SPE Distinguished Lecturer · PDF fileSPE Distinguished Lecturer Program Primary funding is...

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SPE Distinguished Lecturer Program

Primary funding is provided by

The SPE Foundation through member donations and a contribution from Offshore Europe

The Society is grateful to those companies that allow their professionals to serve as lecturers

Additional support provided by AIME

Society of Petroleum Engineers Distinguished Lecturer Programwww.spe.org/dl

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

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

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

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.

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From reservoir to surface –Pressure, Volume and Temperature changes

Surface

Oil Reservoir

GOR behavior, Boi

PVTDescription

0 200 400 600 800 1000 1200 1400 1600CUMULATIVE OIL PRODUCTION (MSTB)

GOR (SCF/STB) Pressure (psia)

From Craft & Hawkins

Reservoir Performance/Time Dependent Behavior

0 500 1000 1500 2000 2500 3000 3500 4000Pressure (psia)

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)

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What Happens From Reservoir to Separators?

Plants, etc.

SurfaceFacilityModeling

Reservoir/ ProcessModeling

WellboreSimulation

RE: Main Focus Areas

• Primary and Secondary Production– Typically fluid properties/depletion

characteristics 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 (“EOS

work”)

Some Aspects of QC Considerations

• Fluid Type• Data Quality

– Sample– Lab Data

• Minimum Data Requirements• Transport Properties (Viscosity)• EOS vs Data

P-T Diagrams/Phase Envelope

“GAS”

Tsep&Psep

“OIL”

100% L

1=wet gas2=dry gas

36 37 38 39 40 41 42 43 44TIME (hr)

0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000P (psia)

Liq % (Data)Liq % (Calc)

Classification of Reservoir Fluids

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

Property BlackOil

VolatileOil

Retrograde Gas

Wet Gas Dry Gas

Initial GOR(SCF/STB)

<1750 1750-3200

>3200 >15000(<66bc/mmcf)

>100,000(<10bc/mmcf)

Initial Stock Oil, oAPI

<45 >40 >40 <70 None

C7+ >20% 20-12.5%

<12.5% <4% <0.7%

80 100 120 140 160 180 200 220Separator Temperature (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

P1 & T1

TresT1

P1 & T1

Low-T Extrapolation

Pres & Tres

-200 0 200 400 600 800 1000 1200 1400T (F)

DATACRIT

Oil Base Mud (OBM) Contamination

• Specially designed HC/Oil-Base Fluids• Pose challenges to get clean samples

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

Acceptable C

ontamination

Black OilDry Gas

cceptable Contam

ination

Black OilDry Gas

Influence of Drilling Mud/Treatments[Contamination]

1 2 3 4 5 6 7 80

S ample Numbe r

Mas s fraction at S andface

Reservoir-1.0 0.1

-1.0 0.1

-0.80-0.70

-0.60-0.50

-0.40-0.30

-0.20-0.10

0.000.10

0.200.30

0.400.50

0.00 2.50 5.00 inches

Oil Base Mud Contamination: Condensate

0 2000 4000 6000 8000 10000 12000 14000Pressure (psia)

Liq %_exp (199 F) -- CONTAMINATEDLiq %_cpk (199 F) -- CONTAMINATEDLiq %_cpk (199 F) -- UNCONTAMINATED

0 2000 4000 6000 8000 10000 12000 14000Pressure (psia)

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

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

0 10 20 30 40 50% Oil Base Mud (w/w)

a)Stock Tank Oil

Reservoir Fluid

Oil Base Mud Contamination: Oil

Compositional Grading• Compositional Grading

– Equilibrium– Non-equilibrium

• No data = No problem (“no brain & no headache”)

Detailed review is in SPE109284

Enabling Technologies: Advances in Subsurface Sampling Techniques

Anshultz Ranch SPE14412, As described by Metcalfe et al.

SPE116243 & 124264

Compositional Grading: GOR versus DepthCompositional Grading: GOR versus Depth

0 10000 20000 30000

GOR (SCF/STB)

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

Critical Point

Black OilGas

7500 8500 9500 10500 11500 12500 13500Pressure/Saturation Pressure (psia)

OIL/LIQUID

CONDENSATE/VAPOR

• Inferred quantity (transport property)• Leading Industrial Measurement Techniques

– Electromagnetic Viscosity Measurement– Rolling Ball Techniques– Capillary Tube– Fann-Type Devices

Liquid Phase Viscosity (Measurement Aspects)

Liquid Phase Viscosity (Computational Aspects)

• Heavy ends have the largest impact on liquid viscosity

• 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

25Stalkup

EOR Aspects

Dependence of residual oil saturation to capillary number

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Impact of Temperature: Viscosity

Farouq Ali (1982) SPE 9897

EOS The Final “Assembly” Step:

• Limitations inherent to two-constant cubic EOS (Mainly Peng and Robinson EOS and Soave 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:

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 the reservoir to the delivery point.

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

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

QUESTIONS ?

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