spe distinguished lectured

8/17/2019 SPE Distinguished Lectured

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Birol Dindoruk

Reservoir Fluid Properties (PVT):

Issues, Pitfalls and Modeling Aspects

Shell International Exp. & Prod. Inc.

Society of Petroleum Engineers

Distinguished Lecturer Programwww.spe.org/dl


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Outline

Purpose/Motivation• Impact (Examples)

– Well Testing

– Surface Oil Volume, Reservoir Depletion Performance• Sources of PVT data

– Main Focus Areas

• QC Considerations/Modeling Issues – Measurement errors/Sample consistency

– Rules-of-thumb/Difficult Fluids

– OBM

– Compositional Grading/Multiple PVT’s

– Viscosity

– EOR

• Summary


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Why Do We Need PVT Data?

• Many petroleum engineering calculations

require PVT data: – Reserves, reservoir connectivity – Reservoir simulation/Material balance

– Pressure transient testing – EOR/Injection processes

– Flow-line, wellbore hydraulics calculations

– Flow assurance – Production allocation and calibration

– Tax implications/qualifications/quotas

– Production Sharing Agreements (PSA’s) – Drilling and completion fluids


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Example(s): Well testing equation(s), MBE

Bottom Line: Most of the equations that we use

have coefficients/parameters that are functions of

fluid properties.

oo Bmh

qk 6.162


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From reservoir to surface –Pressure, Volume and Temperature changes

Surface

Oil Reservoir

GOR behavior, Boi

G

OO

PVT

Description


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0

5000

10000

15000

20000

0 200 400 600 800 1000 1200 1400 1600

CUMULATIVE OIL PRODUCTION (MSTB)

G O R ( S C F / S T B )

0

1000

2000

3000

4000

GOR (SCF/STB) Pressure (psia)

From Craft & Hawkins

Reservoir Performance/Time Dependent Behavior

Pbp

0.0

0.4

0.8

1.2

1.6

2.0

0 500 1000 1500 2000 2500 3000 3500 400

Pressure (psia)

O i l V i s c o s i t y ( c p )


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Sources of PVT data

• PVT Experiments/Measurements(need fluid samples)

– Surface/Subsurface Samples

• Correlations/Analog Data

• Equation of State (EOS)

representation (i.e., cubic)

Estimation/Calculation of

PVT Properties

Sutton (2005)

22

2 bbV V

a

bV

RT P


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What Happens From Reservoir to

Separators?

Plants, etc.

Surface

FacilityModeling

Reservoir/ Process

Modeling

WellboreSimulation


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RE: Main Focus Areas

• Primary and Secondary Production

– Typically fluid properties/depletioncharacteristics from reservoir to separators

• Interaction with non-native (i.e., EOR)

fluids – Experiments/Modeling to capture EOR

processes (i.e., IFT 0)

• Modeling the desired processes (“EOSwork”)


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Some Aspects of QC Considerations

• Fluid Type• Data Quality

– Sample

– Lab Data

• Minimum Data Requirements

• Transport Properties (Viscosity)

• EOS vs Data


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P-T Diagrams/Phase Envelope

70%

50%

20%

90%

10%

“GAS”

Pdp

2

1

Tsep&Psep

CP

PiPi

“OIL”

100% L

T

1=wet gas

2=dry gas

P

0.0

0.1

0.2

0.3

0.4

0.5

36 37 38 39 40 41 42 43 44

TIME (hr)

I n s t a n t a n e o u s G O

R ( M S C F / S e p B B L )

0

10

20

30

40

50

60

70

80

0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000

P (psia)

L i q % @ T r e s

Liq % (Data)

Liq % (Calc)


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Classification of Reservoir Fluids

– “Cut off”/”rule of thumb” (i.e., Mc Cain)

– P-T diagramsProperty Black

Oil

Volatile

Oil

Retrograd

e Gas

Wet Gas Dry Gas

Initial GOR

(SCF/STB)

3200 >15000

(100,000

(40 20% 20-

12.5%


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80

100

120

140

160

180

80 100 120 140 160 180 200 220

Separator Temperature (F)

C G R ( S T B

/ M M S C F )

#1#2Extended Flow1st Stage CGR: Psep = 389.7 psia (139.3 STB/MMSCF)1st Stage CGR: Psep =389.7 psia (119.5 STB/MMSCF)

1st Stage CGR: Psep =550 psia (119.5 STB/MMSCF)

Impact of Test Separator Conditions on CGR

10000


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PCP

Pbp

T

P1 & T1

TresT1

P1 & T1

Low-T Extrapolation

Pres & Tres

Pres & Tres

0

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000

-200 0 200 400 600 800 1000 1200 1400

T (F)

P ( p s i a )

DATA

CRIT


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Oil Base Mud (OBM) Contamination

• Specially designed HC/Oil-Base Fluids

• Pose challenges to get clean samples

0.01

10.01

20.01

30.01

40.01

50.01

7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30

Carbon Number

W e i g h t %

A c c e p t a b l e

C o

n t ami n a t i on

Black OilDry Gas

10%

A c c e p t a b l e

C o

n t ami n a t i on

Black OilDry Gas

10%

?


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Oil Base Mud Contamination: Condensate

0

10

20

30

0 2000 4000 6000 8000 10000 12000 14000

Pressure (psia)

L i q u i d

V o l u m e ( % )

Liq %_exp (199 F) -- CONTAMINATED

Liq %_cpk (199 F) -- CONTAMINATED

Liq %_cpk (199 F) -- UNCONTAMINATED

0

10

20

30

0 2000 4000 6000 8000 10000 12000 14000

Pressure (psia)

L i q u i d

V o l u m e ( % )

Liq %_exp (199 F) -- CONTAMINATED

Liq %_cpk (199 F) -- CONTAMINATED

Liq %_cpk (199 F) -- UNCONTAMINATED0.01

10.01

20.01

30.01

40.01

50.01

7 8 9 101112131415161718192021222324252627282930

Carbon Number

W e i g h t %


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Compositional Grading

• Compositional Grading

– Equilibrium

– Non-equilibrium

• No data = No problem (“no

brain & no headache”)

Depth

Detailed review is in SPE109284

Enabling Technologies:

Advances in Subsurface Sampling

Techniques

Anshultz Ranch SPE14412, As described by Metcalfe et al.

r1

r1

r4

r3

r2

SPE116243 & 124264


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Compositional Grading: GOR versus DepthCompositional Grading: GOR versus Depth

9000

9500

10000

10500

11000

11500

0 10000 20000 30000

GOR (SCF/STB)

D e p t h

( f t )

GOC

0

500

1000

1500

2000

2500

3000

3500

4000

4500

-200 0 200 400 600 800 1000T (F)

P ( p s i a )

Critical Point

Black OilGas

Pres

Tres

GAS

OIL


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9000

9500

10000

10500

11000

11500

12000

12500

13000

13500

14000

7500 8500 9500 10500 11500 12500 13500

Pressure/Saturation Pressure (psia)

D e p t h ( f e e t )

OIL/LIQUID

CONDENSATE/VAPOR


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• Inferred quantity (transport property)

• Leading Industrial Measurement Techniques – Electromagnetic Viscosity Measurement

– Rolling Ball Techniques – Capillary Tube

– Fann-Type Devices

Liquid Phase Viscosity

(Measurement Aspects)


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Liquid Phase Viscosity

(Computational Aspects)

• Heavy ends have the largest impact on liquidviscosity

• Better characterization of the plus fractions

can improve the results significantly:granularity matters!

• Viscosity Models

– Lohrenz-Bray-Clark/Jossi et al. Model – Corresponding States models

– Friction models

– Black oil correlations


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EOR Aspects

Dependence of residual oil saturation to

capillary number

u N Ca


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Impact of Temperature: Viscosity

Farouq Ali (1982) SPE 9897


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EOS The Final “Assembly” Step:

• Limitations inherent to two-constant cubic

EOS (Mainly Peng and Robinson EOS andSoave modified Redlich and Kwong EOS) – Semi-empirical nature of the EOS

– Volume prediction

– Mixing rules

– Having a fixed critical Z-factor for all the

components, etc.• Inexact fluid description (Single Carbon

Number grouping rather than detailed

compositional breakdown)

PREDICTIVE CAPABILITY ISSUES:


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Summary

• Proper PVT data/work is needed to capture – Depletion performance of the reservoir and

– Interaction of injectants and the in-situ fluids

• Consistent fluid description is needed from thereservoir to the delivery point.

• “Difficult fluids” (near-critical systems, heavyfluids, contaminated fluids, lean condensates,graded systems) pose challenges – Characterization/modeling aspects

– Computational aspects

– Initialization aspects

– Measurement aspects


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Summary

• PVT/Fluid Properties should be used to

complement the G&G information

• EOS/Computational Aspects: – QC of the data is a must

– Better viscosity prediction/modeling is needed

– Sample characterization/representation with minimum

# of components

– Multiple (PVT’s) sample characterizations poses a

challenge


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QUESTIONS ?

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