Anju Kurup, Walter Chapman

1. Development of Asphaltene Deposition Tool (ADEPT) Anju Kurup, Walter Chapman Department of Chemical & Biomolecular Engineering, Rice University Houston, TX, April 26, 2011

2. Outline • Introduction / Motivation • Asphaltene deposition simulator structure • Thermodynamic module • Deposition module • Results and discussion • Capillary scale experiments • Field cases – Thermodynamic modeling & deposition simulator predictions • Conclusions • Future work • Acknowledgements

3. What are asphaltenes? Heaviest and the most polarizable components of the crude oil. Solubility class of components of crude oil Insoluble in low molecular weight alkanes (e.g. n-heptane), Soluble in aromatic solvents (toluene or benzene) • Arterial blockage in oil well-bores – waxes, gas hydrates and asphaltenes. • Asphaltenes – Special challenge - not well characterized, form a non-crystalline structure, deposition can occur even at relatively high temperatures.

4. Asphaltenes - Flow Assurance Context http://pubs.acs.org/cen/coverstory/87/8738cover.html • Asphaltenes affect oil production • Deposition in • Reservoirs – near well bore region – alter wettability. • Well bore. • Other production facilities – separator, flow lines, etc. • Poison refinery catalysts. Intervention costs – USD 500,000 for on-shore field to USD 3,000,000 or more for a deepwater well along with lost production that can be more than USD 1,000,000 per Day*. *Creek, J. L. Energy & Fuels, 2005

5. Fast facts about Asphaltenes • Polydisperse mixture. • Deposition mechanism and molecular structure are not completely understood. • Behavior depends strongly on P, T and {xi} (addition of light gases, solvents and other oils in commingled operations or changes due to contamination). (a) n-C5 asphaltenes (b) n-C7 asphaltenes http://baervan.nmt.edu/Petrophysics/group/intro-2-asphaltenes.pdf Uncertainties in literature about asphaltenes (a) Condensed aromatic cluster model (Yen et al, 1972), (b) Bridged aromatic model (Murgich at al., 1991)

6. Motivation Predict asphaltene flow assurance issues Ability to model asphaltene phase behavior as a function of temperature, pressure, and composition. Model mechanisms by which asphaltenes precipitate, disperse, and deposit. • Differentiate between systems that precipitate and deposit and those that precipitate and do not form deposits in well-bores. • Improve deposition prediction. Improved operating practices & risk mgt.

7. Literature review Well bore modeling • Ramirez-Jaramillo et al., 2006, - Molecular diffusion along with shear removal model to describe deposition (SAFT-VR – therm model). • Jamialahmadi et al., 2009, - Mechanistic model - flocculated asphaltene concentration, surface temperature and flow rates – parameters fit to expt. Soulgani et al., 2009 – model of Jamialahmadi et al., with Hirschberg model (thermodynamic modeling) to predict well shut down time and compared with field data. • Vargas et al., 2010 – Conservation equations with proposal to couple with PC SAFT (therm model). • Eskin et al., 2010 - Uses particle flux expressions from literature for particle suspended in turbulent flows to describe diffusion and turbulent induced particle transport, use population balance model to compute particle size distribution in the oil phase, Model parameters obtained by fitting to expt data obtained from Couette flow device. Reservoir modeling / formation damage modeling • Leontaritis 1997, Nghiem and Coombe 1998, Kohse and Nghiem 2004, Wang and Civan 1999, 2001, 2005, Almehaideb 2004 - Surface deposition, pore throat plugging and re-entrainment of deposited solids. • Boek et al., 2008, in press, SRD simulations considering asphaltenes as spherical molecules. Need for quantitative & qualitative comparison of deposition profile

8. Simulator Structure Experimental & Field Data Translator VLXE / Multiflash Oil & Asphaltene Characterization P & T Thermodynamic Modeling Module Asphaltene Solubility CA* Flow rate & geometry Deposition Simulator Asphaltene deposition profile & thickness Precipitation, Aggregation & Deposition Rates Experimental & Field Data

9. m /k  e Thermodynamicmodeling PC SAFT(Perturbed Chain Statistical Associating Fluid Theory) • Parameters required to characterize each component of the mixture: • Segment size () • Number of segments in a molecule (m) • Segment-segment interaction energy (/k) Chapman et al., 1988, 1990 Molecules modeled as chains of bonded spherical segments Gross and Sadowski (2001) proposed PC SAFT – successful in predicting phase behavior of large molecular weight fluids – Asphaltene molecules. Multiflash (Infochem) and VLXE

10. Thermodynamicmodeling Gonzalez, Ph.D. Dissertation, 2008 P-T diagram: Comparison of experimental bubble point and asphaltene onset curves with PC SAFT predictions Comparison of experimental bubble point and asphaltene onset curves with PC SAFT predictions for increased nitrogen gas injection Oil characterization & PC SAFT parameter estimation: thermodynamic module Exp. Data: Jamaluddin et al., SPE 74393 (2001)

11. Simulator Structure Experimental & Field Data Translator VLXE / Multiflash Oil & Asphaltene Characterization P & T Thermodynamic Modeling Module Asphaltene Solubility CA* Flow rate & geometry Deposition Simulator Asphaltene deposition profile & thickness Precipitation, Aggregation & Deposition Rates Experimental & Field Data

12. Wellbore Deposition Simulator Goal  Develop a simulation tool for prediction of occurrence and magnitude of asphaltene deposition in the well bore. advection diffusion

13. Proposed Model Mass balance of asphaltene aggregates in a controlled volume: Accumulation=Diffusion –Convection –Aggregation +Precipitation –Deposition Asphaltene Precipitation / Aggregation / Deposition – first order kinetics Kp, Ka, Kd PRRC, NMT

14. Capillary experiments (NMT) Asphaltene deposition at capillary scale flows Comparison of experimental asphaltene deposition flux with model predictions Capillary deposition experimental results from NMT (Dr. Jill Buckley)

15. Capillary experiments Good qualitative and quantitative agreement between expt and simulations. Comparison of experimental asphaltene deposition flux with model prediction Some discrepancies exist. Overall trend matched.

16. Hassi-Messaoud – Field case 1 Thermodynamic modelingPC SAFT Live oil composition – Haskett and Tartera (1965), SARA – Minssieux (1997) Density prediction = 0.8096 g/cm3 Reported = 41.38 = 0.8185 g/cm3 Precipitation envelope P-T operating condition Ceq variation along the axial length was computed – input to simulator.

17. Hassi-Messaoud – Field case 1 Simulator prediction Simulation parameters Operating and kinetic parameters Asphaltene deposition profile as reported in (Haskett and Tarterra, 1965) • Input from thermodynamic model, duration – 25 days (average of reported time intervals), thickness of deposit matched. • Spread of deposit ~ 2000 ft while reported ~ 1000 ft. • Depends on P-T operating curve - Changes as production continues. • Paper – P-T curve for one well bore while deposit measurements are after the asphaltene mitigation treatment utilized in the paper. Qualitative and Quantitative agreement Model prediction

18. Kuwait Marrat well – Field case 2 Thermodynamic modeling –PC SAFT Asphaltene precipitation envelope SARA - Kabir and Jamaluddin, 1999 • Live oil composition, saturation pressure data from Chevron. • PC SAFT thermodynamic characterization. • Calculated Ceq variation along the length of well bore – input to simulator. *Kabir et al., SPE 71558, 2001 **Data from Chevron

19. Kuwait Marrat well – Field case 2 Simulator prediction Operating parameters • For 2 months: thickness matched, 1 and 3 month kd changes respectively. • With appropriate choice of dissolution kinetics and other kinetics a good qualitative and quantitative agreement is obtained. • P-T curve with axial length has impact on precipitation start and end zone. *Kabir et al., SPE 71558, 2001

20. Summary • Development of Asphaltene deposition simulator – I. • Thermodynamic module. • Deposition module. • Successful application of the simulator to predict asphaltene deposition in capillary experiments. • Simulator used for deposition prediction in well bores. • Two field cases studied. Thermodynamic model of the live oil was developed and coupled with the deposition module to predict deposition in well bores. • A good qualitative and quantitative match between reported field data and simulator predictions has been obtained.

21. x Y Z Microsoft Excel interface for ADEPT

22. Future Activities Protocol for deposition prediction Steps to be followed, Tests to be conducted, Parameters to be determined. Obtain more capillary experiment data and compare simulator predictions. Obtain field case data and compare simulator predictions. Propose set of experiments to be performed to obtain kinetic parameters used in the simulation tool. Model improvement to address limitations of the present simulator. Incorporate effect of aging Scaling up issues of kinetic parameters Version I to be used in conjunction with flow simulators – sensitivity analysis of operating parameters Operating guidelines to reduce deposition probability

23. Acknowledgments DeepStar Chevron ETC Schlumberger New Mexico Tech Infochem VLXE