Analytical Characterization Methods for Crude Oil and Related Products E-Book
139,99 €
Mehr erfahren.
- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
Basic theory, applications, and recent trends in analytical techniques used in crude oil and related products analysis This book covers the application of different spectroscopic methods to characterize crude oil and related products. Its topics are presented in a pedagogical manner so that those new to the subject can better understand the content. The book begins by familiarizing the reader with the rheological characterization of crude oil and related products. Subsequent chapters are directed towards the current trends of different spectroscopic methods for the characterization of crude oil. Analytical Characterization Methods for Crude Oil and Related Products features chapters on: optical interrogation of petroleum asphaltenes (myths and reality); ESR characterization of organic free radicals in petroleum products; high-field, pulsed, and double resonance studies of crude oils and their derivatives; NMR spectroscopy in bitumen characterization; applications of Raman spectroscopy in crude oil and bitumen characterization; and more. * Uses a bottom-up approach--starting from the basic theory of the technique followed by its applications and recent trends in crude oil analysis * Includes informative content so as to take a technician to the level of using a particular analytical method * Covers relevany information so as to enable a manager in the industry to make purchasing decisions Analytical Characterization Methods for Crude Oil and Related Products is aimed at researchers in academia as well as technicians and developers of new analytical methods in the oil industry and related areas. It will also be of interest to professionals, scientists, and graduate students in analytical sciences dealing with oil and environmental analysis.
Sie lesen das E-Book in den Legimi-Apps auf:
Seitenzahl: 483
Veröffentlichungsjahr: 2017
Ähnliche
Table of Contents
Cover
Title Page
Copyright
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
1
H NMR and
13
C 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
1
H and
13
C 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
Pages
xiii
xiv
xv
xvii
xviii
1
2
3
4
5
6
7
8
9
10
11
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
271
272
273
274
275
276
277
Guide
Cover
Table of Contents
Preface
Begin Reading
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 CCl
4
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 (
H
01
and
H
02
) 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; Δ
H
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 of the free-radical signal versus microwave frequency of ESR spectra recorded in the X-, Q- and W-bands at room temperature. ▪, (Arabian petroleum); •, (Colombian petroleum); ▴, (Arabian petroleum); ▾, (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 gβ
H
.
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 VO
2+
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 VO
2+
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 VO
2+
mark the values of B
0
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 VO
2+
(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
51
V nucleus. Magnetic fields B
1
and B
2
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)
1
H 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)
14
N 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
B
1
, and at magnetic field
B
2
. 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
1
H NMR spectrum of bitumen.
Figure 6.2 Typical
13
C NMR spectrum of bitumen.
Figure 6.3
1
H 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
1
H NMR analysis.
Source:
Molina
et al.
(2010). Reproduced with permission of Elsevier.
Figure 6.5 Distribution of transverse (T
2
) 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
31
P NMR spectrum of commercial PPA (105% grade): the resonance peak at 0 ppm is attributed to phosphorus in H
3
PO
4
, 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
31
P NMR spectra of PPA modified bitumen blends (Bit1 to Bit7). The resonance peak around 1 ppm corresponds to phosphorus in H
3
PO
4
. The peaks attributed to phosphorus in polyphosphate chains (see Figure 6.6) are clearly absent.
Source:
Varanda
et al.
(2016).
Figure 6.8
13
C 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
T
2
decay curve.
Figure 7.2 NMR
T
2
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
2
gm
.
Figure 7.5 NMR surface relaxation of water in various pore sizes.
Figure 7.6 NMR surface relaxation: relationship between permeability and
T
2
gm
.
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
2
gm
.
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
2
gm
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
2
gm
.
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
2
gm
-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
T
1
–
T
2
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
m
1
and
m
2
, 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 hν
0
) yielding the emission of a Raman photon (energy h(ν
0
-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
14
N ENDOR spectra and calculated by DFT method ones for VO molecule*
Chapter 6: NMR Spectroscopy in Bitumen Characterization
Table 6.1
1
H NMR chemical shifts (δ, ppm) and corresponding hydrogen types.
Source:
(Molina
et al.
, 2010). Reproduced with permission of Elsevier
Table 6.2
13
C 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
Analytical Characterization Methods for Crude Oil and Related Products
Edited by
Ashutosh K. Shukla
Physics Department Ewing Christian College, India
This edition first published 2018
© 2018 John Wiley & Sons Ltd
All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording or otherwise, except as permitted by law. Advice on how to obtain permission to reuse material from this title is available at http://www.wiley.com/go/permissions.
The right of Ashutosh K. Shukla to be identified as the author of the editorial material in this work has been asserted in accordance with law.
Registered Office(s)
John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USA
John Wiley & Sons Ltd, The Atrium, Southern Gate, Chichester, West Sussex, PO19 8SQ, UK
Editorial Office
The Atrium, Southern Gate, Chichester, West Sussex, PO19 8SQ, UK
For details of our global editorial offices, customer services, and more information about Wiley products visit us at www.wiley.com.
Wiley also publishes its books in a variety of electronic formats and by print-on-demand. Some content that appears in standard print versions of this book may not be available in other formats.
Limit of Liability/Disclaimer of Warranty
In view of ongoing research, equipment modifications, changes in governmental regulations, and the constant flow of information relating to the use of experimental reagents, equipment, and devices, the reader is urged to review and evaluate the information provided in the package insert or instructions for each chemical, piece of equipment, reagent, or device for, among other things, any changes in the instructions or indication of usage and for added warnings and precautions. While the publisher and authors have used their best efforts in preparing this work, they make no representations or warranties with respect to the accuracy or completeness of the contents of this work and specifically disclaim all warranties, including without limitation any implied warranties of merchantability or fitness for a particular purpose. No warranty may be created or extended by sales representatives, written sales materials or promotional statements for this work. The fact that an organization, website, or product is referred to in this work as a citation and/or potential source of further information does not mean that the publisher and authors endorse the information or services the organization, website, or product may provide or recommendations it may make. This work is sold with the understanding that the publisher is not engaged in rendering professional services. The advice and strategies contained herein may not be suitable for your situation. You should consult with a specialist where appropriate. Further, readers should be aware that websites listed in this work may have changed or disappeared between when this work was written and when it is read. Neither the publisher nor authors shall be liable for any loss of profit or any other commercial damages, including but not limited to special, incidental, consequential, or other damages.
Library of Congress Cataloging-in-Publication Data applied for
ISBN : 9781119286318
Cover Design: Wiley
Cover Image: © mmmx / Shutterstock
To my teachers
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
and
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
and
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
and
Department of Chemistry
QOPNA
University of Aveiro
Portugal
M. Volodin
Kazan Federal University
Kremlevskaya
Kazan
Russia
and
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
Chapter 1Rheological Characterization of Crude Oil and Related Products
Flávio H. Marchesini
Pontifical Catholic University of Rio de Janeiro
1.1 Introduction
Crude oil and related products undergo different transport processes from extraction to end use. For example, crude oils may be transported through pipelines before the refining process (Petrellis and Flumerfelt, 1973; Smith and Ramsden, 1978; Rønningsen et al., 1991; Wardhaugh and Boger, 1991a), fuel oils are injected into combustion engines to produce mechanical work (Graboski and McCormick, 1998, Ramadhas et al., 2004; Agarwal, 2007; Joshi and Pegg, 2007), and lubricant oils are used to reduce friction between mechanical parts in contact (Dyson, 1965; Webber, 1999, 2001).
The design of each of these processes requires the rheological properties of the oils, as the pumping power and the dimensions of the lines, connections, and mechanical parts are defined assuming that the oil has a viscosity within a specific range. If this range is not properly set during the design stage and the process starts running with an oil having a viscosity out of the appropriate range, different issues can arise. For example, severe flow assurance issues can be faced during the restart flow of crude oils in pipelines (Petrellis and Flumerfelt, 1973; Smith and Ramsden, 1978; Wardhaugh and Boger, 1991a; Rønningsen et al., 1991), and filters and lines can be plugged, preventing an engine from starting (Graboski and McCormick, 1998; Ramadhas et al., 2004; Agarwal, 2007; Joshi and Pegg, 2007). Therefore, to guarantee that the process works properly, the rheological properties of these oils must be known as accurately as possible, in representative process conditions.
In general, at high enough temperatures, crude oil and related products behave as simple Newtonian liquids, whose viscosities depend solely on temperature. However, at low enough temperatures, the rheological behavior of these oils usually becomes quite complex due to precipitation of higher-molecular-weight compounds, which gives rise to a gelation phenomenon when a certain amount of crystals is present. At this low temperature range, the oil viscosity increases significantly and depends not only on temperature but also on time, shear, and thermal and shear histories (Petrellis and Flumerfelt, 1973; Smith and Ramsden, 1978; Wardhaugh and Boger, 1987, 1991b; Rønningsen et al., 1991; Rønningsen, 1992; Chang et al., 1998, 2000; Webber, 1999, 2001; Venkatesan et al., 2005).
This complex rheological behavior at low temperatures may introduce difficulties in performing the rheological characterization of these oils. A number of precautions must be taken to get accurate properties during rheological measurements with these oils (Wardhaugh and Boger, 1987, 1991a; Marchesini et al., 2012; Alicke et al., 2015). Thus, we discuss in this chapter how to prepare samples for rheological measurements (in Section 1.2), the most common rheological tests performed with these oils and how to interpret the data (in Section 1.2), and the potential sources of errors in rheological measurements and how to avoid them (in Section 1.3).
1.2 Sample Preparation for Rheological Characterization
As described in this section, the sample preparation procedure for rheological characterization can be divided into four main steps: (i) ensuring the chemical stability (Section 1.2.1), (ii) choosing the rheometer geometry (Section 1.2.2), (iii) erasing the thermal memory (Section 1.2.3), and (iv) performing the cooling process (Section 1.2.4).
1.2.1 Ensuring the Chemical Stability
The first step of the sample preparation procedure is to make sure that the crude oil or related product is not going to evaporate or lose significant amounts of lightweight compounds under the temperature and pressure conditions in which the rheological test is going to be performed. This step is intended to guarantee the chemical stability of the sample during the test, thus avoiding evaporation effects on the time-dependent rheological properties being measured (Wardhaugh and Boger, 1987).
If the oil is not stable enough at the test conditions, a pretreatment can be applied to the oil to evaporate light ends before loading a sample into the rheometer or viscometer used. The pretreatment usually consists of heating the oil at a temperature within the temperature range of the process of interest (Smith and Ramsden, 1978; Wardhaugh and Boger, 1987; Marchesini et al., 2012).
It is important noting that a difference between the rheological properties of the pretreated oil and the untreated oil can be observed, and higher viscosity values are usually obtained for the samples after applying a pretreatment to evaporate light ends (Wardhaugh and Boger, 1987). However, with regard to many applications, the rheological tests with the pretreated oil provide conservative data for the transport process design (Wardhaugh and Boger, 1991a). If this is not the case or if more accurate data is needed, the rheological properties of the pretreated oil can be corrected by estimating the increase in viscosity due to evaporation of light ends (Wardhaugh and Boger, 1987; Rønningsen et al., 1991).
1.2.2 Choosing the Rheometer Geometry
The second step is to choose the appropriate rheometer geometry in which the sample is going to be loaded for rheological characterization. The classical geometries used to perform the rheological characterization of materials in rotational rheometers are: (i) cone and plate, (ii) parallel plates, and (iii) concentric cylinders (also known as the Couette geometry). To decide which is the best geometry for the rheological characterization of a given oil used for a particular application, some points must be addressed.
If the rheological tests are going to be performed in a temperature range in which no crystals appear in the sample, the oil may present a Newtonian behavior. In this case, any classical geometry is expected to give the same results, so any of the three geometries can be chosen. However, if crystals are expected to appear during the test and if the oil presents the complex rheological behavior expected at low temperatures, the rheometer geometry must be carefully chosen to obtain reliable data of the bulk rheological behavior (Marchesini et al., 2012).
Even though the cone and plate geometry is widely used for the rheological characterization of crude oil and related products, this geometry may not be the best choice depending on the oil at hand and test conditions (Marchesini et al., 2012). In favor of the cone and plate there is the argument that it is the only geometry in which all parts of the sample are submitted to exactly the same shear rate (Wardhaugh and Boger, 1987). In addition, as the cone and plate geometry requires a small amount of sample, it may be easy to control the temperature inside the sample. However, the cone and plate geometry is not suitable for the rheological characterization of samples having large enough crystals suspended, as it may violate the continuum hypothesis used in the rheometer theory. In addition, there is evidence in the literature that very small gaps—as the ones of commercial cone and plate geometries—cause the precipitation of crystals at higher temperatures (Davenport and Somper, 1971; Rønningsen et al., 1991). Thus, to obtain the bulk rheological properties of these oils at low temperatures, large enough gaps are required (Marchesini et al., 2012).
In this case, the parallel plates or the concentric cylinders can be chosen. The parallel-plate geometry has the advantage of being the best geometry to vary the gap, thus making easy the task of finding the large enough gap above which the rheological data stop changing with the gap. Moreover, the parallel-plate geometry is also a convenient choice for preventing apparent wall slip during rheological measurements, as it is easy to vary the gap and roughen its surfaces (e.g. by using sandpaper). However, the parallel-plate geometry has the disadvantage of having a shear rate dependence on the radius inside the sample, which might complicate the control of the shear history in some cases. It is important to note that as the highest shear rates occur at the highest radii—the regions that contribute most to the torque being measured—the non-homogeneous flow field in the parallel-plate geometry should not be a serious issue, at least in some cases. Corrections are available in the literature to end up with more accurate data when using the parallel-plate geometry (de Souza Mendes et al., 2014).
The concentric cylinders geometry presents the advantage of having a much less significant shear rate gradient inside the sample when compared to the parallel-plate geometry, allowing for a better control of the shear history in some cases. However, the concentric cylinders require larger sample volumes, which can lead to errors in the measurements due to contraction of the sample during the test (Wardhaugh and Boger, 1987, 1991a). Besides that, to obtain gap-independent results with the concentric cylinders geometry, cylinders with different diameters ratio are needed to vary the geometry gap, which may not be available. So, the best choice of rheometer geometry to get accurate data may depend on each case (Marchesini et al., 2012).
1.2.3 Erasing the Thermal Memory
The third step is to load the oil sample into the rheometer geometry and apply an isothermal holding time at an initial temperature within the temperature range of the process of interest (Smith and Ramsden, 1978; Wardhaugh and Boger, 1987; Marchesini et al., 2012). This initial temperature is usually a high enough temperature to dissolve the crystals suspended in the oil sample, thus “erasing the thermal memory” of the oil (Wardhaugh and Boger, 1987, 1991b). This step is intended to ensure that each sample loaded into the rheometer geometry is going to have the same microstructure configuration in the beginning, so that repeatable results can be obtained. It is important to note that the initial temperature should not be higher than the highest temperature observed in the process of interest to avoid introducing effects in the measurements that are not observed in the process (Marchesini et al., 2012).
1.2.4 Performing the Cooling Process
The fourth and last step of the sample preparation procedure is the cooling process, in which the sample is cooled from the initial temperature to the measurement temperature under controlled shear and cooling rate (Wardhaugh and Boger, 1987, 1991b; Marchesini et al., 2012). This fourth step is intended to reproduce in the sample the thermal
