Oxford University Press, Oxford, USA, 2011. J. Kärger, R. Valiullin, Chem. Soc. Rev. 42 (2013) 4172. Y.-Q. Song, J. Magn. Reson. 229 (2013) 12–24. J. Mitchell, J.B. Webber, J.H. Strange, Phys. Rep. 461 (2008) 1–36. P.M. Kekkonen, A. Ylisassi, V.-V. Telkki, J. Phys. Chem. C 118 (2014) 2146–2153. M.A. Javed, P.M. Kekkonen, S. Ahola, V.-V. Telkki, Holzforschung 69 (2015) 899–907. Ö.F. Erdem, D. Michel, J. Phys. Chem. B 109 (2005) 12054–12061. A.C.A. Muller, K.L. Scrivener, A.M. Gajewicz, P.J. McDonald, J. Phys. Chem. C 117 (2013) 403–412. A. Plassais, M.-P. Pomiès, N. Lequeux, P. Boch, J.-P. Korb, D. Petit, F. Barberon, Magn. Reson. Imag. 21 (2003) 369–371. P.J. McDonald, J.-P. Korb, J. Mitchell, L. Monteilhet, Phys. Rev. E - Stat. Nonlinear Soft Matter Phys. 72 (2005) 011409. J.-P. Korb, Curr. Opin. Colloid Interface 14 (2009) 192–202. M. Fourmentin, P. Faure, S. Rodts, D. Lesueur, D. Daviller, P. Coussot, Cement Concr. Res. 95 (2017) 56–64. Y.-Q. Song, Cement Concr. Res. 37 (2007) 325–328. A. Valori, P.J. McDonald, K.L. Scrivener, Cement Concr. Res. 49 (2013) 65–81. J.E. Birdwell, K.E. Washburn, Energy Fuel. 29 (2015) 2234–2243. M. Fleury, E. Kohler, F. Norrant, S. Gautier, J. M'Hamdi, L. Barrè, J. Phys. Chem. C 117 (2013) 4551–4560. K.E. Washburn, J.E. Birdwell, J. Magn. Reson. 233 (2013) 17–28.

Microporous and Mesoporous Materials 281 (2019) 66–74

M.A. Javed, et al.

[48] E. Weiland, M.-A. Springuel-Huet, A. Nossov, A. Gèdèon, Microporous Mesoporous Mater. 225 (2016) 41–65. [49] J. Demarquay, J. Fraissard, Chem. Phys. Lett. 136 (1987) 314–318. [50] V.V. Terskikh, I.L. Moudrakovski, S.R. Breeze, S. Lang, C.I. Ratcliffe, J.A. Ripmeester, A. Sayari, Langmuir 18 (2002) 5653–5656. [51] V.-V. Telkki, J. Lounila, J. Jokisaari, Magn. Reson. Imaging 25 (2007) 457–460. [52] E. Weiland, M.-A. Springuel-Huet, A. Nossov, F. Guenneau, A.-A. Quoineaud, A.A. Gèdèon, J. Phys. Chem. C 119 (2015) 15285–15291. [53] C. Tsiao, R.E. Botto, Energy Fuel. 5 (1991) 87–92. [54] V.-V. Telkki, C. Hilty, S. Garcia, E. Harel, A. Pines, J. Phys. Chem. B 111 (2007) 13929–13936. [55] L. Schröder, Phys. Med. 29 (2013) 3–16. [56] J. Roukala, J. Zhu, C. Giri, K. Rissanen, P. Lantto, V.-V. Telkki, J. Am. Chem. Soc. 137 (2015) 2464–2467. [57] B. Zhou, S. Komulainen, J. Vaara, V.-V. Telkki, Microporous Mesoporous Mater. 253 (2017) 49–54. [58] J. Kärger, C.M. Papadakis, F. Stallmach, R. Haberlandt, D. Michel, A. Pöppl, R. Stannarius (Eds.), Molecules in Interaction with Surfaces and Interfaces, Springer, 2004, pp. 127–162. [59] H. Daigle, N.W. Hayman, E.D. Kelly, K.L. Milliken, H. Jiang, H. Daigle, N.W. Hayman, E.D. Kelly, K.L. Milliken, H. Jiang, Geophys. Res. Lett. 44 (2017) 2167–2176. [60] D.W. Aksnes, K. Førland, L. Kimtys, J. Phys. Chem. Chem. Phys. 3 (2001) 3203–3207. [61] E.W. Hansen, M. Stöcker, R. Schmidt, J. Phys. Chem. 100 (1996) 2195–2200. [62] V.-V. Telkki, J. Lounila, J. Jokisaari, J. Phys. Chem. B 109 (2005) 757–763. [63] M.A. Khan, J. Phys. C 9 (1976) 81. [64] R. Garg, V.K. Garg, Y. Nakamura, Hyperfine Interact. 67 (1991) 447. [65] C. Weis, C. Sternemann, V. Cerantola, C.J. Sahle, G. Spiekermann, M. Harder, Y. Forlov, A. Kononov, R. Sakrowski, H. Yavas, M. Tolan, M. Wilke, Sci. Rep. 7 (2017) 16526. [66] S. Mindess, J.F. Young, D. Darwin, Concrete, second ed., Pearson, Upper Saddle River, USA, 2003. [67] V.V. Terskikh, I.L. Mudrakovskii, V.M. Mastikhin, J. Chem. Soc. Faraday. Trans. 89 (1993) 4239–4243. [68] J. Demarquay, J. Fraissard, Chem. Phys. Lett. 136 (1987) 314–318. [69] A. Hauser, Top. Curr. Chem. 233 (2004) 49–58. [70] S.M. Melnikov, A. Höltzel, A. Seidel-Morgenstern, U. Tallarek, J. Phys. Chem. C 117 (2013) 6620–6631.

[24] J.-P. Korb, B. Nicot, A. Louis-Joseph, S. Bubici, G. Ferrante, J. Phys. Chem. C 118 (2014) 23212–23218. [25] J.-P. Korb, B. Nicot, I. Jolivet, Microporous Mesoporous Mater. 269 (2018) 7–11. [26] J.-P. Korb, Prog. Nucl. Magn. Reson. Spectrosc. 104 (2018) 12–55. [27] F. Martini, S. Borsacchi, M. Geppi, M. Tonelli, F. Ridi, L. Calucci, Microporous Mesoporous Mater. 269 (2018) 26–30. [28] V.-V. Telkki, Magn. Reson. Chem. 56 (2018) 619–632. [29] K.R. Brownstein, C.E. Tarr, Phys. Rev. A 19 (1979) 2446–2453. [30] J.-H. Lee, C. Labadie, C.S. Springer Jr., G.S. Harbison, J. Am. Chem. Soc. 115 (1993) 7761–7764. [31] K.E. Washburn, P. Callaghan, Phys. Rev. Lett. 97 (2006) 175502. [32] J. Jeener, B.H. Meier, P. Bachmann, R.R. Ernst, J. Chem. Phys. 71 (1979) 4546–4553. [33] L. Monteilhet, J.-P. Korb, J. Mitchell, P.J. McDonald, Phys. Rev. E. 74 (2006) 061404. [34] J. Cox, P.J. McDonald, B.A. Gardiner, Holzforschung (2010) 64. [35] A. Haber, F. Casanova, S. Haber-Pohlmeier, B. Blümich, Vadose Zone J. 9 (2010) 893. [36] S.E. Mailhiot, F. Zong, J.E. Maneval, R.K. June, P. Galvosas, J.D. Seymour, J. Magn. Reson. 287 (2018) 82–90. [37] M. Van Landeghem, A. Haber, J.-B. D'Espinose De Lacaillerie, B. Blümich, Concepts Magn. Reson. 36A (3) (2010) 153–169. [38] J. Mitchell, J.D. Griffith, J.H.P. Collins, A.J. Sederman, L.F. Gladden, M.L. Johns, J. Chem. Phys. 127 (2007) 234701. [39] R. Song, Y.-Q. Song, M. Vembusubramanian, J.L. Paulsen, J. Magn. Reson. 265 (2016) 164–171. [40] E. Paciok, A. Haber, M. Van Landeghem, B. Blümich, Z. Phys. Chem. 226 (2012) 1243–1257. [41] R.D. Dortch, R.A. Horch, M.D. Does, J. Chem. Phys. 131 (2009) 164502. [42] O.V. Petrov, I. Furó, Prog. Nucl. Magn. Reson. Spectrosc. 54 (2009) 97–122. [43] C.L. Jackson, G.B. McKenna, J. Chem. Phys. 93 (1990) 9002–9011. [44] P. Pipilikaki, M. Beazi-Katsioti, Constr. Build. Mater. 23 (2009) 1966–1970. [45] S. Tong, Y. Dong, Q. Zhang, D. Elsworth, S. Liu, Energy Fuel. 31 (12) (2017) 13317–13328. [46] B. Zhou, Q. Han, P. Yang, Energy Fuel. 30 (11) (2016) 9122–9131. [47] D.A. Barskiy, A.M. Coffey, P. Nikolaou, D.M. Mikhaylov, B.M. Goodson, R.T. Branca, G.J. Lu, M.G. Shapiro, V.-V. Telkki, V.V. Zhivonitko, I.V. Koptyug, O.G. Salnikov, K.V. Kovtunov, V.I. Bukhtiyarov, M.S. Rosen, M.J. Barlow, S. Safavi, I.P. Hall, L. Schröder, E.Y. Chekmenev, Chem. Eur J. 23 (2017) 725–751.

74