Hydrocarbon Thermophysical Properties:

unexpected frontiers

John M. ShawProfessor 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

Acknowledgements

SponsorsNatural Sciences and Engineering Research Council of Canada Alberta Innovates - Energy and Environment Solutions BP CanadaConocoPhillips Canada Resources Corp.Nexen Inc. Shell Canada Ltd.Total E&P Canada Ltd.Virtual Materials Group

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ColleaguesMarco Satyro, Harvey Yarranton, Loic Barre, Kirk Michaelian, Jean-Luc Daridon, Jerome Pauly, …

So what is the big deal?

We’ve been doing this for more than a century at an industrial scale globally

So what is the big deal?

We’ve been doing this for more than a century at an industrial scale globally

but …

Canadian National Advisory Panel Report 2006

Other than CO2 capture and storage, and gasification, there is no mention of research in the “carbon sector”

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Chemical, thermodynamic and transport property knowledge ranked last among NINE surveyed industrial priorities. New processes ranked first.

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.

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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.deCalculation suggestion: Michael Raymont, CEO EIN

football stadium, Vanderbilt University.

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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)

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140 795132*

206*335*

* Including a fraction of heavy oil/bitumen reserves, BP statistical Review 2012

Middle East

EurasiaAfrica

South AmericaNorth

AmericaAsia Pacific 41

•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

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known and mapped for ~ 100 years* Phase diagram 2011** liquid crystals identified, 2010***

Oil Sands

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) 32-41Bazyleva, Al. et al., J. Chemical & Engineering Data, 2011 56 (7) 3242-3253.

Equilibrium Modeling

Speciation is THE challenge for mixtures containing heavy hydrocarbons.

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Enthalpy modeling is “solved.” A rare success but implementation of the methods poses challenges.

Heat capacity modeling – naive approximation

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n

ii

n

i i

i

n

iii

n

ii

n

iii

n

ii

w

Mw

Mx

x

MM

N

1

1

1

1

1

1

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:

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 ()

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).

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.

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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

Speciation and Modeling for CEoS

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SpeciationDivide fluid into components and pseudo-componentsAssign 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, TCorrelations

xi, SGi, MWi, NBPi,

Tci, Pci, i

interaction parameters

H.Yarranton & M. Satyro helped here!

Speciation of Heavy OilC

arbo

n N

umbe

r

Atmospheric Equivalent Boiling Point

Boduszynsky, E&F, 1987

ISSUE: How best to represent property distributions to predict phase behavior and phase properties?

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“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!

Pseudo-Components for CEoS- Boiling CutsC

arbo

n N

umbe

r

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.

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NBP (distillation based)

H.Yarranton & M. Satyro helped here!

Pseudo-Components for CEoS-Representative Molecules

Car

bon

Num

ber

Atmospheric Equivalent Boiling Point

Boduszynsky, E&F, 1987

Characterize property distributions with a representative set of molecules

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Source: astrochemistry.ca.astro.it

H.Yarranton & M. Satyro helped here!

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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

Heavy Oil Speciation for CEoS - Refinery Approach

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Bo

ilin

g T

emp

erat

ure

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!

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.

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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*

Phase Behaviour Computation Face-off

n-decane + (10, 20, 30, 40, 70, 90 wt %) AVR*

- Phase boundaries and critical phenomena.

NOTES:

1.Above ~300 C, AVR begins to pyrolyze.2.Below ~ 50 C, AVR begins to solidify.

* X. Zhang, PhD Thesis, 2006.

Supplied by Syncrude

Molecule generation algorithm*

13C NMR, CHNOS, …

Refinery CharacterizationGroup 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.

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

NN

N

N

VO

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.

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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

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

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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*

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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)

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Physical Models for Asphaltene Aggregation and Deposition

Dr. Yeganeh Khaniani, PDF, work in progress; Amin Pourmohammadbagher (PhD thesis, University of Alberta, in progress)

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Physical Models for Asphaltene Aggregation and Deposition

Dr. Yeganeh Khaniani, PDF, work in progress; Amin Pourmohammadbagher (PhD thesis, University of Alberta, in progress)

Depletion flocculation driven liquid-liquid phase behavior – toluene + polystyrene + asphaltenes

32Khammar,M.; Shaw, J.M.,  Energy & Fuels 2012, 26 (2), 1075-1088.Khammar, M.; Shaw J.M., Review of Scientific Instruments 2011, 82, (10).

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)

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local speed of sound acoustic wave attenuationattenuation 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).

Phase Diagram Prediction - asphaltene + toluene + polystyrene mixtures

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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).

The phase behavior of bitumen + diluents

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Phase volumes, phase densities, phase boundaries, phase diagrams, mutual diffusion coefficients, ….

Poster II: Farshad Amani

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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.

Nanostructure change – diluted bitumen

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Mean size - radius of gyration (open symbols) - solid sphere assumption (closed symbols)

Scattering coefficient - structure

Nanostructure change with phase boundaries – diluted bitumen fraction

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L1L1L2

radius of gyration surface: volume ratio scattering coefficient

L1 L1 L1 L1 L1

L2 L2L2

pentaneAthabasca vacuum residue (AVR)

L1L2L1L2 L1 L1

X-ray

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Impacts of Materials Complexity on Rheology – example Maya Crude Oil

Thixotropy

Shear Thinning

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

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Mutual diffusion coefficient measurement

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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

The next challenge is to make measurements in natural porous media!

42Poster III: Dr. Marc Cassiede.

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)

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•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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