Using new modeling technology to help solve heavy oil processing issues final

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Heavy feedstocks present difficult operational challenges for refiners that can add to safety risks and reduce profitability. Processing heavy crudes safely and profitably can require development of new equipment or major changes in operating conditions.

Innovative new methods, which model heavier feedstock processing more accurately, enable refiners to adapt their processes more easily.

Register now to learn more about this important new technology.

Who should attend: Plant Managers, Process Engineers, Engineering Managers, Operations Managers, Process Design Engineers

View OnDemand at: www.real-time-answers.com/refinery

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Using new modeling technology to help solve heavy oil processing issues final

  1. 1. Using New Modeling Technology toHelp Solve Heavy Oil ProcessingIssues© 2010 Invensys. All Rights Reserved. The names, logos, and taglines identifying the products and services of Invensys are proprietary marks of Invensys or itssubsidiaries. All third party trademarks and service marks are the proprietary marks of their respective owners.
  2. 2. www.real-time-answers.com/refinery/
  3. 3. Introducing our first speaker
  4. 4. Crude OilYesterday and Today• Initially oil was developed based on it ease of access and quality – Light Sweet Crude – Relatively shallow fields• However…"The easy oil is coming to an end“* – Production in the North Sea is on the Decline – Major oil fields in the Gulf region have pumped more than half their oil Drumbeat: October 5, 2009, The Oil Drum *Alex Munton, Analyst for Wood Mackenzie
  5. 5. Crude Oil Demand• Demand keeps increasing –Growth in China and India jumped by 2.3 MBPD barrels in 2010 –Current consumption is +87 MBPD and expected to increase 2-3% per year –Prices per BBL remaining at $80 to $100 EIA, International Energy Outlook 2011
  6. 6. Heavy Oil / BitumenGaining Momentum• Largest % of Reserves• Aggressively being sought out as prices remain elevated.• Resources are estimated +8 Trillion Barrels Bitumen 30% Conventional 30% –That’s about 100 years of global consumption at current levels. Heavy 15% Extra Heavy 25% Alboudwarej, Hussein, "Highlighting Heavy Oil“ Oilfield Review (Schlumberger).
  7. 7. Heavy Oil / Bitumen Deposits (MBBL) 529 3041 92 482 178 971 18 129 72 3359 Heavy Oil USGS, Heavy Oil and Natural Bitumen Resources in Geological Basins of the World Bitumen
  8. 8. Characteristics• High asphaltene content –Incorporates roughly 90 percent of the sulfur and metals in the oil• Dense, viscous nature (similar to molasses),• Contains impurities such as waxes and carbon residue.• API Gravity from 22° to below 10° 3D Picture of Venezuelan Crude Asphaltene Molecule
  9. 9. Challenges – Technology &InfrastructureProduction• Location (e.g. Tar Sands)• Extraction ProcessesTransportation• Limited Pipelines• Flow capabilityProcessing• Old Equipment Designs• Impurity Removal• Inconsistency in composition
  10. 10. Investment is Ongoing• Extraction Technologies – Expect $20 Billion USD in Western Canada alone – Mining Technologies / In Situ development, e.g. SAGD• Billions in Transportation – Keystone XL Pipeline• Billions in Refining Investments – $1.6 Billion upgrade at Valero Port Arthur – $3.7 Billion expansion Cenovus /ConocoPhillips Wood River
  11. 11. The Role of Modeling• Models serve a critical role in the production, transportation and processing of crude oil – Design: Proper sizing and costing of equipment – Operate: Used to study changes in the process, changes in the feedstock, Operating Training simulation – Optimize: Reduce Energy, Increase Product Value, Improve Procedures
  12. 12. Getting It Right• Years of experience on light crudes• Heavy Crudes are a challenge –Lack of experience –Complex relationships –Impurities Without the proper models, companies run the risk of not getting the most out of their Investment!
  13. 13. Introducing our second speaker
  14. 14. Agenda – Heavy Oils Webinar• Functionality currently available to improve simulations of processes with heavy oils and bitumens • Improved characterization of high boiling point petro components (Tb > 1,000K) • Improved accuracy in prediction of liquid viscosity • Improved accuracy in prediction of liquid thermal conductivity • EoS binary interaction parameters for accurate prediction of Hg solubility in light hydrocarbons • EoS binary interaction parameters for accurate prediction of H2 and H2S solubility in hydro-processing applications• Functionality under development • Characterization of asphaltenes and prediction of phase equilibrium • Molecular-based characterization as alternative to petro-components • New properties from structure method for improved accuracy of thermo- physical properties
  15. 15. Heavy Oil Method for Characterization• The original SIMSCI (Twu) correlation was developed for Normal Boiling Points (NBP) of up to 1000 K (1340 oF).• The Heavy Oil method extrapolates critical properties and molecular weight to NBPs above 1000 K.• Property at 1000K is used as reference and extrapolation is based on constant Watson K to NBP of pseudo component
  16. 16. Method for Improved Accuracy in Predictionof Liquid Viscosity• Based on correlation published by Twu* [1985]• Uses principle of corresponding states – Normal alkane reference series – Kinematic viscosities at 100oF and 210oF are estimated for n-alkane with same boiling point as fluid of interest – Kinematic viscosities at 100oF and 210oF are calculated for fluid of interest based on perturbation from reference series, based on specific gravity and boiling point.• Walther equation is fitted to estimate temperature dependence and used to interpolate or extrapolate *Twu, C.H., Ind. Eng. Chem. Process Des. Dev., 24 (1985) 1287-1293 Slide 16
  17. 17. Effect of Accuracy of Viscosity Prediction onHeat Exchanger Performance Crude20.98oAPI 735oF MeABP 11.43Watson K 103oF 400 psig Vac Resid 4.6oAPI 1182oF MeABP 11.35 Watson K Slide 18 278oF 250 psig
  18. 18. Effect of Accuracy of Viscosity Prediction onHeat Exchanger Performance Viscosity(L) Measured API Procedure Heavy Oil method Values 11A4.2 (2011) Prediction Crude Duty (106 BTU/hr) 3.654 8.976 5.43220.98oAPI 735oF MeABP LMTD (oF) 165 150 159 11.43 U (BTU/hr/ft2/oF) 4.657 12.62 7.144Watson K Shell Side 103oF Tout (oF) 265 245 258 400 psig ΔP (psi) 58 8 27 Tube Side Tout (oF) 110 119 113 ΔP (psi) 9 13 11 Vac Resid 4.6oAPI 1182oF MeABP 11.35 Watson K Slide 19 278oF 250 psig
  19. 19. Effect of Accuracy of Viscosity Prediction onHeat Exchanger Performance Temperature, oF Crude Kinematic Viscosity, cSt Measured API 11A4.2 Heavy Oil Crude20.98oAPI 60 49.75 111.6 70.65 735oF 80 27.95 58.77 39.69 MeABP 100 17.52 34.26 24.38 11.43Watson K Vac Resid Kinematic Viscosity, cSt 103oF 210 6787 454.0 4784 400 psig 250 1261 147.5 858.5 300 274.6 50.83 176.8 Vac Resid 4.6oAPI 1182oF MeABP 11.35 Watson K Slide 20 278oF 250 psig
  20. 20. Conclusions: SimSci Heavy Oil Method forLiquid Viscosity Prediction• Method results in a significant improvement to accuracy of prediction of liquid viscosities – Marked improvement in the 100 cSt to 100,000 cSt range – No reduction in accuracy in the 0.1 cST to 100 cSt range• Temperature dependence, especially at low temperatures is improved• Observations: – Method is very sensitive to value used for normal boiling point (method uses SimDis 50% point) – It appears best to treat undefined portion of fluid (petro fractions) as single component and avoid applying mixing rules to individual component viscosities – Accuracy may be improved further if more heavy oil viscosity data are made available – Some correlation between viscosity and asphaltene content was expected, but not supported by current data set Slide 21
  21. 21. Prediction of Liquid Thermal Conductivity• Negligible amount of published data for liquid thermal conductivity for bitumens and heavy oils – Data from Wiltec* for narrow boiling fractions from SRC-II coal liquids• Fortunately, thermal conductivity (λL) for most organic liquids varies between 0.10 and 0.17 W.m-1.K-1and varies linearly with temperature – No successful theory, to date, for predicting λL – Approximate techniques are recommended for engineering calculations• Heavy Oil Method (modified Sato-Riedel)λL = const1 + const2(1 – Tr)2/3 Heavy Oil*Gray, J.A., Brady, C.J., Cunningham, J.R., Freeman, J.R., Wilson, G.M., Ind. Eng. Chem. Process Des. Dev. 1983,22, 410-424
  22. 22. Conclusions: Liquid Thermal Conductivity• The best and the worst: – Riazi & Faghri method predicts wrong sign of temperature gradient for heaviest fractions – Heavy Oil method (based on Sato-Riedel) requires only estimate of critical temperature, Tc and appears to be the most accurate• Very little data available – No data on bitumens – Some evaluated data on pure components from NIST via TDE – Coal liquid data (used as surrogate for bitumen) are suspect Slide 23
  23. 23. Adverse impact of Mercury• Process impairment• Process equipment failure (corrosion / cracking)• Catalyst poisoning• Adsorption bed malfunction• Wastewater contamination• Mercury carryover in flare systems
  24. 24. Mercury Solubility in n-Alkanes at 30°C 1.8 Mercury Solubility (ppm-molar) 1.6 1.4 1.2 1 Measured ZERO Kijs (SRK EOS) ‐ Uncorrected Mercury Vapor Pressures 0.8 CUSTOM Kijs (SRK EOS) ‐ Uncorrected Mercury Vapor Pressures ZERO Kijs (SRKM EOS) 0.6 CUSTOM Kijs (SRKM EOS) 0.4 0.2 5 6 7 8 9 10 Carbon Number of n-Alkane
  25. 25. ResultsMercury — n‐Alkane (nC1 – nC10) binary interaction parameters available forlow pressure, low temperature (0-65 °C) applicationsSRK, SRKM, PR and PRM Equations of State• Predicted solubility validated against thermodynamic and plant dataSRKM and PRM Equations of State•Additionally, EOS α-parameters fine-tuned to match mercury vaporpressures to 1500 K
  26. 26. Hydrogen Solubility Prediction• Heavy Oils require increased Hydroprocessing to remove trace impurities (Sulfur, Nitrogen etc)• Supply and Management of Hydrogen is critical to Refinery optimization and meeting stringent Sulfur specification• Current techniques are inaccurate as T & P dependence not considered – Very little published data with Heavy Fractions• Invensys SimSci-Esscor is taking to lead in this emerging area – Improve prediction of H2 solubility in hydrocarbons – Improve prediction of H2S solubility – Improve prediction of NH3 solubility
  27. 27. Hydrogen – n-Paraffins kij = kij(T) P, T, x regression Invensys proprietary & Slide 28 confidential
  28. 28. Hydrogen – mono-aromatics kij = kij(T) P, T, x regression Invensys proprietary & Slide 29 confidential
  29. 29. Solubility of hydrogen in n-decane – no kijs Invensys proprietary & Slide 30 confidential
  30. 30. Solubility of hydrogen in n-decane – with kijs Invensys proprietary & Slide 31 confidential
  31. 31. Observations and Conclusions• For H2 – hydrocarbon binaries, kij appears to be a linear function of temperature – kij should take values greater than unity at high temperatures• For H2 – hydrocarbon binaries, kij required for an aromatic component appears to be larger than kij for similar molecular weight paraffin• For H2S – hydrocarbon binaries, kij is poorly correlated with temperature ‒ Very low R2 values• Work is continuing for NH3 – hydrocarbon binaries• Work is continuing to generalize kij for undefined narrow-boiling fractions (petro components), based on molecular descriptor(s) – Boiling point, API gravity and/or molecular weight
  32. 32. Functionality Under Development• Asphaltene Phase Behavior• Component-Based Characterization• Properties from Structure© 2010 Invensys. All Rights Reserved. The names, logos, and taglines identifying the products and services of Invensys are proprietary marks of Invensys or itssubsidiaries. All third party trademarks and service marks are the proprietary marks of their respective owners.
  33. 33. Asphaltenes• Bitumen and heavy oils are rich in Structures of Hypothetical Asphaltene asphaltenes Components* • Heaviest, most polar fraction • Insoluble in n-alkanes, soluble in aromatics • Self-associating, forming aggregates • Contribute significantly to viscosity and coking tendency• Asphaltenes are precipitated to lower viscosity and make more easily refined product (e.g. VAPEX processes)• Heavy oils are diluted with solvent (e.g. condensate) to lower viscosity for transport• To optimize processes, it is necessary to have accurate prediction of amount of asphaltene precipitated as function of amount of solvent temperature and pressure *From University of Utah
  34. 34. Calculation of Asphaltene Phase Behavior• Bitumen is divided into four SARA fractions, Saturates (S), Aromatics (A), Resins (R), Asphaltenes (A)• Asphaltene fraction is further divided into fractions with different molar masses based on gamma distribution• Correlations from Akbarzardeh* et al. provide estimates of solubility parameters and molar volumes of asphaltene fractions• Flory-Huggins version of Regular Solution Theory is applied to predict activity coefficients of asphaltene fractions • Assume liquid-liquid equilibrium • Heavy, asphaltene-rich phase with only asphaltenes and resins • Light oil-rich phase including all components• Parameters of gamma distribution are adjusted to match onset of precipitation conditions *Kamran Akbarzadeh, Hussein Alboudwarej, William Y. Svrcek, Harvey W. Yarranton, Fluid Phase Equilibria 232 (2005) 159-170
  35. 35. Asphaltenes Characterized as Low Molecular Weight Polymers Shultz-Zimm distribution (special case of Gamma distribution) f(x,α,β) = βα/Γ(α). x(α-1).e-βx, mean = α/β x = (r – 1)/(ravg – 1) r = number of monomer units ravg = average number of monomer units11/7/2012 36
  36. 36. Middle Distillate Example MOLECULAR-BASED CHARACTERIZATIONSlide 37
  37. 37. From: Narrow Boiling Fraction Representationof Middle Distillate NBP 540 (Tb = 540K, S = 0.8876) Tpc = 745.78K Ppc = 2093.07 kPa API Procedure 4D3.1 (1987) ( ) T pc = 10.6443 exp − 5.1747 × 10 − 4 Tb − 0.5444S + 3.5995 × 10 − 4 Tb S Tb0.81067 S 0.53691 API Procedure 4D4.1 (1987) ( ) Ppc = 6.162 × 10 6 exp − 4.725 × 10 −3 Tb − 4.8014 S + 3.1939 × 10 −3 Tb S Tb−0.4844 S 4.0846 where : Tb = mean average boiling point in oR S = specific gravity, 60 o F/60 o F T pc = pseudocritical temperatur e in oR Ppc = pseudocritical pressure in psia Slide 38
  38. 38. To: Molecular-Based Representation ofMiddle Distillate
  39. 39. To: Molecular-Based Representation ofMiddle Distillate Tc Pc (K) (kPa) butyldecalin 729.78 1906 butylindan 749.25 2507 pentadecane 701.08 1471
  40. 40. Linear Combination of Bond Orbitals (LCBO) MethodApplied to Properties from Structure• Based on work of R.D. Brown (1953): for molecules with σ bonds, using a set of orbitals situated in each bond (e.g. methane’s 8 σ electrons move in orbitals formed by linear combinations of localized σ orbitals in each C-H bond• Total electronic energy by orders of overlap integral: – At zeroth order, just depends linearly on number of C and H atoms – At first order, just depends on number of CH3, CH2, CH and C atoms – At second, just depends on number of each possible Ci-Cj bonds (9 of them) and of above carbon atoms – At third order, just depends linearly on number of possible Ci-Cj-Ck triplets (35 of them) and of the above carbon atoms and bonds• The higher the degree of expansion, the more distinguishable will be the positions of isomers• LCBO method accurately predicts ΔH0f, ΔG0f, enthalpies of alkyl radicals, C-H and C-C dissociation energies• It is possible to add CH2, CH and C naphthenic carbon atoms to alkyl atoms and corresponding bond combinations• Provides additional accurate prediction of: – Activation energies required for kinetic model of thermal cracking (initiation, propagation, evolution and termination reactions)
  41. 41. Example: Decylbenzene, Normal Boiling Point Atoms Bonds Type Count Δi Type Count Δi CH3 1 0.10340 CH3-CH2 1 -0.04364 CH2 9 -0.28352 CH2-CH2 8 0.23626 aromCH 5 -0.05881 CH2-aromC 1 0.12346 aromC 1 0.02533 aromCH-aromCH 4 0.00341 aromCH-aromC 2 -0.03111Multi - linear regression of tranformed equation used to initialize non - linear form : ⎡⎛ N desc ⎞ ⎛ N desc ⎞⎤Tb = c3 ⎢⎜ c1 + ∑ ni ∆ i ⎟ ⎜ ⎟ ⎜ c 2 + ∑ ni ∆ i ⎟ ⎥ ⎜ ⎟ ⎣⎝ i =1 ⎠ ⎝ i =1 ⎠⎦N desc∑n ∆ i =1 i i = 1 * 0.10340 + 9 * −0.28352 + 5 * −0.05881 + 1 * 0.02533+ 1 * −0.04364 + 8 * 0.23626 + 1 * 0.12346 + 4 * 0.00341 + 2 * −0.03111 = −0.79569Tb = 1195[(− 0.10206 − 0.79569) (− 1.10206 − 0.79569 )]Tb = 565.31K Slide 42
  42. 42. Slide 43
  43. 43. Conclusions and Further Work• The Linear Combination of Bond Orbitals (LCBO) method improves accuracy of prediction of thermo-physical properties • Number of “atom” and “bond” descriptors is manageable for hydrocarbons and molecules with hetero-atoms found in crude oil • Linear and hyperbolic equation forms are sufficient to represent almost all properties • Linear regression can be used to ascertain statistical significance of regressed coefficients and confidence intervals around predictions • Equations to predict properties are independent of other property predictions• Molecular representation may be a viable alternative approach to the characterization of petroleum fluids • Accuracy in prediction of thermo-physical properties of mixtures can be improved • More knowledge of chemical structure is retained for reaction pathways and kinetics calculations• Work is in progress to: • Create a data bank of thermo-physical property data for components with boiling points up to 800K and above • Implement a Gibbs energy minimization method to infer composition in terms of a subset of defined surrogate components from basic assay data
  44. 44. Wrap Up• Heavy Oil / Bitumen is in Our Future• Current Modeling Technology is Limited• Invensys SimSci-Esscor is on a journey – Heavy Oil Consortium Providing Input – Working Through the Issues – Improving Model Robustness and Accuracy• Driven to ensure proper design, operations, and optimization of facilities• Ultimately translates into to cost savings for our clients through the design, operate, and optimize life cycle.
  45. 45. Thank you!
  46. 46. Questions
  47. 47. Thank you! Feel free to contact us Terry Kubera Terry.kubera@invensys.com David Bluck David.bluck@invensys.com48
  48. 48. VISIT www.real-time-answers.com/refinery/
  49. 49. APPENDIXSlide 50
  50. 50. Methods Evaluated for Prediction of λLAPI Procedure 12A1.2 λL = 0.4407M0.7717[(3 + 20(1 – Tr)2/3)/( 3 + 20(1 – 298.15/Tc)2/3] / VL25API Procedure 12A3.1 λL = 0.164 – 0.0001277TAPI Procedure 12A3.2 λL = Tb0.2904(2.551x10-2 – 1.982x10-5T)Heavy Oil Method (modified Sato-Riedel) λL = const1 + const2(1 – Tr)2/3Riazi & Faghri λL = (0.11594Tb0.7534SG0.5478 – 2.2989Tb0.2983SG0.0094)(1.8T - 460) / (3 + 2.2989x10-2Tb0.2983SG0.0094) Heavy Oil Slide 51
  51. 51. H2S – n-Paraffins, kij = kij(T) Invensys proprietary & Slide 52 confidential
  52. 52. H2S – n-Paraffins, kij = kij(T) Invensys proprietary & Slide 53 confidential
  53. 53. H2S – n-Paraffins, kij = kij(T) Invensys proprietary & Slide 54 confidential
  54. 54. H2S – n-Paraffins, kij = kij(T) Invensys proprietary & Slide 55 confidential
  55. 55. H2S – n-Paraffins, kij = kij(T) Invensys proprietary & Slide 56 confidential
  56. 56. H2S – n-Paraffins, kij = kij(T) Invensys proprietary & Slide 57 confidential
  57. 57. H2S – n-Paraffins, kij = kij(T) Invensys proprietary & Slide 58 confidential
  58. 58. H2S – n-Paraffins, kij = kij(T) Invensys proprietary & Slide 59 confidential
  59. 59. H2S – n-Paraffins, kij = kij(T) Invensys proprietary & Slide 60 confidential
  60. 60. H2S – n-Paraffins, kij = kij(T) Invensys proprietary & Slide 61 confidential
  61. 61. H2S – n-Paraffins, kij = kij(T) Invensys proprietary & Slide 62 confidential
  62. 62. H2S – mono-Aromatics, kij = kij(T) Invensys proprietary & Slide 63 confidential
  63. 63. H2S – mono-Aromatics, kij = kij(T) Invensys proprietary & Slide 64 confidential
  64. 64. H2S – mono-Aromatics, kij = kij(T) Invensys proprietary & Slide 65 confidential
  65. 65. H2S – mono-Aromatics, kij = kij(T) Invensys proprietary & Slide 66 confidential
  66. 66. H2S – mono-Aromatics, kij = kij(T) Invensys proprietary & Slide 67 confidential
  67. 67. Examples of Descriptors – σ Bonding, SaturatesExamples of Descriptors – π Bonding, Aromatics andHeterocyclics Slide 68
  68. 68. www.real-time-answers.com/refinery/

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