Sai R Panuganti , Anju S Kurup, Francisco M Vargas, Walter G Chapman

1. PC-SAFT Crude Oil Characterization for Modeling of Phase Behavior and Compositional Grading of Asphaltene Sai R Panuganti, Anju S Kurup, Francisco M Vargas, Walter G Chapman

2. Outline • Asphaltene introduction • Background of asphaltene thermodynamic analysis • Comparison of Cubic and PC-SAFT EoS • Robustness of PC-SAFT characterization methodology • Asphaltene compositional grading • Future Work • Conclusion

3. Introduction • Modified Yen Model • Mullins OC. Energy & Fuels 2010; 24(4):2179-2207 Asphaltene Polarizable Polydisperse Heavy fraction in crude oil • Operational Definition • Soluble in aromatic solvents • Insoluble in light paraffinic solvents

4. Modeling Asphaltene Stability • Colloidal Model (~1930) • Stability based on polar-polar interactions. • Micelle formation • Asphaltene particles kept in solution by resins adsorbed on them • Solubility Model (~1980) • Asphaltene solubilized by the oil. • London dispersion dominate phase behavior. • Approaches: (Less parameters) • Flory-Huggins-regular solution theory • EoS • Limitations of Colloidal Model: • Negative Hydropilic-Lipophilic Balance for asphaltene [Czarnecki J-2009] • Impedence Analysis – Resins are unlikely to coat asphaltene [Goual-2009] • Diffusion coefficient of asphaltene is same in the presence and absence of resin Nellensteyn FJ. Journal of the Institute of Petroleum Technologist 1928; 14:134-138

5. Solubility Model Approaches • Flory-Huggins type models Limitation: • Effective molar volume significantly lower than actual molar volume. • Cannot account for compressibility • Equations of State 1. Cubic-EoS 2. SAFT based models Hirshberg A. Journal of Petroleum Technology 1988; 40(1):89-94

6. Modeling using Cubic EoS(Crude A) • Crude A • Characterized the crude oil system using PVT-Sim of Calsep • The Cubic EoS employed was SRK-P

7. Modeling using Cubic EoS(Crude A) The optimized Cubic EoS parameters from 5% were used to predict the phase behavior for 30% injected gas Limitations of cubic equation of state: • Asphaltene critical properties are not well known • Results are very sensitive to parameters Larry GC et al. Advances in Thermodynamics (Volume 1): C7+ Fraction Characterization. Taylor & Francis; 1989

8. Introduction to SAFT • Parameters represent the physical system directly • PC-SAFT EOS is be used • Parameters for most compounds are known • Chapman WG et al. Industrial Engineering and Chemistry Research 1990; 29(8):1709-1721. • Gonzalez DL et al. Energy & Fuels 2005; 19(4):1230-1234.

9. PC-SAFT Characterization SARA analysis Flashed Liquid and Gas compositions (C9+) Molecular Weights Liquid Density Bubble Pressure AOP Developed a standardized characterization procedure based on: Methodology: • Composition data up to C9+ is sufficient. • Few parameters were needed • Temperature independent binary interaction parameters for all compounds are very small Panuganti SR et al. “PC-SAFT Characterization of Crude Oils and Modeling of Asphaltene Phase Behavior” Fuel - Submitted Stable Unstable VLE

10. Comparison of PC-SAFT and Cubic EoS SRK-P PC-SAFT (Crude A) Characterized using PC-SAFT and SRK-P EoS • Will PC-SAFT work better than Cubic EOS? • Will a specific set of PC-SAFT parameters be sufficient to capture the phase behavior of the system at a different condition?

11. Comparison of PC-SAFT and Cubic EoS SRK-P PC-SAFT Crude B Better performance of PC-SAFT is visible • Will PC-SAFT with proposed characterization procedure be able to predict phase behavior for higher amounts of gas injected?

12. Comparison of PC-SAFT and Cubic EoS SRK-P PC-SAFT Crude B PC-SAFT holds upper hand over C EoS What about for even higher gas injection?

13. PC-SAFT vs. Optimized Cubic EOS SRK-P PC-SAFT Crude B

14. Prediction of Effect of Gas Injection Crude C • A different crude, exhibiting different physical properties. • Characterized using standardized methodology

15. Robust Methodology Crude C 1. Robust Methodology 2. Good parameter estimation Any property of the precipitate phase can be calculated

16. Compositional Grading Introduction Used for: 1. To predict oil properties with depth 2. Find out gas-oil contact How is asphaltene compositional grading useful? Reservoir connectivity A M Schulte. SPE Conference; September 21-25, 1980 Høier L, Whitson CH. SPE 74714; 2001; 4(6)525-535

17. Compositional Grading Algorithm Whitson C H & Belery P; SPE 28000 1994 443-459

18. Reservoir Compartmentalization • All zones belong to the same reservoir as the gradient slopes are nearly the same. • The curves do not overlap meaning each of them belong to different zone.

19. Approximate Analytical Solution ρ= Molar density; h=Depth; = Partial Molar Volume Mi =Mol wt Assumptions: Changes in density of oil with depth can be neglected At infinite dilution partial molar volume is independent of composition System is far away from critical point such that partial molar volume is independent of pressure changes Sage BH, Lacey WN. Los Angeles Meeting, AIME; October 1938 Morris Muskat. Physical Review 1930; 35(1):1384-1392

20. Approximate Analytical Solution • We have Partial molar volume of asphaltene = 1934 cm3/mol. It corresponds to a particle size of 1.83 nm • Analytical solution can be used for sensitivity analysis and approximate estimate.

21. Future Work • Tar mat occurrence due to compositional grading of asphaltene. • QCM-D experiments for determination of asphaltene deposition rates and aging effects. • Micro fluidic studies to understand the asphaltene deposition mechanism.

22. Conclusion • Solubility model using PC-SAFT EoS • PC-SAFT characterization methodology proposed • Robustness of PC-SAFT characterization methodology • Evaluate reservoir compartmentalization through asphaltene compositional grading.

23. Acknowledgement • Walter G Chapman • Francisco Vargas • Anju S Kurup • Jeff Creek

25. Characterization of T Oil

26. Derivation of Thermodynamic Model

27. Algorithm