and FTIR spectroscopy for characterization of light

Journal of Petroleum Science and Engineering 168 (2018) 256–262

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Journal of Petroleum Science and Engineering journal homepage: www.elsevier.com/locate/petrol

Application of high resolution NMR (1H and 13C) and FTIR spectroscopy for characterization of light and heavy crude oils

T

I.Z. Rakhmatullina, S.V. Efimova, V.A. Tyurina, A.A. Al-Muntaserb, A.E. Klimovitskiib, M.A. Varfolomeevb, V.V. Klochkova,∗ a b

Institute of Physics, Kazan Federal University, 18 Kremlevskaya St., Kazan, 420008, Russian Federation Department of Physical Chemistry, Butlerov Institute of Chemistry, Kazan Federal University, 18 Kremlevskaya St., Kazan, 420008, Russian Federation

A R T I C LE I N FO

A B S T R A C T

Keywords: Crude oil 1 H NMR spectroscopy 13 C NMR spectroscopy FTIR spectroscopy SARA fraction Functional group

High-resolution nuclear magnetic resonance (NMR) and Fourier transform infrared (FTIR) spectroscopy experiments were applied to obtain detailed information on the hydrocarbon chemistry of three light and three heavy crude oils. Quantitative fractions of aromatic molecules and functional groups constituting oil hydrocarbons were determined by 13C NMR spectroscopy and comparative analysis of the oil samples with different viscosity, origin and preliminary treatment. SARA composition and some important information about aromaticity, oxidation behavior, branching, aliphaticity and sulfurization of studied oil samples were obtained. Integral characteristics of high-resolution NMR and FTIR spectra have a great potential to study the structure and characterization of light and heavy crude oils, which could substitute present traditional fractionation procedures. Relationships between spectroscopic parameters obtained by high-resolution NMR and FTIR spectroscopy methods and crude oil compositions can be useful for fast prediction of crude oil properties due to different type of treatment, including thermal methods for enhanced oil recovery. Also quantitative proportions of functional groups obtained by NMR and spectral indices obtained by FTIR can be one of the criteria for developing fingerprint approach.

1. Introduction 1 H and 13C NMR spectroscopy is well-known recognized technique for establishing structural formulas, spatial and electronic structure of either first synthesized or isolated from natural raw materials individual organic compounds (Usachev et al., 2013a, 2013b, 2017a; Kononova et al., 2017; Rakhmatullin et al., 2015, 2017a; Abdrakhmanov et al., 2017; Khodov et al., 2017). In recent years, there has been a growing interest in using NMR spectroscopy for studying various objects in petroleum chemistry (Gao et al., 2017; Mondal et al., 2017; Alcazar-Vara et al., 2016; Smirnov and Vanyukova, 2014). The NMR method opens wide opportunities in studying the structure of oil disperse systems and determining their physical and chemical characteristics. The NMR method can help to determine aromatic and aliphatic hydrogen and carbon atoms in crude oil samples. Areas under resonance peaks in NMR spectra characterize the amount of 1H or 13C atoms of corresponding chemical types. Integration of the peaks and subsequent manipulations and calculations yield quantitative proton and carbon type analyses (Kushnarev et al., 1989). 1H and 13C NMR can be applied to obtain information on content of general functional

∗

Corresponding author. E-mail address: [email protected] (V.V. Klochkov).

https://doi.org/10.1016/j.petrol.2018.05.011 Received 19 January 2018; Received in revised form 28 April 2018; Accepted 1 May 2018 0920-4105/ © 2018 Elsevier B.V. All rights reserved.

groups (tertiary and primary carbon atoms, aromatic cores) and possible presence of olefins or water impurity (Rakhmatullin et al., 2017b). However, it has not been widely used yet for analysis of high-molecular weight oil samples because of their complex structural organization. Number of works on the NMR analysis of crude oils and petroleum products, including oil residues, is limited. This circumstance is largely due to the natural shift of interests towards the study of the properties of heavy oil, for which the possibilities of NMR are limited. In particular, the correlation between viscosity of oil and relaxation times in these objects is no longer observed clearly as for light oils, and the use of pulsed NMR method is becoming increasingly problematic. High resolution NMR spectroscopy has provided detailed chemical information on the proton and carbon chemistry of petroleum materials for over 60 years (Edwards, 2011). Today high resolution NMR instrumentation used to derive detailed multinuclear hydrocarbon information is based on superconducting magnet technologies (Derome, 1987; Rule and Hitchens, 2006; Kolosova et al., 2016; Usachev et al., 2017b; Galiullina et al., 2017). In the case of oil and petroleum products containing typically hundreds of compounds, mainly hydrocarbons, an important feature of NMR spectroscopy is a strict


I.Z. Rakhmatullin et al.

companies. Crude oils (1), (3), (5) and (6) were extracted by thermal enhanced oil recovery (EOR) methods.

correlation of integral intensities of separate groups of signals in certain chemical shifts ranges in 1H and 13C NMR spectra with the content of the corresponding molecular fragments (Holzgrabe, 2017; Poveda et al., 2014; Da Silva Oliveira et al., 2014). Note that approaches widely used in petrochemical methods of analysis, such as elemental (X-ray fluorescence, absorption and emission optical spectroscopy), fragment and component (IR spectroscopy, mass spectrometry, chromatography) analyses are indirect (unlike NMR) because they require using reference materials for quantitative investigations. It should also be noted that the quantitative 13C NMR spectrum is the only direct method for measuring aromaticity (Car) – relative percentage of aromatic carbon atoms in hydrocarbons (McBeath et al., 2011; Fergoug and Bouhadda, 2014; Lee and Glavincevski, 1999). SARA (saturates, aromatics, resins, and asphaltenes) analysis is another important application in crude oil composition studies, because these data are used for predicting the oil recovery using specific technology, the compatibility and stability of blends of crude oils to anticipate problems of sedimentation during storage and transportation (Aske et al., 2002). Some studies have revealed that NMR can be used for predicting SARA composition of crude oils (Molina et al., 2010; Sanchez-Minero et al., 2013). NMR results were used to obtain the concentration of aromatic-hydrogen and aromatic-carbon, from which aromaticity factors were computed and correlated to SARA analysis. This allowed developing correlations for predicting SARA composition of crude oils with a wide range of API gravity (10–33°) (Sanchez-Minero et al., 2013). FTIR spectroscopy has been an important analytical tool for determining the chemical composition and structures of the components of crude oils and their refined products for more than four decades (Brown et al., 1975). Many of the early investigations emphasized the isolation of various types of hydrocarbons, e.g., paraffins, naphthenes, and olefins, from petroleum. Infrared spectra of all types of hydrocarbons have been measured and catalogued by the National Bureau of Standards, API Project 44 (American Petroleum Institute Project 44). Since petroleum and the products of petroleum are mixtures of many hydrocarbons, much emphasis has been placed on using IR spectra to quantitatively measure the composition of mixtures. In addition to measuring the composition of hydrocarbon mixtures this method has been also used in determining the degree of branching in paraffins, e.g., methods have been suggested to determine the number of CH3 and CH2 groups in hydrocarbons (Rericha and Horak, 1967, 1968). The aim of this study was to determine the qualitative and quantitative composition of six different crude oil samples by means of the high-resolution 13C NMR spectroscopy, FTIR spectroscopy and SARA analysis and to find some general relationship between spectroscopic parameters and crude oil composition. These relationships can be useful for fast prediction of crude oil properties changing upon different type of treatment, including thermal methods for enhanced oil recovery.

2.1. NMR spectroscopy NMR experiments on the studied oil samples (1–6) were performed on a Bruker Avance II 500 NMR spectrometer. Field lock and shimming were achieved using the deuterium signal from D2O in a glass capillary placed into the 5 mm NMR tube together with the investigated oil sample. All samples were studied without dilution. 1H NMR spectra were recorded using 30° pulses (zg30 pulse program); acquisition time was 4.7 s; pre-scan delay was 6.5 μs, and the relaxation delay between scans was 2 s; spectrum width was 12.0 ppm (6000 Hz); 400 scans were accumulated. 13C NMR spectra were recorded using 90° pulses with inverse gated broadband proton decoupling (zgig pulse program); relaxation delay between pulses was 9 s (and acquisition time was 3.5 s); spectrum width was set to 220.0 ppm; number of scans was 3200. Exponential digital filter with the lb parameter of 10 Hz was applied to process 13C NMR spectra prior to Fourier transformation. Measurements were made at the temperature of 30 °C. All NMR spectra were integrated after baseline correction, and a mean of at least three integration values were taken for each calculation. The relative standard deviation of the results of manual integration did not exceed 3%. Estimation of molar fractions of primary, secondary, tertiary, aromatic and quaternary carbons was carried out in a way similar to our previous work (Rakhmatullin et al., 2017b). Information obtained by quantitative integration of aromatic signals in individual spectral ranges is represented by the fraction of the corresponding carbon atoms relative to their total number. Fraction of aromatic carbons Car can be straightforwardly found from NMR spectra:

Car =

Table 1 Viscosity and extraction method of studied oil samples. Extraction method

1 2 3 4 5 6

7.5 5.95 37.2 106 2420 49700

high pressure air injection (HPAI) primary recovery high pressure air injection (HPAI) primary recovery SAGD cyclic steam simulation (CSS)

Ct = ((1.04It − 0.034Isq)/(It + Isq + Ip))(1 − Car ),

(2)

Cp = ((1.02Ip − 0.006Isq)/(It + Isq + Ip))(1 − Car ),

(3)

Csq = ((1.04Isq − 0.04It − 0.02Ip)/(It + Isq + Ip))(1 − Car ),

(4)

where Ct is the fraction of tertiary carbons; Cp – fraction of primary carbons; Csq – fraction of secondary and quaternary carbons (due to the complexity of separation of methylene and methine signals, their summary contents is estimated); It is the total integral intensity of tertiary (CH) groups; Isq – total integral intensity of secondary (CH2) and quaternary groups, Ip – total integral Intensity of primary (CH3) groups in 13C NMR spectrum of the oil sample. Mean chain length (MCL) was calculated as:

Viscosity of the samples used in this study is reported along with the extraction method in Table 1. There are three low viscosity oils (1), (2) and (3), one semi-heavy oil (4), and two high viscosity oils (5) and (6). These samples were provided by Tatneft, Zarubezhneft and RITEK oil

Viscosity, mPa*s

(1)

where Car is the fraction of aromatic carbons, Iar is the total integral intensity of aromatic carbons and Ij is the integral intensity of all functional groups in 13C NMR spectrum of the sample. It is impossible to obtain unambiguous information on the content of hydrocarbons (alkanes, cyclanes) from 13C NMR spectra, although this information is contained in the fragmentary composition, which can be determined with high accuracy (Kushnarev et al., 1989). If integral intensities of individual groups signals in the 13C NMR spectrum are known, then corresponding molar fractions of tertiary, primary, secondary and quaternary carbons can be calculated by the following formulas (Kalabin et al., 2000):

2. Materials and methods

Sample number

Iar , ∑j I j

Csq + Ct ⎞ MCL = 2∗ ⎜⎛ ⎟ + 2. ⎝ Cp ⎠

(5)

The aromaticity factor (FCA) can be calculated from the equation (Sanchez-Minero et al., 2013):

FCA = 257

Car , Car + Cal

(6)



where Cal = Cp + Csq + Ct is the total aliphatic carbon content. 2.2. FTIR spectroscopy Infrared spectra (4000–600 cm−1) were obtained using a Vertex 70 FTIR spectrometer (Bruker, Germany) equipped with a single reflection ZnSe crystal ATR accessory (MIRacle, PIKE Technologies, USA), purged under dry air to remove atmospheric water vapor. Background spectra of 128 scans at a resolution of 4 cm−1 were subtracted from the sample spectra. The data was processed in the OPUS 7.2 (Bruker) program. Spectral coefficients (C1-5) were obtained as the ratios of the optical density values (D) at the maxima of the corresponding absorption bands according to the following equations (Permanyer et al., 2002; Borrego et al., 1996; Kayukova et al., 2012; Abdrafikova et al., 2013; AlMuntaser et al., 2017): Fig. 1. SARA separation scheme.

C1 =

D1600 , D720

(7)

C2 =

D1710 , D1465

(8)

C3 =

D1380 , D1465

(9)

C4 =

D720 + D1380 , D1600

(10)

C5 =

D1030 , D1465

(11)

that during the oxidation process in the crude oil 1, some amount of water appeared. The samples 3, 5 and 6 were also recovered by thermal methods. The presence of the water protons signal in samples 5 and 6 was also observed due to formation of strong water emulsions in steam injection processes. In the 1H NMR spectra of these samples, in addition to broadening of resonance lines due to increased viscosity, weak signals from unsaturated hydrocarbon groups in the olefinic area were also revealed for the treated samples 3 and 5. For a more detailed study of these samples, 13C NMR spectroscopy was applied. The 13C NMR spectra of crude oils 1–6 are shown in Fig. 3. 13 C NMR spectroscopy is much more informative thanks to the wide range of chemical shifts (200 ppm) of non-equivalent 13C nuclei (Breitmaier and Woelter, 1986; Friebolin, 1991). Moreover, broadband decoupling between 1H and 13C nuclei facilitates interpretation of 13C NMR spectra. This method can be applied for analysis of mixtures containing molecules of known type (Makhiyanov and Safin, 2006). Crude oil contains vast number of compounds; however, spectral width of 13C spectra still allows classifying them into several classes even if individual components cannot be identified (Kalabin et al., 2000). 13C NMR spectra contain numerous distinguishable signals, which can be assigned to different typical regions and thus give information on the fractions of primary (methyl groups CH3) – Cp, secondary (methylene groups CH2) – Cs, tertiary (methine groups CH) – Ct, quaternary (C) – Cq and aromatic Car types of carbon atoms. Estimating of the molar content of various carbon groups (Cp, Csq, Ct, Car) made by integrating of the corresponding regions in 13C NMR spectra was carried out in a way similar to our previous work (Rakhmatullin et al., 2017b). Table 2 shows the obtained results. A quantitative analysis of the composition of the oil samples studied by NMR showed that for more viscous oils (5, 6), on the one hand, a small decrease in the concentration of carbon groups in the aliphatic region (Cp, Csq) is observed, and on the other hand, the concentration of aromatic groups (Car) increases 3–4 times compared to light oils (1, 2). For oils not exposed to the heat treatment (2, 4), a decrease in the concentration of tertiary groups (Ct) and increase in the concentration of secondary groups (Csq) is observed. Also, with increasing viscosity, there is an obvious tendency to increase of the aromaticity factor (FCA) and mean chain length (MCL). Aromaticity factor can be used for developing correlations for predicting SARA composition of studied oils by NMR spectroscopy (Sanchez-Minero et al., 2013).

where D1600 is the optical density of arenes; D1710 is the optical density of oxidation products; D1380 of alkanes (methyl CH3– groups); D720 – of long methylene chains (CH2 > 4); D1030 of sulfoxide groups; D1465 of alkanes (methylene CH2– groups); C1 is the aromaticity index, C2, – oxidation index, C3, – branching index, C4, – aliphaticity index, and C5 is the sulfurization spectral coefficient (Abdulkadir et al., 2016). 2.3. SARA fractionation According to ASTM 2007 standard, oil samples were divided into their fractions: saturates, aromatics, resins, and asphaltenes (SARA). Asphaltenes were precipitated under the effect of a 40-fold amount of nalkane solvent, heptane. Heptane is chosen to preserve the light fractions of oil. Maltenes were separated by means of liquid-adsorption chromatography or open column on aluminum oxide calcined at 420 °C into saturated hydrocarbons via their elution with heptane as an adsorbent. Similarly, aromatic compounds were separated via their elution with toluene and resins, which were extruded from the adsorbent with the aid of a solvents mixture, namely, toluene and isopropyl alcohol taken in equal proportions. The SARA separation scheme and gravimetric method is illustrated in Fig. 1 (Bissada et al., 2016). 3. Results and discussion 3.1. NMR spectroscopy analysis The 1H NMR spectra of studied oil samples 1–6 are shown in Fig. 2. There are characteristic spectral regions of aliphatic and aromatic proton signals in the high and low fields. Evidently, as the viscosity increases, resonance signals broaden. The samples 1 and 2 are similar in origin and viscosity, but the first was subjected to heat treatment. In the middle area of the spectra protons of water (4.89 ppm) give a peak next to the proton signal of water in the capillary (4.68 ppm). The only difference between these spectra (1, 2) is due to the fact that the signal from water protons has a four times greater integral intensity in sample 1 than in thermally untreated sample 2: (H2O:H2Oref = 8:1) in sample 1 vs. (H2O:H2Oref = 2:1) in sample 2. This fact agrees with the suggestion

3.2. SARA analysis of studied oil samples One of the common ways for studying the group composition of oil is SARA analysis; analysis scheme is to divide an oil sample into its saturate, aromatic, resin, and asphaltene (SARA) fractions (Fig. 1). The saturate fraction consists of nonpolar material including linear, branched, and cyclic saturated hydrocarbons. Aromatics, which contain one 258



Fig. 2. 1H (500 MHz) NMR spectra of oil samples 1–6.

absorption pattern of the material. The infrared spectra are dominated by the stretching aliphatic bands (v (CH3+CH2)) at 2923 and 2852 cm−1 and the deformation bands of methyl (1365 cm−1) and both methyl and methylene groups (δ- (CH3+CH2), 1465 cm−1). They also display distinct bands assigned to carbonyl and/or carboxyl groups (v(C = 0), 1705 cm−1) which is observed in the case of 6 sample and to the stretching vibration of aromatic carbons (v (C=C), 1605 cm−1). In the region 700-900 cm−1, various bands occur assigned to the out-ofplane deformation vibration of one isolated aromatic C—H bond (γ (CHar), 870 cm−1), two or three adjacent aromatic C—H bonds (γ (CHar), 814 cm−1), and four adjacent aromatic C—H bonds (γ (CHar), 750 cm−1). In addition, the band assigned to the skeletal vibration of

or more aromatic rings, are more polarizable. The remaining two fractions, resins and asphaltenes, have polar substituents. The distinction between the two is that asphaltenes are insoluble in an excess of heptane whereas resins are miscible with heptane (Tianguang et al., 2002; Ashoori et al., 2017). The results of SARA analysis are reported in Table 3. 3.3. FTIR spectroscopy analysis 3.3.1. FTIR spectra of oil samples The FTIR spectra of studied oil samples 1–6 are shown in Fig. 4. FTIR spectroscopy generates unique spectral signatures related to the

Fig. 3.

13

C (125 MHz) NMR spectra of crude oil samples 1–6. 259



studied oils by IR spectroscopy, spectrometric indices (C1-C5) were calculated according to equations (7)–(11) (Permanyer et al., 2002; Borrego et al., 1996; Kayukova et al., 2012; Al-Muntaser et al., 2017). The optical density values (D) were obtained as a maxima peak heights of selected and corresponding absorption bands. Spectrometric indices were calculated from peak heights of selected infrared bands allow for a better comparison of the spectra. This procedure is more reliable as it takes into consideration several vibrations of the same type occurring simultaneously (Permanyer et al., 2002). Tables 4 and 5 presents the results of IR spectral analysis of the studied oil samples. As follows from the data in Table 5, an increasing in the index of aromaticity (C1) and aliphaticity (C4) was observed depending on their viscosity, but it is also noticeable that sample 4 falls out from this tendency. For the index of oxidation C2 some changes was observed for oil samples depending on their extraction methods and viscosity. Sample 4, as well as sample 2, was not subjected to heat treatment. At the same time, these samples differ remarkably in viscosity. So, such a sharp difference in the spectral indices for a more viscous and untreated sample 4 can be explained by its original characteristics. However, some ambiguous changes are observed for the remaining spectral indices: branching (C3) and sulfurization (C5), which can be related to the difference in the origin of the investigated samples. In order to reconcile the data obtained by both methods (NMR and FTIR), the correlation between the Car value from NMR and C1 coefficient based of optical data was built. (Fig. 5). Both values refer to the relative amount of aromatic cores, normalized by the total intensity of carbon signals in NMR or to long methylene chains in FTIR. While the aromaticity degrees obtained by two methods vary in a concerted way for samples 1, 3, 5 and 6, the two samples 2 and 4 stand out against this correlation. It is significant that the latter two samples are primary recovery oils, while products 1, 3, 5, 6 were extracted by thermal EOR methods.

Table 2 Molar fractions (%) of primary (Cp), secondary and quaternary (Csq), tertiary (Ct), aromatic (Car) groups, aromaticity factor (FCA) and mean chain length (MCL) of aliphatic hydrocarbons based on13C NMR spectra of oil samples 1–6. Group type

Cp Csq Ct Car FCA MCL

Sample number 1

2

3

4

5

6

32.0 49.5 12.1 6.4 0.064 5.8

33.6 54.7 4.8 6.9 0.069 5.5

24.5 53.0 10.0 12.5 0.125 7.1

18.8 56.1 8.4 16.7 0.167 8.9

17.5 48.9 12.8 20.8 0.208 9.0

14.7 40.4 15.8 29.1 0.291 9.6

Table 3 SARA analysis (%) of studied oil samples. Sample number

1

2

3

4

5

6

Saturates Aromatics Resins Asphaltenes

65.51 24.60 9.21 0.68

67.70 23.52 8.29 0.49

49.79 31.89 14.87 3.46

59.62 26.71 12.08 1.59

26.20 40.60 28.49 4.70

30.96 39.18 14.17 15.69

4. Conclusions Based on the results of 1H, 13C NMR and FTIR spectroscopy experiments, the important data about the “structure – property” characterization of light and heavy oil samples were obtained, indicating a great potential of these methods. From the 1H NMR characterization of oils with different origin, viscosity and treatment performed, the following conclusions can be made: crude and refined oils are distinguishable both by estimating the ratio of proton concentrations of water molecules and by observing signals in the olefinic area of spectra. From the 13C NMR characterization of oils with different origin, viscosity and treatment, the following conclusions can be pointed out: a decrease in the concentration of tertiary carbon groups is observed in oil samples which were not subjected to heat treatment; in the transition from light to heavy oils, a decrease in the concentration of primary and an increase in the concentration of aromatic carbons are observed. An increase in the degree of aromaticity and aliphaticity is observed from the FTIR spectroscopy analysis as we go from light to heavy oil samples; also some changes in the degree of branching, oxidation, sulfurization were identified. Thus, these results indicated that combined use of high-resolution NMR and FTIR spectroscopy has a great potential to study the structure and characterization of light and heavy crude oils, which could substitute present traditional fractionation procedures. Relationships between spectroscopic parameters obtained by the above methods and crude oil compositions can be useful for fast prediction of crude oil properties changing upon different type of treatment, including thermal methods for enhanced oil recovery. Also, quantitative proportions of functional groups obtained by NMR and spectral indices obtained by FTIR can be one of the criteria for developing fingerprint approach.

Fig. 4. FTIR spectra of oil samples 1–6.

more than four methylene groups (r (CH2)n, 720 cm−1) was observed. Further, the most important peaks of the spectra are labelled including the aliphatic hydrocarbons, the aromatics and polyaromatics as well as the carbonyls and aromatics (Mouillet et al., 2008; Feng et al., 2016; Van den bergh, 2011). By moving from oil with low viscosity to high viscosity, there is an increasing in the intensities of the absorption bands characteristic of aromatic and polyaromatics structures (1600 and 900-730 cm−1) which, along with changes in their spectral indices (Tables 4 and 5), indicates an increase in the degree of their aromaticity. 3.3.2. Spectrometric indices calculation To study the structural-group composition and characterization of 260



Table 4 Optical density values (D) at the maxima of absorption bands of oil samples 1–6 according to FTIR spectroscopy data. Sample number

Optical densities at the maxima of absorption bands

1 2 3 4 5 6

1710 cm−1

1600 cm−1

1465 cm−1

1380 cm−1

1030 cm−1

720 cm−1

0.217 0.215 0.023 0.025 0.035 0.203

0.143 0.094 0.257 0.156 0.190 0.239

0.859 0.820 0.771 1.826 0.816 1.507

0.514 0.494 0.435 0.972 0.497 0.902

0.280 0.267 0.135 0.203 0.175 0.117

0.337 0.277 0.464 0.540 0.338 0.393

Table 5 Spectral coefficients (C) of oil samples 1–6 according to FTIR spectroscopy data. Sample number

1 2 3 4 5 6

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Spectrometric indices C1

C2

C3

C4

C5

0.424 0.339 0.554 0.288 0.560 0.608

0.253 0.262 0.030 0.014 0.043 0.135

0.598 0.602 0.564 0.532 0.609 0.599

3.800 3.728 7.574 9.717 5.832 5.418

0.326 0.326 0.175 0.111 0.354 0.078

Fig. 5. Correlation between Car (from NMR data) and C1 (from FTIR data) for oil samples 1–6.

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