Estimating Reservoir Connectivity and Tar-Mat Occurrence Using Gravity-Induced

Asphaltene Compositional Grading

Sameer Punnapala1, Sai Panuganti2, Francisco Vargas1 and Walter Chapman2

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

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):1047-1058

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 {xi}

(a) n-C5 asphaltenes (b) n-C7 asphaltenes

Buckley, J. To be published.

Outline

Introduction

PC-SAFT Asphaltene Phase Behavior Modeling Asphaltene Compositional Grading

Prediction of tar-mat occurrence

Conclusion

Accurate Model for Asphaltene Precipitation

Advanced EOS Modeling

Case Study: Fluid B, Comparison SRK Vs PC-SAFT

-

2,000

4,000

6,000

8,000

10,000

0 100 200 300 400

Temperature, °F

Pres

sure

, psi

a

+ 5% gas (fit) + 30% gas (prediction)

PC-SAFT SRK+P

SAFT Equation of State

Chapman, Jackson, and Gubbins, Mol. Phys. 65, 1057 (1988) Gross & Sadowski, Ind. Eng. Chem. Res., 40, 1244-1260 (2001)

RTA

RTA

RTA

RTA assocchainsegres ∆

+∆

+=σ m

ε/k

Gonzalez. PhD Thesis. Rice University, 2008

PC-SAFT Modeling of Asphaltene PVT Behavior

7

Tahiti Field - Black Oil, Offshore, Gulf of Mexico GOR: 510 scf/stb API: ~30o

S Field – Light Oil, Onshore, Middle East GOR: 787 scf/stb API: ~40o

Panuganti, S.R. et al., Fuel, 2012; 93:658-669

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

)(),,(),,( oi

ooii hhgMTZPTZP −+= µµ

AOP

−=

RThhgM

ffo

ioii

)(expˆˆ

Compositional Grading

Tahiti Field

PC-SAFT prediction matches the field data

Predicting Asphaltene Compositional Grading

• 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

24000

24500

25000

25500

26000

26500

27000

27500

0 0.5 1 1.5 2 2.5

Dep

th (f

t)

Optical Density (@1000 nm) PC-SAFT (M21B)

Field Data (M21B)

PC-SAFT (M21A Central)

Field Data (M21A Central)

PC-SAFT (M21A North)

Field Data (M21A North)

Tahiti Field

Approximate Analytical Solution

)()()()(ln 12

2

1 hhRT

gVMhh ii

i

i −−

=ρ

ρρ

ρi= Molar density; h=Depth; = Partial Molar Volume; ρ=Mass density; Mi = Molecular Weight 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

iV

Sage, B. H.; Lacey, W. N. Los Angeles Meeting, AIME; October 1938

Approximate Analytical Solution

• Broken lines are the analytical solution predictions

• Analytical solution can be used for sensitivity analysis and approximate estimate

24000

24500

25000

25500

26000

26500

27000

27500

0 0.5 1 1.5 2 2.5

Dep

th (f

t)

Optical Density (@1000 nm) PC-SAFT (M21B)

Analytical Solution (M21B)

Field Data (M21B)

PC-SAFT (M21A Central)

Analytical Solution (M21ACentral)Field Data (M21A Central)

PC-SAFT (M21A North)

Analytical Solution (M21A North)

Field Data (M21A North)

Field Data (M21A South)

Tahiti Field

PC-SAFT Asphaltene Compositional Grading

24000

26000

28000

30000

32000

34000

36000

2 7 12

Dep

th (f

t)

Asphaltene Weight % in STO

Reference Depth

• PC-SAFT asphaltene compositional grading extended to further depths

• Field observations did not report any tar mat

Predicting Asphaltene Compositional Grading

7500

7700

7900

8100

0.5 0.7 0.9 1.1 1.3 1.5

Dep

th (f

t)

Dimensionless Optical Density (OD/ODo)

Zone A1

Zone B1

Field Data

Well Z

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

S Field

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

Predicting Tar-mat Occurrence

7800

8100

8400

8700

9000

0 10 20 30 40 50 60

Dep

th (f

t)

Asphaltene weight percentage in STO

Crude-Tar Transition

Zone 1

Zone 2 Zone 3

Panuganti, S.R. et al., Energy and Fuels, 2011; dx.doi.org/10.1021/ef201280d

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

Tar-mat Analysis

7800

8100

8400

8700

9000

0 10 20 30 40 50 60

Dep

th (f

t)

Asphaltene Weight % in STO 24000

26000

28000

30000

32000

34000

36000

2 7 12

Dep

th (f

t)

Asphaltene Weight % in STO

S field T field

Can the T field have an S field situation and vice versa ?

Asphaltene Compositional Gradient Isotherms

Thus any field can show large or low asphaltene gradients without a need of asphaltene precipitation

7800

8800

9800

10800

11800

12800

0 10 20 30 40 50 60 70 80 90

Dep

th (f

t)

Asphaltene weight % in STO

P = 3500 PsiaP = 4000 PsiaP = 5500 PsiaP = 7500 PsiaP = 10000 PsiaP = 15000 PsiaPhase Boundary

Panuganti, S.R. et al., Energy and Fuels, 2012; The 1st International Conference on Upstream Engineering and Flow Assurance

Liquid 1 + Liquid 2 S

field

Asphaltene Compositional Gradient Isotherms

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

S field

7800

8800

9800

10800

11800

12800

0102030405060708090

Dep

th (f

t)

Asphaltene weight % in STO

P = 3500 Psia

P = 4000 Psia

P = 5500 Psia

P = 7500 Psia

P = 10000 Psia

P = 15000 Psia

Phase Boundary

Liquid 1 + Liquid 2

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.

Acknowledgment ADNOC Oil R&D Subcommittee

EOR and FA TC.

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

Back-Up Slides

Structure of Asphaltene Molecule?

Modified Yen Model

Mullins OC. Energy & Fuels 2010; 24(4):2179-2207

Accurate Model for Asphaltene Precipitation ??

Colloidal Model (~1930)

Stability based on polar-polar interactions.

• Micelle formation

• Asphaltene particles kept in solution by resins adsorbed on them

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

Two approaches for modeling asphaltene

stability:

never proven recent exp findings support this approach over the colloidal model

General Background

Parameter Estimation

Gonzalez. PhD Thesis. Rice University, 2008 Chapman, Jackson, and Gubbins, Mol. Phys. 65, 1057 (1988)

Gross & Sadowski, Ind. Eng. Chem. Res., 40, 1244-1260 (2001)

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.

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

Modeling using PC-SAFT

Advanced EOS Modeling: Asphaltene Instability

-

2,000

4,000

6,000

8,000

10,000

Pres

sure

, psi

a

-

2,000

4,000

6,000

8,000

10,000

Pres

sure

, psi

a

0 100 200 300 400

Temperature, °F

+ 10% gas

100 200 300 400

Temperature, °F

predicted + 5% gas

fitted

+ 15% gas predicted

+ 30% gas predicted

Cas

e St

udy:

Flu

id B

Isothermal Compositional Grading Algorithm

Whitson, C.H., Belery, P., SPE 28000; 1994, 443-459

Effect of Pressure

Z

Reservoir

0

2,000

4,000

6,000

8,000

10,000

12,000

0.10 0.12 0.14 0.16 0.18 0.20 0.22 0.24 0.26

Separator Gas, Mass Fraction

Pres

sure

, psi

aGOR = 152 m3/m3

Bubble Point Curve

Asphaltene Instability Curve

Recombined Oil

GOR = 212 m3/m3

GOR = 212 m3/m3

GOR = 152 m3/m3

A

A

C

C B

B

Ting, Hirasaki & Chapman. Pet. Sci. & Tech. 21, 647 – 661 (2003)