ISES 2013 - Day 2 - Professor John M. Dhaw (Professor, University of Alberta) - Energy on New Frontiers

  • 182 views
Uploaded on

Game-Changing Technologies In The Oil and Gas Industry …

Game-Changing Technologies In The Oil and Gas Industry

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?

  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Be the first to comment
    Be the first to like this
No Downloads

Views

Total Views
182
On Slideshare
0
From Embeds
0
Number of Embeds
0

Actions

Shares
Downloads
4
Comments
0
Likes
0

Embeds 0

No embeds

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
    No notes for slide
  • Cape horn south america cape of good hope africa

Transcript

  • 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
  • 45. 45