How does the shale gas situation in the world change energy markets, are oil sands a part of the future and can subsea help provide the future with energy?

1. Hydrocarbon Thermophysical Properties:
unexpected frontiers
John M. Shaw
Professor and NSERC Industrial Research Chair in
Petroleum Thermodynamics
Department of Chemical and Materials Engineering
University of Alberta, Edmonton, Canada
jmshaw@ualberta.ca
www.jmshaw.ualberta.ca

2. Acknowledgements
Sponsors
Natural Sciences and Engineering Research Council of Canada
Alberta Innovates - Energy and Environment Solutions
BP Canada
ConocoPhillips Canada Resources Corp.
Nexen Inc.
Shell Canada Ltd.
Total E&P Canada Ltd.
Virtual Materials Group 2
Colleagues
Marco Satyro, Harvey Yarranton, Loic Barre, Kirk Michaelian, Jean-Luc Daridon,
Jerome Pauly, …

3. So what is the big deal?
We’ve been doing this for more than a century at an industrial scale globally

4. So what is the big deal?
We’ve been doing this for more than a century at an industrial scale globally
but …

5. Canadian National Advisory Panel Report 2006
Other than CO2 capture and storage, and gasification,
there is no mention of research in the “carbon sector”
5
Chemical, thermodynamic and transport
property knowledge ranked last among NINE
surveyed industrial priorities. New processes
ranked first.

6. Classic Property Knowledge Example
brute force
better
property
knowledge
M. Satyro reminded me of this example from J. M. Douglas’ Conceptual Design of Chemical Processes, McGraw-Hill, N.Y., 1988.
6

7. 7

8. Hydrocarbon vs Renewable Energy Resources
world consumption of oil alone exceeds 90 million bpd (5000 MT/year).
1 million bpd yields ~ 60 gigawatts
maximum production per wind turbine 5 MW!
5 MW units
rotor diameter 126 m
mast height 90-120 m
source: www.repower.de
Calculation suggestion: Michael Raymont, CEO EIN
football stadium, Vanderbilt University.
8

9. ALBERTA OILSANDS: 300 billion barrels are recoverable.
An additional 1.4 trillion barrels are proven. AEUB Data
300
1400
1980
Proven “oil” reserves worldwide (2012)
9
140 795132*
206*
335*
* Including a fraction of heavy oil/bitumen reserves, BP statistical Review
2012
Middle
East
Eurasia
Africa
South
AmericaNorth
America
Asia
Pacific 41

10. •The oilsands resource and related industrial processes are poorly understood.
•Each insight regarding the fundamental behaviours and properties spurs innovation
•Greenhouse gas emission intensity has decreased 40 % over the past 20 years!
•Property discovery presents experimental and theoretical challenges and opportunities (innovation).
•Integration of quantitative materials property knowledge and theory from the molecular scale to the
nanometer scale to the macro scale is required so that thermophysical properties, transport properties,
and phase behaviors identified across these length scales and diverse processing environments are
better understood and become exploitable.
* Dusseault, B. and R. Morgenstern, Canadian Geotechnology Journal, 15, 1978.
** Bazyleva, A., et al., J. Chemical & Engineering Data 2011, 56. (7),3242-3253
*** Bagheri, R., et al., Energy & Fuels, 2010, 24 (8), pp 4327–4332
10
known and mapped for ~ 100 years* Phase diagram 2011** liquid crystals identified, 2010***
Oil Sands

11. nanofiltration
Predictive Cp
correlations
Calorimetry Rheology
samples with
different wA
Cp baseline
definition
• Cp data
• detection of phase transitions
• rheological data
• nature of phase transitions
Phase diagram preparation approach with broad potential for application to
reservoir fluids, heavy oils and bitumen other complex organic materials
PHASE BEHAVIOUR
• Temperatures and enthalpies of phase transitions
• States and numbers of phases
•Process design
•Process development
•Process optimization
Indispensable for
(interpretation of results,
experimental conditions, …)
Theory
Fulem, M. et al., Fluid Phase Equilibria, 2008 (272) 32-41
Bazyleva, Al. et al., J. Chemical & Engineering Data, 2011 56 (7) 3242-3253.

12. Equilibrium Modeling
Speciation is THE challenge for mixtures
containing heavy hydrocarbons.
12
Enthalpy modeling is “solved.” A rare
success but implementation of the methods
poses challenges.

13. Heat capacity modeling – naive approximation
13
∑
∑
∑
∑
∑
∑
=
=
=
=
=
=
==== n
i
i
n
i i
i
n
i
ii
n
i
i
n
i
ii
n
i
i
w
M
w
Mx
x
M
M
N
1
1
1
1
1
1
υ
υ
α
V. Lastovka, et al., Fluid Phase Equilibria, 268, 51-60, 2008.
V. Lastovka and J. M. Shaw, Fluid Phase Equilibria (submitted, 2013)
Rigid Rotor-Harmonic Oscillator Model
On a mass basis heat capacity is expected to scale as:

14. The Power of Similarity
a) differing molecular structure,
b) differing molar masses and molecular structure,
c) differing molar masses, elemental composition
and molecular structure.
Pairs of compounds with common (α)
Share constant pressure heat capacities
Correlations available:
SOLID: V. Lastovka, et al., Fluid Phase Equilibria, 268, 134-141, 2008.
LIQUID: N. Dadgostar and J. M. Shaw, Fluid Phase Equilibria, 313, 211–226, 2012.
LIQUID: N. Dadgostar and J. M. Shaw, Fluid Phase Equilibria, 344, 139– 151,2013.
IDEAL GAS: V. Lastovka, and J. M. Shaw, Fluid Phase Equilibria (submitted 2013).

15. 100.δ=6%
Virtual Materials Group has implemented methods for liquids and ideal gases!
Others are applying the concept and the correlations to bio-fuels and pharmaceuticals.
15
1) M. Fulem et al., Fluid Phase Equilibria 272 (2008) 32-41.
2) A. Bazyleva, et al.,, J. Chem. Eng. Data 56 (2011) 3242–3253.
Pure Predictions for Heavy Hydrocarbons
100.δ= 2.8%
Poster I: Dr. Nafiseh Dadgostar

16. Speciation and Modeling for CEoS
16
Speciation
Divide fluid into components and pseudo-components
Assign mole fractions, x, and properties to each
Thermodynamic Model (Cubic Equation of State)
Calculate equilibrium ratios, Ki = xi,vapour/xi,liquid
FLASH CALCULATION
xi, Ki
amount and composition
of each phase
x1,feed
x2,feed
x3,feed
x4,feed
P, T
Correlations
xi,
SGi, MWi, NBPi,
Tci, Pci, ωi
interaction
parameters
H.Yarranton & M. Satyro helped here!

17. Speciation of Heavy OilCarbonNumber
Atmospheric Equivalent Boiling Point
Boduszynsky, E&F, 1987
ISSUE:
How best to represent
property distributions to
predict phase behavior
and phase properties?
17
“islands”
colloidal stacks
“islands” and
“archipelagos”
chains, discs, and fluffy balls
“islands”
colloidal stacks
“islands” and
“archipelagos”
chains, discs, and fluffy balls
Asphaltenes?
H.Yarranton & M. Satyro helped here!

18. Pseudo-Components for CEoS- Boiling CutsCarbonNumber
Atmospheric Equivalent Boiling Point
Boduszynsky, E&F, 1987
Refinery Approach:
Start with boiling cuts.
Upstream Approach:
Start with GC fractions.
Each cut is assigned
average properties
based on NBP or MW.
18
∆NBP
(distillation based)
H.Yarranton & M. Satyro helped here!

19. Pseudo-Components for CEoS-
Representative MoleculesCarbonNumber
Atmospheric Equivalent Boiling Point
Boduszynsky, E&F, 1987
Characterize property
distributions with a
representative set of
molecules
19
Source: astrochemistry.ca.astro.it
H.Yarranton & M. Satyro helped here!

20. 20
Quantitative molecular level speciation is infeasible.
10’s of thousands of molecular species can be identified even in subfractions
Images courtesy of Amy McKenna, NHMFL at FSU

21. Heavy Oil Speciation for CEoS - Refinery Approach
21
BoilingTemperature
Cumulative Mass Fraction Distilled
Large Extrapolation: Uncertainty in properties for 70 wt% of bitumen.
Maltenes
(Gaussian extrapolation)
Asphaltenes
(Gamma distribution)
H.Yarranton & M. Satyro helped here!

22. Pseudo components are determined from chemical analysis + construction algorithms
and respect known aspects of molecular properties, elements, functional groups, etc.
Tc, Pc, acentric factor are then estimated using classic correlations.
Molecular construction algorithms are under constrained. For any given set of input
data, molecular species outcomes* are sensitive to the selection of submolecular
building blocks known to be present.**
Representative Molecule Approach
* Boek, E. S., Energy Fuels 2009, 23 (3), 1209–1219.
**Jaffe, S. B. et al., Ind. Eng. Chem. Res. 2005, 44 (26), 9840–9852.

23. 23
Representative molecule construction algorithms are ambiguous!
*Obiosa-Maife and Shaw Energy and Fuels, 2011, 25(2), 460-471
Michaelian et al., Vibrational Spectroscopy 2012, 58, 50-56.
Michaelian et al., Vibrational Spectroscopy, 2009, 49, 28–31.
Excellent residuals Misidentification of molecules
Comparative DFT computational study*

24. Phase Behaviour
Computation Face-off
n-decane + (10, 20, 30, 40, 70, 90 wt %) AVR*
- Phase boundaries and critical phenomena. 0
1 0
2 0
3 0
4 0
5 0
0 1 0 0 2 0 0 3 0 0 4 0 0 5 0 0
T e m p e r a t u r e , ° C
Pressure,bar
L 1 L 2 V
L 1 L 2
L 2 V
K p o in t
P h a s e b o u n d a r y
Figure 4.2 P-T phase diagram of 10% ABVB + decane mixture
0
1 0
2 0
3 0
4 0
5 0
0 1 0 0 2 0 0 3 0 0 4 0 0 5 0 0
T e m p e r a tu r e , ° C
Pressure,bar
L 1 L 2 V
L 2 V
p h a s e b o u n d a r y
Figure 4.3 P-T phase diagram of 20% ABVB + decane mixture
L1L2V
L2V
L1L2
V
L1L2V
L2V
L1L2
K
K
NOTES:
1.Above ~300 C, AVR begins to
pyrolyze.
2.Below ~ 50 C, AVR begins to
solidify.
* X. Zhang, PhD Thesis, 2006.

25. Supplied by
Syncrude
Molecule generation algorithm*
13
C NMR, CHNOS, …
Refinery Characterization
Group Contribution based Tc, Pc,
acentric factor, fit boiling curve to
get mole % values. Tuned
interaction parameters and a GC PR
EoS**,
***
SG, MW
Qualitative agreement with LV-L
and LLV-LV P-T and P-X phase
boundary data.
Blind use of the refinery based
approach DOES NOT yield
correct phase behaviors!****
APR CEoS
phase compositions in LL and LLV
regions are poorly represented.
A priori phase behaviour
prediction of vacuum residue +
light hydrocarbons is infeasible.
* Sheremata, J. PhD Thesis, University of Alberta, 2008
** Saber, N.; Shaw, J. M., Fluid Phase Equilibria 2011, 302, (1-2), 254-259.
***Saber, N., et al., Fluid Phase Equilibria 2012, Vol 313, 25-31.
****Saber, N. et al., Hydrocarbon World 2012, 6(2) 51-57.

26. Diverse models for molecular and supramolecular
structures for asphaltenes, even for the same or
closely related materials, have been proposed.
S
S
S
S
HN
O
O
O
NH
S
O
S
S
S
O
N
N
N
N
V
O
Supra molecular models for asphaltenes
pericondensed
archipelago
J. Murgich, et al., Energy Fuels, 1999, 13, 278 -286.
S. Zhao, et al., Fuel, 2001, 80, 1155-1163.
26

27. Proposed Supramolecular Structure - Pericondensed Molecules
A. Crystallite B. Chain Bundle C. Particle
D. Micelle E. Weak link F. Gap & hole
G. Intracluster H. Intercluster I. Resin
J. Single layer K. Porphyrin L. Metal (M)
J. P. Dickie and Y.T. Yen, Anal. Chem., 1967, 39, 1847-1852.
27M. Agrawala, H. W. Yarranton, Ind. Eng. Chem. Res., 2001, 40 , 4664-4672.
asphaltene monomers
active sites
Polymeric network based
on association

28. de Boer plot
Background
http://www.oilfieldwiki.com/wiki/Asphaltenes
*Nikooyeh, K., Shaw, J.M., Energy & Fuels, (2012) 26(1), 576-585, 2012.
Nikooyeh, K., et al., Energy & Fuels 2012, 26(3), 1756-1766.
D. Merino-Garcia, et al., Energy Fuels, 2010, 24 (4), pp 2175–2177
28
(Saturates+Asphaltenes)
(Resins+ Aromatics)
Colloidal stability index (C.S.I) =
Asphaltene Deposition/Plugging Risk Models
The behaviors are too complex to be treated
using simple notions of solution thermodynamics
such as regular solution theory*

29. 29
Structured Approach for Development of Physical Models for
Asphaltene Aggregation and Deposition
Dr. Yeganeh Khaniani, PDF, work in progress; Amin Pourmohammadbagher (PhD thesis, University of Alberta, in progress)

30. 30
Physical Models for Asphaltene Aggregation and Deposition
Dr. Yeganeh Khaniani, PDF, work in progress; Amin Pourmohammadbagher (PhD thesis, University of Alberta, in progress)

31. 31
Physical Models for Asphaltene Aggregation and Deposition
Dr. Yeganeh Khaniani, PDF, work in progress; Amin Pourmohammadbagher (PhD thesis, University of Alberta, in progress)

32. Depletion flocculation driven liquid-liquid
phase behavior – toluene + polystyrene + asphaltenes
32
Khammar,M.; Shaw, J.M., Energy & Fuels 2012, 26 (2), 1075-1088.
Khammar, M.; Shaw J.M., Review of Scientific Instruments 2011, 82, (10).

33. Depletion flocculation driven liquid-liquid
phase behavior – toluene + polystyrene + asphaltenes
Liquid-liquid (lower) and liquid-vapour (upper) interface elevation identification for a mixture of
asphaltenes (14 vol. %) + toluene (83 vol. %) + polystyrene (3 vol. %, molar mass 393,400 g/mole)
33
local speed of sound acoustic wave attenuation
attenuation relative
to toluene 7.9 MHz
Khammar,M.; Shaw, J.M., Energy & Fuels 2012, 26 (2), 1075-1088.
Khammar, M.; Shaw J.M., Review of Scientific Instruments 2011, 82, (10).

34. Phase Diagram Prediction - asphaltene + toluene + polystyrene mixtures
34
The Fleer and Tuinier*,
**
depletion flocculation model was
modified to account for the
variability of asphaltene
aggregate size with global
composition***.
• Fleer, G. J. & Tuinier, R. Advances in Colloid and Interface Science, 2008, (143) 1-47.
** Khammar, M., Shaw, J.M., Fluid Phase Equilibria, 2012, 332(10), 105-119.
*** Sajjad Pouralhossein, PhD thesis (University of Alberta, in progress).

35. The phase behavior of bitumen + diluents
35
Phase volumes, phase densities, phase boundaries, phase diagrams, mutual diffusion coefficients, ….
Poster II: Farshad Amani

36. 36
Nanostructure in bitumen - SAXS measurements
Measurements performed at ANL (APS)
Long et al., Energy Fuels, 2013, 27 (4) 1779–1790.
Amundarain, et al., Energy & Fuels 2011, 25(11) 5100-5122.

37. Nanostructure change – diluted bitumen
37
Mean size - radius of gyration (open symbols)
- solid sphere assumption (closed symbols)
Scattering coefficient - structure

38. Nanostructure change with phase
boundaries – diluted bitumen fraction
38
L1L1L2
radius of gyration surface: volume ratio scattering coefficient
L1 L1 L1 L1 L1
L2 L2L2
pentane
Athabasca
vacuum
residue
(AVR)
L1L2L1L2 L1 L1
X-ray

39. 39
Impacts of Materials Complexity on Rheology
– example Maya Crude Oil
Thixotropy
Shear Thinning

40. Viscosity – Athabasca bitumen
Abbreviations and symbols:
PPV – parallel plate viscometer,
CapV – capillary viscometer,
RBV – rolling ball viscometer,
CCV – concentric cylinder viscometer,
MS – mechanical spectrometer,
n/s – not stated,
γ' – shear rate,
ω – angular frequency
• 1. Sample identity
a) geographical location
b) elevation
c) sample pre-treatment
history
• 2. Experimental conditions
a) temperature
b) shear conditions
c) sample history during
measurements
• 3. Applicability,
restrictions, and errors of
certain experimental
methods and techniques
40

41. Mutual diffusion coefficient measurement
41
David Sinton’s group at UofT.
Visible light transmission through micro channels.
Measurement time reduced to minutes from days.
- CO2 sequestration, reactions, ….
Fadaei, Hossein, et al., Energy Fuels, 2013, 27(4), 2042-2048.
Ardalan Sadighian, et al., Energy Fuels, 2011, 25(2), pp. 782-790.
Zhang, X.H., et al., Journal of Chemical & Engineering Data, 2007, 52(3), 691-694.
Zhang, X, Shaw, J.M., Petroleum Science and Technology, 25(6), 2007, 773–790.
See also work by Jay W. Grate at the PNNL (USA) microscale visualization and measurement

42. The next challenge is to make
measurements in natural porous media!
42Poster III: Dr. Marc Cassiede.

43. Conclusions
• Hydrocarbon resource definitions and availability have changed
radically over the last century.
• “New” resources are complex and present
– materials challenges:
• Thermophysical property measurement & prediction.
• Data and observation interpretation.
• Translating property knowledge into process knowledge and new processes.
– conceptual challenges:
• Theory applicability
• Experimental measurement development
• Significant uncertainty remains:
• molecular structure
• supramolecular structure
• phase behavior simulation and prediction
• transport properties (mutual diffusion coefficients and rheology)
43

44. •The subject and the potential prizes are vast. We are pushed to the frontiers
of knowledge in analytical chemistry, computational thermodynamics, fluid
physics.
•There are excellent opportunities for individual and collaborative research
related to production, transport and refining sectors globally.
•Choose a length scale and a topic and get going!
44
*image: Experiencia KONEX, 22, April – June, 2013

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