Estimating Reservoir Connectivity and Tar-Mat Occurrence Using Gravity-Induced Asphaltene Compositional Grading

Transcription

1 Estimating Reservoir Connectivity and Tar-Mat Occurrence Using Gravity-Induced Asphaltene Compositional Grading Sameer Punnapala 1, Sai Panuganti 2, Francisco Vargas 1 and Walter Chapman 2 1 Department of Chemical Engineering, The Petroleum Institute, Abu Dhabi 2 Department of Chemical and Biomolecular Engineering, Rice University, Houston Third EAGE/SPE Workshop on Tar Mats Abu Dhabi, UAE 21 May 2012

2 Motivation Understanding reservoir connectivity helps in effective sweep of oil for a given number of wells Pressure communication can only be used to understand compartmentalization The presence of a tar mat could not be inferred from the PVT behavior of the reservoir oil in the upper part of the reservoir Hirschberg, A. JPT 1988; 40(1):89-94 Zao, J.Y., et al., Journal of Chemical & Engineering Data, 2011; 56(4):

3 Fast Facts about Asphaltenes Polydisperse mixture of the heaviest and most polarizable fraction of the oil Defined in terms of its solubility Miscible in aromatic solvents, but insoluble in light paraffin solvents Deposition mechanism and molecular structure are not completely understood Behavior depends strongly on P, T and {x i } (a) n-c 5 asphaltenes (b) n-c 7 asphaltenes Buckley, J. To be published.

4 Outline Introduction PC-SAFT Asphaltene Phase Behavior Modeling Asphaltene Compositional Grading Prediction of tar-mat occurrence Conclusion

5 Advanced EOS Modeling Accurate Model for Asphaltene Precipitation Case Study: Fluid B, Comparison SRK Vs PC-SAFT Pressure, psia 10,000 8,000 6,000 4,000 2, % 30% gas (fit) (prediction) PC-SAFT SRK+P Temperature, F

6 SAFT Equation of State m ε/k σ A RT res = A RT seg + A RT chain + A RT assoc Chapman, Jackson, and Gubbins, Mol. Phys. 65, 1057 (1988) Gross & Sadowski, Ind. Eng. Chem. Res., 40, (2001) Gonzalez. PhD Thesis. Rice University, 2008

7 PC-SAFT Modeling of Asphaltene PVT Behavior Tahiti Field - Black Oil, Offshore, Gulf of Mexico GOR: 510 scf/stb API: ~30 o S Field Light Oil, Onshore, Middle East GOR: 787 scf/stb API: ~40 o Panuganti, S.R. et al., Fuel, 2012; 93:

8 Compositional Grading Compositional Grading can be a result of: 1. Gravity segregation 2. Thermal diffusion 3. Incomplete hydrocarbon migration/mixing 4. Natural convection 5. Asphaltene precipitation 6. Biodegradation 7. Reservoir compartmentalization Used to: 1. Predict oil properties with depth 2. Find out gas-oil contact Schulte, A.M., SPE Conference, 1980; September 21-25, SPE 9235

9 ) ( ),, ( ),, ( o i o o i i h h g M T Z P T Z P + = µ µ AOP = RT h h g M f f o i o i i ) ( exp ˆ ˆ Compositional Grading Tahiti Field PC-SAFT prediction matches the field data

10 Predicting Asphaltene Compositional Grading Optical Density nm) PC-SAFT (M21B) Field Data (M21B) Depth (ft) PC-SAFT (M21A Central) Field Data (M21A Central) PC-SAFT (M21A North) Tahiti Field Field Data (M21A North) All continuous lines are PC-SAFT predictions All zones belong to the same reservoir as the gradient slopes are nearly the same The curves do not overlap implying each zone belongs to different compartment

11 Approximate Analytical Solution ρ i = Molar density; h=depth; ρi ( h1 ) ( M i Vi ρ) g ln = ( h2 h1 ) ρ ( h ) RT i ρ=mass density; M i = Molecular Weight 2 V i = Partial Molar Volume; Assumptions: 1. Incompressible oil 2. Asphaltene is present in the oil at infinite dilution 3. System is far away from critical point 4. Isothermal System Sage, B. H.; Lacey, W. N. Los Angeles Meeting, AIME; October 1938

12 Approximate Analytical Solution Optical Density nm) PC-SAFT (M21B) Analytical Solution (M21B) Field Data (M21B) PC-SAFT (M21A Central) Depth (ft) Analytical Solution (M21A Central) Field Data (M21A Central) PC-SAFT (M21A North) Analytical Solution (M21A North) Field Data (M21A North) Field Data (M21A South) Tahiti Field Broken lines are the analytical solution predictions Analytical solution can be used for sensitivity analysis and approximate estimate

13 PC-SAFT Asphaltene Compositional Grading Asphaltene Weight % in STO Depth (ft) Reference Depth PC-SAFT asphaltene compositional grading extended to further depths Field observations did not report any tar mat

14 Predicting Asphaltene Compositional Grading S Field Depth (ft) Dimensionless Optical Density (OD/ODo) Zone A1 Well Z Zone B Field Data Well X Well Y All continuous lines are PC-SAFT predictions All zones belong to the same reservoir as the gradient slopes are nearly the same The curves do not overlap implying each zone belongs to different compartment Wells X and Y are connected because they lie on the same asphaltene grading curve

15 Tar-mat Onshore S field Tar-mat formation mechanism of S field Asphaltene compositional grading Other tar-mat formation mechanisms Settling of precipitated asphaltene Asphaltene adsorption onto mineral surfaces Oil-water contact Biodegradation Maturity between the oil leg and tar-mat Oil cracking Carpentier, B. et al. Abu Dhabi International Petroleum Exhibition and Conference 1998; November 11-14

16 Predicting Tar-mat Occurrence 7800 Asphaltene weight percentage in STO Depth (ft) Zone 1 Crude-Tar Transition Zone 2 Zone 3 Matches field observations and tar-mat s asphaltene content in SARA Zone 1 Liquid 1 (Asphaltene lean phase) Zone 2 Liquid 1 + Liquid 2 Zone 3 Liquid 2 (Asphaltene rich phase) Such a prediction is possible only with an equation of state Predicted the tar-mat formation depth matching field data, from PVT behavior in the upper parts of the reservoir Panuganti, S.R. et al., Energy and Fuels, 2011; dx.doi.org/ /ef201280d

17 Tar-mat Analysis Asphaltene Weight % in STO Asphaltene Weight % in STO Depth (ft) Depth (ft) T field S field Can the T field have an S field situation and vice versa?

18 Asphaltene Compositional Gradient Isotherms S field Depth (ft) Asphaltene weight % in STO Liquid 1 + Liquid 2 P = 3500 Psia P = 4000 Psia P = 5500 Psia P = 7500 Psia P = Psia P = Psia Phase Boundary Thus any field can show large or low asphaltene gradients without a need of asphaltene precipitation Panuganti, S.R. et al., Energy and Fuels, 2012; The 1st International Conference on Upstream Engineering and Flow Assurance

19 Asphaltene Compositional Gradient Isotherms S field P = 3500 Psia P = 4000 Psia P = 5500 Psia P = 7500 Psia Depth (ft) P = Psia P = Psia Liquid 1 + Liquid Phase Boundary Asphaltene weight % in STO Thus any field can show large or low asphaltene gradients without a need of asphaltene precipitation Panuganti, S.R. et al., Energy and Fuels, 2012; The 1st International Conference on Upstream Engineering and Flow Assurance

20 Conclusion PC-SAFT is a highly useful EoS for modeling asphaltenes. Successful capture of asphaltene PVT behavior in the upper parts of the reservoir. Evaluated reservoir connectivity through asphaltene compositional grading. Predicted tar-mat occurrence depth because of asphaltene compositional grading.

21 ADNOC Oil R&D Subcommittee EOR and FA TC. Acknowledgment Anju Kurup (BP), Jeff Creek (CVX), Jianxin Wang (CVX), Hari Subramani (CVX), Jill Buckley (NMT), Oliver Mullins (SLB), Dalia Abdullah (ADCO), Sanjay Misra (ADCO), Shahin Negahban (ADCO). PI Research Team Rice University Research Team

22 Back-Up Slides

23 Structure of Asphaltene Molecule? Modified Yen Model Mullins OC. Energy & Fuels 2010; 24(4):

24 General Background Accurate Model for Asphaltene Precipitation?? Two approaches for modeling asphaltene stability: Colloidal Model (~1930) Stability based on polar-polar interactions. Micelle formation Asphaltene particles kept in solution by resins adsorbed on them never proven Solubility Model (~1980) Asphaltenes solubilized by the oil. Resins are in the solvent fraction van der Waal s interactions (London dispersion) dominate phase behavior. (Induced molecular polarizability) Polar-polar interactions: negligible Approaches: Flory-Huggins-regular solution theory EOS recent exp findings support this approach over the colloidal model

25 Parameter Estimation Pure Component Parameters taken from the works of Gross and Sadowski. SAFT Parameters for Saturates calculated from correlations based on MW. Correlations are also available for Aromatics+Resins fraction, with an adjustable parameter called Aromaticity (γ) fit to describe their tendency to behave as a benzene derivative (γ=0) or as a PNA (γ=1). Asphaltenes parameters are fit to AOP data. Gonzalez. PhD Thesis. Rice University, 2008 Chapman, Jackson, and Gubbins, Mol. Phys. 65, 1057 (1988) Gross & Sadowski, Ind. Eng. Chem. Res., 40, (2001)

26 PC-SAFT Characterization Methodology PVT Data Fluid Characterization Model Oil Properties (gas & liquid) Tuning of PC-SAFT EoS to experimental data Plot Asphaltene Precipitation Envelope

27 Advanced EOS Modeling: Asphaltene Instability Modeling using PC-SAFT 10, % gas + 10% gas 8,000 fitted predicted Case Study: Fluid B Pressure, psia Pressure, psia 6,000 4,000 2,000-10,000 8,000 6,000 4, % gas predicted + 30% gas predicted 2, Temperature, F Temperature, F

28 Isothermal Compositional Grading Algorithm Whitson, C.H., Belery, P., SPE 28000; 1994,

29 Effect of Pressure Z C B Pressure, psia 12,000 10,000 8,000 6,000 4,000 2,000 Recombined Oil GOR = 152 m 3 /m 3 GOR = 152 m 3 /m 3 A GOR = 212 m 3 /m 3 B C GOR = 212 m 3 /m 3 Asphaltene Instability Curve Bubble Point Curve A Separator Gas, Mass Fraction Reservoir Ting, Hirasaki & Chapman. Pet. Sci. & Tech. 21, (2003)