Table of Contents

Cover

Title Page

Dedication

List of Contributors

Preface

Chapter 1: Rheological Characterization of Crude Oil and Related Products

1.1 Introduction
1.2 Sample Preparation for Rheological Characterization
1.3 Rheological Tests
1.4 Potential Sources of Errors
References

Chapter 2: Optical Interrogation of Petroleum Asphaltenes: Myths and Reality

2.1 Introduction
2.2 Mythical “Characteristic Signatures” of Asphaltenes in Optical Analytical Methods
2.3 Misconceptions about the Properties of UV/Vis Absorption Spectra of Asphaltenes
2.4 Current State of Knowledge about Asphaltene Monomers and Primary Asphaltene Aggregates
References

Chapter 3: ESR Characterization of Organic Free Radicals in Crude Oil and By-Products

3.1 Introduction
3.2 Organic-Free Radicals in Crude Oil
3.3 ESR of Crude Oil
3.4 By-Product Oil by ESR
3.5 ESR and Calculations on the Electronic Structure of Free Radicals in Oil By-Products
References

Chapter 4: High-Field, Pulsed, and Double Resonance Studies of Crude Oils and their Derivatives

4.1 Introduction
4.2 EPR: Basic Principles and Magnetic Interactions
4.3 EPR Pulse Sequences
4.4 Application Examples
4.5 Conclusion
Acknowledgments
References

Chapter 5: NMR Spectroscopic Analysis in Characterization of Crude Oil and Related Products

5.1 Introduction
5.2 ¹H NMR and ¹³C NMR Spectroscopy Analysis Methods
5.3 NMR Techniques
5.4 Application of NMR Analysis in Characterization of Crude Oil and Related Products
5.5 Asphaltene Characterization using NMR Techniques
5.6 Conclusions
References

Chapter 6: NMR Spectroscopy in Bitumen Characterization

6.1 Introduction
6.2 ¹H and ¹³C NMR Spectroscopy
6.3 Phosphorus-31 NMR Spectroscopy
6.4 NMR Imaging and Solid-State NMR
6.5 Conclusion
References

Chapter 7: Applications of Low Field Magnetic Resonance in Viscous Crude Oil/Water Property Determination

7.1 Introduction
7.2 Background for NMR Measurements
7.3 Fluid Content in Oil/Water Systems
7.4 Oil Viscosity from NMR
7.5 Fluid Saturations and Viscosity in Porous Media
7.6 NMR in Oil-Solvent Systems
7.7 Summary of NMR and Fluid Property Measurements
Acknowledgments
References

Chapter 8: Application of Near-Infrared Spectroscopy to the Characterization of Petroleum

8.1 Introduction
8.2 Sample Handling and Preparation
8.3 Near-Infrared Spectroscopy
8.4 Chemometrics
8.5 Commercial NIR Equipment and Industrial Applications
8.6 Conclusions
References

Chapter 9: Raman and Infrared Spectroscopy of Crude Oil and its Constituents

9.1 Introduction
9.2 Fundamentals of Raman and Infrared Spectroscopy
9.3 Infrared Spectroscopy
9.4 Raman Spectroscopy
9.5 Evaluation of Vibrational Spectra
9.6 Applications
9.7 Conclusion
References

Index

End User License Agreement

List of Illustrations

Chapter 1: Rheological Characterization of Crude Oil and Related Products

Figure 1.1 A temperature ramp performed with a crude oil sample.
Figure 1.2 Example of the flow curves of a crude oil at different temperatures.
Figure 1.3 A stress amplitude sweep test performed with a crude oil sample.

Chapter 2: Optical Interrogation of Petroleum Asphaltenes: Myths and Reality

Figure 2.1 Earlier structural model of a typical asphaltene molecule.
Figure 2.2 Currently popular structural models of a typical asphaltene “monomer” (left) and of an asphaltene “micelle”/“nanoaggregate” (right).
Figure 2.3 Emerging structural models of small asphaltene “monomers” (left) and of asphaltene supramolecular aggregates (right).
Figure 2.4 Representative examples of published “evidence” for “characteristic peaks” of asphaltenes (see text).
Figure 2.5 Employment of “characteristic absorbance maximum” for analytical characterization of crude oils by “pattern recognition method.” Source: Adapted from Lai et al. (1993).
Figure 2.6 Highly suspect coincidence of published absorbance spectra for toluene solutions of asphaltenes and crude oils of diverse origin (see text).
Figure 2.7 UV/Vis absorption spectra of crude oil solutions in toluene, in CCl₄ and of a solvent-free crude oil.
Figure 2.8 A scheme for determining the molecular weight of asphaltenes from the position of “characteristic monomer peaks” in fluorescence emission spectra.
Figure 2.9 A scheme of determination of the predominant fused aromatic systems in asphaltenes from the position of “characteristic monomer peaks” in fluorescence emission spectra.
Figure 2.10 Sharp increase of short-wavelength absorbance in asphaltene solutions. Source: Adapted from Mullins (2010).
Figure 2.14 The original asphaltene absorbance data (re-plotted from Castillo et al., 2001) with a “perfectly linear” concentration dependence. Source: Adapted from Castillo et al. (2001).
Figure 2.11 Significant shift of “characteristic monomer peak” after proper correction of as-measured (raw) fluorescence emission spectrum for “inner filter”/self-absorption effects.
Figure 2.12 Another example of the necessity of “inner filter”/self-absorption corrections in analysis of asphaltenes by fluorescence emission measurements. Source: Evdokimov, Fesan, and Losev (2016).
Figure 2.13 Representative fluorescence emission spectra from benzene solutions with non-aggregated and aggregated asphaltenes. Source: Adapted from Evdokimov, Fesan, and Losev (2016).
Figure 2.15 The data from Figure 2.14 presented in terms of asphaltene absorptivity. Asphaltene aggregation effects become clearly seen.
Figure 2.16 The earliest experimental evidence of aggregation-dependent asphaltene absorptivity in highly dilute solutions. Source: Adapted from Yokota et al. (1986).
Figure 2.17 Experimental evidence for strong molecular aggregation effects on asphaltene absorptivity in toluene solutions. Source: Adapted from Evdokimov, Eliseev and Akhmetov (2003b); Evdokimov (2008).
Figure 2.18 “Representative” optical absorption spectra of a crude oil and of asphaltenes (re-plotted from Mullins, Mitra-Kirtley, and Zhu, 1992). Each spectrum is constructed from spliced spectra of multiple samples with various dilutions, an artificial “chimera” which does not belong to any real substance.
Figure 2.19 Schematic representation of a molecular system described by the standard AS model. All electronic transitions are within energy levels of a specific large molecule (chromophore).
Figure 2.20 The main regions of continuous absorption spectra in the AS model. Source: After Adachi (1999) and Singh and Shimakawa (2003).
Figure 2.21 Schematic representation of a molecular system implicitly accepted in the revised AS model. Note the impossible electronic transitions between energy levels of different molecules.
Figure 2.22 Left, a “continental”-type asphaltene molecule with a single large PAH chromophore. Source: Rogel (1995). Reproduced with permission of Elsevier. Right, absorption spectra of large PAH molecules. Source: Fetzer (2000). Reproduced with permission of John Wiley & Sons, Ltd.
Figure 2.23 (a) Re-plotted simulated and experimental absorption spectra of asphaltenes. (b) The large-wavelength parts of these spectra. Source: Adapted from Ruiz-Morales and Mullins (2009).
Figure 2.24 Schematic illustration of direct experimental proof that asphaltene “absorbance” in the visible range is due to scattering by molecular aggregates. 1. As-prepared asphaltene solution. 2. Solution with aggregates removed by ultrafiltration. Source: Adapted from Dechaine and Gray (2011a).
Figure 2.25 Schematic illustration of typical shapes of the as-measured (raw) UV/Vis spectra for samples with different asphaltene concentrations. Source: After Derakhshesh (2012) and Derakhshesh, Gray, and Dechaine (2013).
Figure 2.26 Schematic illustration of “raw” spectra from Figure 2.25 converted to the Rayleigh scattering form. Source: After Derakhshesh (2012) and Derakhshesh, Gray, and Dechaine (2013).
Figure 2.27 Long equilibration times in solutions with asphaltene content 15 mg/L. Data points: the values of RI increments (RI_solution–RI_toluene), normalized to their equilibrium values after a period of 10 days. Source: Adapted from Evdokimov and Fesan (2016).
Figure 2.31 Effects of asphaltene concentration on RI increment in non-equilibrated (aged for 3 h) and in equilibrated (aged for 10 days) toluene solutions. Source: Based on the original data of Evdokimov and Fesan (2016).
Figure 2.28 Concentration dependence of absorptivity at λ = 670 nm in dilute toluene solutions of solid asphaltenes. Source: Adapted from Evdokimov, Eliseev, and Akhmetov (2003b).
Figure 2.29 A representative part of RI “time series.” Source: Adapted from Evdokimov and Fesan (2016).
Figure 2.30 Statistical analysis of the measured RI “time series.” Source: Adapted from Evdokimov and Fesan (2016).
Figure 2.32 Multiple equilibrium structural states of asphaltenes revealed by concentration effects on mean RI in the measured RI “time series.” Source: Based on the original data of Evdokimov and Fesan (2016).
Figure 2.33 Close similarity of structural states in solutions of asphaltenes from crude oils of diverse geographical/geological origin. Source: Adapted from Evdokimov and Fesan (2016).
Figure 2.34 Multiple equilibrium structural states of asphaltenes revealed by concentration effects on standard RI deviation in the measured RI “time series.” Source: Based on the original data of Evdokimov and Fesan (2016).
Figure 2.35 Fluorescence emission spectra from asphaltene solutions in toluene, obtained with 265 nm excitation. The vertical dashed lines delimit characteristic ranges of emission wavelengths for monomers and two types of primary aggregates (see text).
Figure 2.36 Non-monotonic effects of asphaltene concentration on the relative intensity of fluorescence emission from all primary molecular aggregates in toluene solutions of asphaltenes.
Figure 2.37 The effects of asphaltene concentration on the relative intensity of fluorescence emission from asphaltene aggregates with smaller fluorophores (ASF) with respect to intensity of emission from aggregates with larger fluorophores (ALF).
Figure 2.38 Non-monotonic effects of asphaltene concentration on the relative intensity of fluorescence emission from primary asphaltene aggregates. Source: Based on original data from Zhang (2010) and Zhang et al. (2014).
Figure 2.39 Non-monotonic effects of asphaltene concentration on the relative intensity of the peak from the heaviest molecular aggregates in mass spectra of asphaltenes. Source: Based on original data from McKenna et al. (2013).
Figure 2.40 Suggested sites of “loosely” bound (1) and “tightly” bound (2) MP molecules in asphaltene supramolecular aggregates. Source: Adapted from Dechaine and Gray (2011a).
Figure 2.41 Absorbance spectra for asphaltene solutions in benzene with concentrations indicated in the Figure Absorbance peak at ca. 410 nm is from vanadyl petroporphyrins. Additional analysis of experimental data from Evdokimov, Fesan, and Losev (2016).
Figure 2.42 Evaluation of the relative intensity of the Soret absorption peak of vanadyl porphyrins in Figure 2.41.
Figure 2.43 Effects of asphaltene concentration on the intensity of porphyrin absorption peak in solutions of solid asphaltenes and in solutions of the parent crude oil. Source: Additional analysis of experimental data from Evdokimov, Fesan, and Losev (2016).
Figure 2.44 “Consecutive aggregation” scheme with parental relationship between asphaltene aggregates formed at increasing concentrations.
Figure 2.45 Scheme with autonomous routes to independent systems of asphaltene aggregates at different concentrations.

Chapter 3: ESR Characterization of Organic Free Radicals in Crude Oil and By-Products

Figure 3.1 ESR spectrum for Kuwait crude oil and signal of the free radical showed in another scale of magnetic field and intensity.
Figure 3.2 Energy levels of a single electron in the presence of an external magnetic field.
Figure 3.3 ESR spectrum as a first derivative curve (solid line) and absorption curve (dotted line).
Figure 3.4 Schematic diagram of the hf splitting for unpaired electron interaction with a nucleus of nuclear spin I = 1/2.
Figure 3.5 Resonance lines at different magnetic fields (H₀₁ and H₀₂) for unpaired electron interaction with a nucleus of nuclear spin I = 1/2 and indication to the hf coupling constant (A).
Figure 3.6 Free-radical ESR spectra of Arabian crude oil at room temperature obtained in: (a) X- band, (b) Q- band, (c) W- band; ΔH_1/2 is the half height separation of the ESR derivative peak. Source: Di Mauro, Guedes, and Nascimento (2005). Reproduced with permission of Springer.
Figure 3.7 Free-radical ESR spectra of Colombian crude oil at room temperature obtained in: (a) X- band, (b) Q- band, (c) W-band. Source: Di Mauro, Guedes, and Nascimento (2005). Reproduced with permission of Springer.
Figure 3.8 Linewidth $c03-math-014$ of the free-radical signal versus microwave frequency of ESR spectra recorded in the X-, Q- and W-bands at room temperature. ▪, $c03-math-015$ (Arabian petroleum); •, $c03-math-016$ (Colombian petroleum); ▴, $c03-math-017$ (Arabian petroleum); ▾, $c03-math-018$ (Colombian petroleum). Source: Di Mauro, Guedes, and Nascimento (2005). Reproduced with permission of Springer.
Figure 3.9 ESR spectrum of marine diesel in X-band at room temperature, showing the hf separation into seven lines owing to the interaction between six equivalent strongly coupled protons, and each of the seven lines is resolved into four lines owing to the three weakly coupled protons. Source: Di Mauro, Guedes, and Piccinato (2007). Reproduced with permission of Springer.
Figure 3.10 Energy diagram of a free radical in marine diesel (bunker).
Figure 3.11 Structural representation to perinaphthenyl radical indicating 1 to 9 hydrogen atoms responsible for the hf splitting observed in marine diesel spectrum.
Figure 3.12 Comparison between ESR spectrum of marine diesel. (a) ESR spectrum of marine diesel (older sample) at 9.37 GHz at room temperature. (b) ESR spectrum of marine diesel (fresh sample). Source: Piccinato, Guedes, and Di Mauro (2009). Reproduced with permission of Springer.
Figure 3.13 (a) ESR spectra of marine diesel (older sample) at 9.37 GHz in the temperature range from 301 to 378 K. (b) Resolved hf lines at 383 K. Source: Piccinato, Guedes, and Di Mauro (2009). Reproduced with permission of Springer.
Figure 3.14 Spectra subtraction for analysis of ESR hf lines. (a) Unresolved line simulated by the software WINEPR SimFonia. (b) Overlap of the simulated spectrum and marine diesel spectrum at 383 K for subtraction of the unresolved line. (c) Result of the spectra subtraction.
Figure 3.15 (a) Simulation of the septet-quartet ESR spectrum. (b) Simulation of the sextet-quartet ESR spectrum. (c) Simulation of the quintet–quartet spectrum. (d) Superposition of the septet–quartet, sextet–quartet, and quintet–quartet with weight percentages of the 53.5, 30.0, and 16.5%, respectively.
Figure 3.16 Structures of the phenalenyl radical (a) and phenalenyl derivatives (b and c).
Figure 3.17 Superposition of theoretical model, with three groups of lines (dotted line), and experimental spectrum (solid line). Source: Piccinato, Guedes, and Di Mauro (2009). Reproduced with permission of Springer.
Figure 3.18 hf coupling constants in gauss for the hydrogen atoms of the perinaphthenyl radical obtained using DFT. Source: Piccinato et al. (2015). Reproduced with permission of John Wiley & Sons, Ltd.
Figure 3.19 Structure optimized via DFT showing the values of the hf coupling constants of the hydrogen atoms. (a) Hydroxyperinaphthenyl radical. (b) Dimethylperinaphthenyl radical. Source: Piccinato et al. (2015). Reproduced with permission of John Wiley & Sons, Ltd.

Chapter 4: High-Field, Pulsed, and Double Resonance Studies of Crude Oils and their Derivatives

Figure 4.1 Types of paramagnetic centers.
Figure 4.2 The variety of the modern commercially realized EPR techniques. Source: Stoll and Schweiger (2006). Reproduced with permission of Elsevier.
Figure 4.3 Reconstruction of the first EPR machine of E. K. Zavoisky operating at 10 MHz on which the first EPR spectrum in the world was observed in 1944. Courtesy of Igor Silkin, the keeper of E. K. Zavoisky Museum at Kazan Federal University. (1) Transformer. (2) Solenoid supplied by transformer and producing a low-frequency magnetic field that substituted constant magnetic field in this setup. (3) Ampoule with sample inserted into resonator–radio frequency coil, oriented perpendicular to solenoid axis. (4) Autodyne generator working at 10 MHz and signal preamplifier. (5) Oscilloscope. (6) A rheostat for adjusting the current through the transformer. (7) Ammeter used to control the magnetic field inside the solenoid, which is proportional to the alternating current value at the transformer's secondary coil.
Figure 4.4 Typical EPR spectrum of crude oil sample at X-band at near room temperature.
Figure 4.5 The simplified scheme of the conventional EPR spectrometer.
Figure 4.6 Commonly used EPR tubes for PDS samples in the W-band and X-band EPR spectrometers.
Figure 4.7 The Zeeman effect. An increasing magnetic field is applied in the presence of a fixed microwave frequency. When the resonance condition is reached (position of the arrow), an absorption occurs between the lower energy level (spin magnetic quantum number m_s = −1/2) and the upper energy level (m_s = +1/2). The energy difference is quantized and is equivalent to the term gβH.
Figure 4.8 Schematic representation of a vanadyl porphyrin molecule backbone. The orientations of nitrogen hf (A) and quadrupole coupling (Q) tensors derived from DFT calculations are shown for a selected nuclei. Spatial distribution of spin density is visualized as an isosurface. X–Y–Z axes of the molecular frame are shown with the Z axis perpendicular to the porphyrin plane. As shown, the calculated g_z is collinear with the molecular Z axis.
Figure 4.9 The energy levels and the corresponding absorption EPR spectrum for VO²⁺ complex calculated for the microwave frequency ν = 94 GHz, g_|| = 1.963, g_⊥ = 1.985, A_|| = 470 MHz, A_⊥ = 150 MHz. Particular contributions from every EPR transition are color marked. Calculations are done in EasySpin package for Matlab (Stoll and Schweiger, 2006).
Figure 4.10 Mims pulse sequence at microwave and radio frequencies used to obtain the ENDOR spectra as a function of stimulated electron spin echo amplitude from the frequency of RF pulse.
Figure 4.11 The scheme of fractionation of asphaltenes.
Figure 4.12 Typical W-band EPR spectrum of asphaltene fraction A1 for sample #3 in pulsed mode at T = 40 K and repetition time of 0.5 µs along with its simulation as a sum of VO²⁺ powder spectra with g_|| = 1.964, g_⊥ = 1.984, A_|| = 16.8 mT, A_⊥ = 6.0 mT, and FR single line with g = 2.0036. Arrows FR and VO²⁺ mark the values of B₀ at which the electronic relaxation times were measured for “free” radical and vanadyl-porphyrins, correspondingly. Owing to the short repetition time, the amplitude of the FR signal is suppressed. Simulations are performed with the EasySpin package for Matlab (Stoll and Schweiger, 2006).
Figure 4.13 FR (a) and VO²⁺ (b) longitudinal relaxation times T_1e at RT for all the investigated samples.
Figure 4.14 Dependencies of the primary ESE amplitude (semilog plot) on the delay τ between the two MW pulses in the pulse sequence of the sample #4 (fraction A1) for FR at RT. Symbols indicate the experimental data, solid lines are the results of the fits corresponding to Equation 4.6 with m = 0 for the fraction A1 diluted in toluene (upper curve) and with m = 3.6·10^–6 ns^–2 for the undiluted fraction A1 (lower line).
Figure 4.15 Examples of FS-ESE EPR spectra for two fractions from different samples. Splitting between the EPR features (local maxima) are shown.
Figure 4.16 (a) X-band and (b) W-band EPR spectra at T = 50 K in crude oil sample #1 in pulse mode shown together with separate simulations of the different hf components due to ⁵¹V nucleus. Magnetic fields B₁ and B₂ correspond to the g_Z axis parallel and perpendicular to the direction of magnetic field (m_I = 3/2). The signal with a g-factor of 2.004 related to FR is marked by an asterisk.
Figure 4.17 (a, b) ¹H Mims ENDOR spectra corresponding to different molecular orientations of vanadyl porphyrin detected in the vicinity of proton Larmor frequency at T = 50 K for samples #1 and #2. (c, d) ¹⁴N ENDOR spectra of vanadyl porphyrins (solid curve) in X-band for crude oil samples #1, simulation (dashed curve) and calculated spectrum with parameters obtained by DFT calculations (dotted curve) at magnetic field B₁, and at magnetic field B₂. Corresponding parameters are listed in Table 4.4.
Figure 4.18 (a) Optimized chemical structures of vanadyl porphyrin models (VO, VOEtio, VODPEP, VOBenzo). Circles indicate the positions of the representative protons of the porphyrin skeleton (H1 and H2) and those attributed to the possible classes of side groups (H3–H6). The illustrated orientation of the g-tensor corresponds to the VO molecule. (b) ENDOR spectra simulated (dashed curve) for the selected protons are presented in comparison with the experimental spectra obtained for sample (solid curve).

Chapter 6: NMR Spectroscopy in Bitumen Characterization

Figure 6.1 Characteristic ¹H NMR spectrum of bitumen.
Figure 6.2 Typical ¹³C NMR spectrum of bitumen.
Figure 6.3 ¹H NMR spectrum of a crude oil and spectral partition. Source: Molina et al., (2007). Reproduced with permission of American Chemical Society.
Figure 6.4 Correlation between the SARA fractions of vacuum residues from Colombian crude oils measured and predicted by ¹H NMR analysis. Source: Molina et al. (2010). Reproduced with permission of Elsevier.
Figure 6.5 Distribution of transverse (T₂) relaxation times for bitumen (A), bitumen modified with SBS (B) and bitumen modified with SBS and PPA (C) analysed at 30 °C after different ageing steps, namely: no ageing (A, B, C), RTFOT ageing (A′, B′, C′) and PAV ageing (A″, B″, C″). Source: Rossi et al., (2015). Reproduced with permission of Elsevier.
Figure 6.6 ³¹P NMR spectrum of commercial PPA (105% grade): the resonance peak at 0 ppm is attributed to phosphorus in H₃PO₄, a smaller peak at −13 ppm is assigned to phosphorus in end groups of PPA chains and a much smaller peak around −26 ppm is ascribed to phosphorus in middle groups of PPA chains. Source: Varanda et al. (2016).
Figure 6.7 ³¹P NMR spectra of PPA modified bitumen blends (Bit1 to Bit7). The resonance peak around 1 ppm corresponds to phosphorus in H₃PO₄. The peaks attributed to phosphorus in polyphosphate chains (see Figure 6.6) are clearly absent. Source: Varanda et al. (2016).
Figure 6.8 ¹³C cross polarization magic-angle spinning (CPMAS) NMR spectra of asphalts from different sources, at −45 °C: (A) Venezuela, (B) Middle East, (C) Italy, (D) Africa and (E) North Africa. Source: Michon et al. (1999b). Reproduced with permission of American Chemical Society.
Figure 6.9 NMR images of water drops falling into four asphalts (AAA, AAB, AAC and AAD) from the Strategic Highway Research Program (SHRP) over a period of one week Source: Miknis et al. (2005). Reproduced with permission of Elsevier.

Chapter 7: Applications of Low Field Magnetic Resonance in Viscous Crude Oil/Water Property Determination

Figure 7.1 Measured NMR T₂ decay curve.
Figure 7.2 NMR T₂ relaxation distribution processed from measured decay curve.
Figure 7.3 NMR bulk relaxation of fluids of variable viscosity.
Figure 7.4 NMR bulk relaxation: relationship between viscosity and T_2gm.
Figure 7.5 NMR surface relaxation of water in various pore sizes.
Figure 7.6 NMR surface relaxation: relationship between permeability and T_2gm.
Figure 7.7 Relationship between NMR signal and fluid mass or volume.
Figure 7.8 Conventional oil systems: change of RHI with variable oil content in liquid.
Figure 7.9 Separation of fluid NMR signals in mixtures of oil and water.
Figure 7.10 NMR vs. Dean–Stark predictions of water cut in laboratory oil-water samples. Source: Wright et al. (2004). Reproduced with permission of the Journal of Canadian Petroleum Technology.
Figure 7.11 NMR relaxation distributions for water cut predictions at ambient and elevated temperatures.
Figure 7.12 NMR relaxation distributions of low and high water cut samples at elevated temperatures.
Figure 7.13 Thermal production wellhead samples: water cuts by centrifuge and NMR. Source: Allsopp et al. (2001). Reproduced with permission of the Journal of Canadian Petroleum Technology.
Figure 7.14 Production water cuts from thermal operating wells in northern Alberta. Source: Allsopp et al. (2001). Reproduced with permission of the Journal of Canadian Petroleum Technology.
Figure 7.15 Relationship between NMR mean relaxation time and oil viscosity for multiple temperature samples. Source: Bryan et al. (2005a). Reproduced with permission of SPE Reservoir Evaluation and Engineering.
Figure 7.16 Relationship between NMR normalized AI and oil viscosity for multiple temperature samples. Source: Bryan et al. (2005a). Reproduced with permission of SPE Reservoir Evaluation and Engineering.
Figure 7.17 Single oil sample at multiple temperatures: change in oil relaxation time with viscosity.
Figure 7.18 Single oil sample at multiple temperatures: relationship between RHI and oil T_2gm.
Figure 7.19 General NMR correlation viscosity predictions.
Figure 7.20 Improvements in NMR viscosity from tuning to specific oils. Source: Bryan et al. (2005a). Reproduced with permission of SPE Reservoir Evaluation and Engineering.
Figure 7.21 Loss of viscosity: T_2gm relationship for high viscosity oils. Source: Chen and Bryan (2013). Reproduced with permission of SPE Proceedings.
Figure 7.22 Correlation between viscosity and oil RHI for high viscosity oils. Source: Chen and Bryan (2013). Reproduced with permission of SPE Proceedings.
Figure 7.23 Correlation between viscosity and oil RHI for high viscosity oils: individual relationships at each temperature vs. total RHI-viscosity trend. Source: Chen and Bryan (2013). Reproduced with permission of SPE Proceedings.
Figure 7.24 Prediction of viscosity of water-in-oil emulsions using the NMR oil viscosity correlation. Source: Bryan et al. (2002a). Reproduced with permission of the Society of Core Analysts.
Figure 7.25 Prediction of viscosity of water-in-oil emulsions using the NMR oil viscosity correlation corrected for NMR water content. Source: Bryan et al. (2002a). Reproduced with permission of the Society of Core Analysts.
Figure 7.26 Heavy oil in porous media: location of oil and connate water. Source: Bryan et al. (2005b). Reproduced with permission of the Society of Core Analysts.
Figure 7.27 Representative relaxation distributions of water in unconsolidated sand and clay. Source: Bryan et al. (2006). Reproduced with permission of SPE Reservoir Evaluation and Engineering.
Figure 7.28 Comparison of bulk heavy oil relaxation distribution to relaxation distribution of oil and water in oil sand.
Figure 7.29 Comparison between bulk and in situ oil mean relaxation times. Source: Bryan et al. (2006). Reproduced with permission of SPE Reservoir Evaluation and Engineering.
Figure 7.30 Prediction of in situ oil RHI from oil T_2gm. Source: Bryan et al. (2006). Reproduced with permission of SPE Reservoir Evaluation and Engineering.
Figure 7.31 Prediction of in situ oil viscosity on core samples using non-linear T_2gm-based approach. Source: Bryan et al. (2006). Reproduced with permission of SPE Reservoir Evaluation and Engineering.
Figure 7.32 Interpretation of in situ oil RHI from combined NMR/density log data. Source: Chen and Bryan (2013). Reproduced with permission of SPE Proceedings.
Figure 7.33 Example of NMR predictions of fluid content and in situ oil viscosity in oil sand core.
Figure 7.34 Oil sand log prediction of in-situ bitumen viscosity using RHI-based model. Source: Chen and Bryan (2013). Reproduced with permission of SPE Proceedings.
Figure 7.35 Synthetic oil sand with 4.5 wt% bitumen and clays between 50 and 90% of total solids mass.
Figure 7.36 Synthetic oil sand with solid containing 50% clay, but oil content varied between 4.5 and 22 wt%. Source: From Bryan et al. (2006). Reproduced with permission of SPE Reservoir Evaluation and Engineering.
Figure 7.37 Example oil sand samples containing variable measured oil and water contents. Source: Jones et al. (2014). Reproduced with permission of SPE Proceedings.
Figure 7.38 T₁–T₂ two-dimensional NMR array for separating overlapping bitumen and clay-bound water signals in oil sands. Source: Jones et al. (2014). Reproduced with permission of SPE Proceedings.
Figure 7.39 NMR relaxation distributions of bitumen, liquid solvent, and oil/solvent mixture. Source: Bryan et al. (2002b). Reproduced with permission of SPE Proceedings.
Figure 7.40 Correlation between diluted oil viscosity and mixture mean relaxation time for solutions of oil and paraffinic solvent (heptane). Source: From Wen, Bryan, and Kantzas (2005). Reproduced with permission of the Journal of Canadian Petroleum Technology.
Figure 7.41 Correlation between diluted oil viscosity and mixture RHI for solutions of oil and paraffinic solvent (heptane). Source: From Wen, Bryan, and Kantzas (2005). Reproduced with permission of the Journal of Canadian Petroleum Technology.
Figure 7.42 NMR pseudo-viscosity correlation vs. bitumen content for different paraffinic solvents. Source: From Salama and Kantzas (2005). Reproduced with permission of SPE Proceedings.
Figure 7.43 Correlation between diluted oil viscosity and mixture mean relaxation time for solutions of oil and paraffinic or aromatic solvent.
Figure 7.44 Correlation between diluted oil viscosity and mixture RHI for solutions of oil and paraffinic or aromatic solvent).
Figure 7.45 NMR RHI for paraffinic vs. aromatic solvents, showing potential asphaltene dropout in oil/solvent mixtures. Source: From Wen, Bryan, and Kantzas (2005). Reproduced with permission of the Journal of Canadian Petroleum Technology.
Figure 7.46 Correlation between oil viscosity and mean relaxation time for gas-free oil with temperature or live oil saturated with vapor phase solvent.
Figure 7.47 NMR predicted viscosity of live oil during equilibrium vs. non-equilibrium pressure depletion of a methane-live oil system. Source: Goodarzi et al. (2007). Reproduced with permission of SPE Journal.

Chapter 8: Application of Near-Infrared Spectroscopy to the Characterization of Petroleum

Figure 8.1 NIR spectra of some Brazilian crude oil samples in three regions: (a) second overtone of the C–H stretch and C–H combination bands appear; (b) the first overtones of C–H stretching vibrations; and (c) C–H stretch, first overtone and C–H, C=C combinations.
Figure 8.2 Relative standard deviation for reproducibility analysis. Source: Falla et al. (2006). Reproduced with permission of Elsevier.
Figure 8.3 Effect of smoothing (Savitzky–Golay method) to three different first-derivative spectra.
Figure 8.4 (a) NIR spectra of 50 crude oil samples. (b) First-derivative NIR spectra of 50 crude oil samples.
Figure 8.5 Schematic representation of the decomposition process in the PCA method.
Figure 8.6 Relationship between the measured values and the predicted ones for (a) API degrees and (b) viscosity (cP). (•) Calibration samples. (Δ) Validation samples.
Figure 8.7 Validation curves for NIR models developed using PLS for API degree (a) and viscosity (b) of crude oil samples.

Chapter 9: Raman and Infrared Spectroscopy of Crude Oil and its Constituents

Figure 9.1 Two-body system as a model for molecular vibration and rotation. Masses m₁ and m₂, bond strength (spring constant) k, interatomic distance R, and angular velocity ω.
Figure 9.2 Illustration of the different types of vibrational modes in polyatomic molecules.
Figure 9.3 Schematic energy level diagram illustrating the direct absorption process of a photon (energy hν_R) between the ro-vibrational energy states a and b, and the inelastic scattering of an incident photon (energy hν₀) yielding the emission of a Raman photon (energy h(ν₀-ν_R)).
Figure 9.4 Illustrations of the most common IR concepts: (a) transmission, (b) diffuse reflectance, and (c) attenuated total reflection.
Figure 9.5 Schematic Raman backscattering setup.
Figure 9.6 The matrix of the original data (X) can be expressed as the product of the matrix of the scores (S) and the matrix of the loadings (L) plus the matrix of the residuals (E).
Figure 9.7 Determination of the first and second principal components.
Figure 9.8 Normalized IR spectra of different crude petroleum oils. Source: Galtier et al. (2011). Reproduced with permission of American Chemical Society.
Figure 9.9 Score plot from the PCA of the IR spectra of 18 crude oils and condensates (two outliers removed). Source: Aske, Kallevik, and Sjöblom (2001). Reproduced with permission of American Chemical Society. (Note that the Figure was redrawn based on the original graphic to improve the graphical quality.)
Figure 9.10 Results from PLSR. Predicted value plotted against measured for (a) density based on GC, (b) velocity of sound based on GC, (c) static permittivity (e_st) based on IR data, (d) high frequency permittivity (e_inf) based on IR data, (e) density based on IR data, and (f) velocity of sound based on IR data. Source: Tomren, Barth, and Folgerø (2012). Reproduced with permission of American Chemical Society.
Figure 9.11 IR spectra of SARA fractions from a crude oil are shown together with a spectrum of the original crude oil. The broken line is a theoretical spectrum of the crude oil calculated from a linear combination (weighted with the experimental composition) of the individual SARA spectra. Source: Hannisdal, Hemmingsen, and Sjöblom (2005). Reproduced with permission of American Chemical Society.
Figure 9.12 Raman spectra of an asphaltene sample: (a) shows the overall spectra recorded at tree different locations on the sample and (b) shows the zoomed-in region of the D1 and G bands. Source: Abdallah and Yang (2012). Reproduced with permission of American Chemical Society.
Figure 9.13 In situ macro ATR-FTIR spectroscopic images of the blends of two crude oils measured at 60 °C. The partial volumes are 50% (1) and 75% (2). The images were obtained based on the distribution of the integrated absorbance of the spectral band at 1550–1650 cm⁻¹. The measured area is ca. 610 µm × 530 µm. Source: Gabrienko, Martyanov, and Kazarian (2015). Reproduced with permission of American Chemical Society.

List of Tables

Chapter 2: Optical Interrogation of Petroleum Asphaltenes: Myths and Reality

Table 2.1 Positions of fluorescence emission (FE) peaks in standard spectra of some 1- to 4-ring molecules and their aggregates
Table 2.2 The diversity in properties of studied crude oils. Source: Adapted from Evdokimov and Fesan (2016)

Chapter 3: ESR Characterization of Organic Free Radicals in Crude Oil and By-Products

Table 3.1 hf parameters and weight percentages in intensity of the lines used in the simulation of lines groups
Table 3.2 First- (A) and second-order (A′) hf coupling constants for the perinaphthenyl radical. D is the deviation between experimental and theoretical values
Table 3.3 First- (A) and second-order (A′) hf coupling constants for the hydroxyperinaphthenyl and dimethylperinaphthenyl radicals. D is the deviation between experimental and theoretical values

Chapter 4: High-Field, Pulsed, and Double Resonance Studies of Crude Oils and their Derivatives

Table 4.1 EPR microwave frequency bands with the corresponding wavelengths, energies, and typical magnetic fields for g = 2
Table 4.2 List of the studied samples
Table 4.3 Samples and their physical properties at RT
Table 4.4 Comparison between spin Hamiltonian parameters of vanadyl porphyrin complex in natural crude oil obtained from the simulation of the experimental EPR and ¹⁴N ENDOR spectra and calculated by DFT method ones for VO molecule*

Chapter 6: NMR Spectroscopy in Bitumen Characterization

Table 6.1 ¹H NMR chemical shifts (δ, ppm) and corresponding hydrogen types. Source: (Molina et al., 2010). Reproduced with permission of Elsevier
Table 6.2 ¹³C NMR chemical shifts (δ, ppm) and identification of the corresponding types of carbon atoms. Source: Michon et al. (1997a). Reproduced with permission of American Chemical Society
Table 6.3 Structural average molecular parameters. Source: Michon et al. (1997b). Reproduced with permission of Elsevier

Chapter 7: Applications of Low Field Magnetic Resonance in Viscous Crude Oil/Water Property Determination

Table 7.1 Calculation of conventional oil amplitude signal (RHI)
Table 7.2 Calculation of viscous heavy oil amplitude signal (RHI)
Table 7.3 Tuning of NMR viscosity coefficients for different oil samples with temperature. Source: From Bryan et al. (2005a). Reproduced with permission of SPE Reservoir Evaluation and Engineering
Table 7.4 NMR viscosity model parameters for bitumen (130,000 mPa s) and various paraffinic solvents. Source: From Wen, Bryan, and Kantzas (2005). Reproduced with permission of the Journal of Canadian Petroleum Technology

Chapter 8: Application of Near-Infrared Spectroscopy to the Characterization of Petroleum

Table 8.1 Summary of the main instruments in the market in online NIR

Chapter 9: Raman and Infrared Spectroscopy of Crude Oil and its Constituents

Table 9.1 Functional groups and their normal vibrational bands in cm^–1
Table 9.2 Advantages and disadvantages of IR and Raman spectroscopy

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List of Contributors

T. Biktagirov
Kazan Federal University
Kremlevskaya
Kazan
Russia

Jonathan L. Bryan
Department of Chemical and
Petroleum Engineering
Schulich School of Engineering
University of Calgary
Canada

PERM Inc.
Calgary
Canada

Stella Corsetti
College of Life Sciences
University of Dundee
United Kingdom

Eduardo Di Mauro
Universidade Estadual de Londrina
(UEL)/Laboratório
de Fluorescência e Ressonância
Paramagnética Eletrônica
(LAFLURPE), Brazil

Igor N. Evdokimov
Department of Physics
Gubkin Russian State University of
Oil and Gas
Moscow
Russia

Marat Gafurov
Kazan Federal University
Kremlevskaya
Kazan
Russia

Carmen Luisa Barbosa Guedes
Universidade Estadual de Londrina
(UEL)/Laboratório
de Fluorescência e Ressonância
Paramagnética Eletrônica
(LAFLURPE), Brazil

Siavash Iravani
Faculty of Pharmacy and
Pharmaceutical Sciences
Isfahan University of Medical
Sciences
Iran

Apostolos Kantzas
Department of Chemical and
Petroleum Engineering
Schulich School of Engineering
University of Calgary
Canada

PERM Inc.,
Calgary
Canada

Johannes Kiefer
Technische Thermodynamik
Universität Bremen
Germany

Galo Antonio Carrillo Le Roux
Departamento de Engenharia
Química
Escola Politécnica da Universidade de
São Paulo
Brasil

Juan López-Gejo
SICPA SA
Prilly
Switzerland

G. Mamin
Kazan Federal University
Kremlevskaya
Kazan
Russia

Flavio H. Marchesini
Department of Mechanical Engineering
Pontifical Catholic University of
Rio de Janeiro
Brazil

Claudio Augusto Oller do Nascimento
Departamento de Engenharia
Química
Escola Politécnica da Universidade de
São Paulo
Brasil

S. B. Orlinskii
Kazan Federal University
Kremlevskaya
Kazan
Russia

Patricia Araujo Pantoja
Universidad de Ingeniería y
Tecnología (UTEC)
Lima
Peru

Marilene Turini Piccinato
Universidade Tecnológica Federal
do Paraná – Campus Londrina
(UTFPR-LD)
Brasil

Inês Portugal
Department of Chemistry
CICECO
Aveiro Institute of Materials
University of Aveiro
Portugal

Jorge Ribeiro
Galp Energia
Refinaria de Matosinhos
Leça da Palmeira
Portugal

Artur M. S. Silva
Department of Chemistry
QOPNA
University of Aveiro
Portugal

Carlos M. Silva
Department of Chemistry
CICECO
Aveiro Institute of Materials
University of Aveiro
Portugal

Catarina Varanda
Department of Chemistry
CICECO
Aveiro Institute of Materials
University of Aveiro
Portugal

Department of Chemistry
QOPNA
University of Aveiro
Portugal

M. Volodin
Kazan Federal University
Kremlevskaya
Kazan
Russia

Sakhalin Energy Investment
Company Ltd.
Yuzhno-Sakhalinsk
Russia

Preface

The characterization of crude oil and related products is of increasing interest to the scientific community as well as the petroleum industry because the property and composition of samples from different oilfields are different. This present collection of writings intends to describe the potential applications of a variety of spectroscopic techniques in this field. This volume contains nine chapters which include ESR, NMR, IR, UV-Vis, and Raman spectroscopic techniques. In addition, a chapter on rheological characterization is included to bring a sense of completeness. Contributors to this volume are from a variety of disciplines and hence lend this volume a multidisciplinary character. Mathematical details have been kept to a minimum. All the authors are experts of eminence in their field and I learned many things from their chapters. I hope that readers will also enjoy reading it in a meaningful way.

I sincerely thank Jenny Cossham, commissioning editor, Natural Sciences, John Wiley & Sons, Ltd for giving me an opportunity to present this book to readers. I wish to thank Emma Strickland, assistant editor, Natural Sciences, John Wiley & Sons, Ltd for extending all the support during the development of this project. It is the prompt response of the project editor, Elsie Merlin, which allowed me to present this work in such a short time. I thank the authors for taking time out of their busy academic schedules to contribute to this volume. I offer my special thanks to anonymous reviewers for their comments, which helped me to cover a wide range of spectroscopic tools.

I am grateful to Prof. Ram Kripal and Prof. Raja Ram Yadav, Department of Physics, University of Allahabad for their suggestions and comments. My sincere thanks are also due to Dr. M. Massey, Principal, Ewing Christian College, Allahabad and my colleagues for their constant encouraging remarks during the development of this book.

Gratitude to my parents cannot be expressed in words. I could complete this task with their blessings only. My brother Dr. Arun K. Shukla, Department of Biological Sciences and Bioengineering, Indian Institute of Technology, Kanpur has always supported my endeavors. My special thanks are also due to my wife Dr. Neelam Shukla, my daughter Nidhi and son Animesh for their patience during the progress of this work.

Ashutosh K. Shukla
Allahabad, India
January 2017