Reservoir Fluid PVT Analysis

1. t: +44 (0) 7771 881182 e: info@petrophase.com
www.petrophase.com
Reservoir Fluid (PVT) Analysis - Value to
Appraisal / Field Development Planning
Brian Moffatt

2. PVT Information Key for all
areas of Field Development
Exploration
• Composition for economics
Appraisal
• Contaminants
• Flow Assurance
Development
• Phase Behaviour for
Reservoir Simulation
Production
• Composition monitoring
PVT Information

3. How to Maximise the Value of PVT
Information?

4. Issues from Linkedin PVT Forum Questions Value of PVT
Introduction - PVT Concerns
0 2 4 6 8 10 12 14 16 18 20
Understanding PVT Data
EOS Modelling Methods
PVT and Reservoir Behaviour
Equipment
Sampling
QC Methods
Training
Questions

5. Forum replies focus on:
• Data QC Methods
• Sampling
Introduction – PVT Concerns
0 2 4 6 8 10 12 14 16 18 20
Understanding PVT Data
EOS Modelling Methods
PVT and Reservoir Behaviour
Equipment
Sampling
QC Methods
Training
Questions Replies/Question

6. How to Maximise the Value of PVT Information?
• PVT Data QC
• Uncertainties from Sampling
• Storage Issues
• Uncertainties from PVT Lab Measurements
• Understand the Data in Context
• Modelling Key Information
• Focus on Matching Key Data
• Correct handling MWs
• Poor EOS performance for oil compressibility and viscosity
• Mapping reservoir simulation results to a surface model
• Which PVT uncertainties can most affect Development?
This Presentation

7. PVT Data QC
Maximising the Value of PVT Data

8. PVT Data QC
PVT DATA
QC
Traditional
QC
Sampling
Conditions
Well
Characteristics
Field GOR vs
Lab data
Sample
Quality
Air /OBM
Contamination
Opening
Pressures of
Samples
Sample
Compositions
Equilibrium
Plots
Data trends
Lab
Measurements
Consistency
Material
Balance
Equilibrium
Plots
Context /
Application
Agreement
with Field
Data

9. Sampling
• Bottomhole-two phase flow into sampler
• Formation tester-OBM Contamination
• Separator-Reservoir two phase flow,
Recombination GOR, Liquid Carryover
Storage
• Contaminant absorbtion
Measurement Errors
• Sample handling-loss of heavy ends from gas
samples
Where do PVT Data Errors Arise ?

10. QC for Sampling Errors
Maximising the Value of PVT Information

11. Problem Areas
• Bottomhole-two
phase flow into
sampler
• Commingled flow
from different
intervals
QC: Bottomhole Flowing Samples

12. Information Obtained
• Formation pressure and
pressure gradient (fluid type)
• Estimate formation
permeability.
• Sample compositions
Possible Problems
• Two phase flow from poor
probe contact
• OMB contamination
QC: Formation Tester

13. QC FT: OBM Contamination
• GC trend analysis: hump in the compositional analysis, especially observed in
the carbon number range of the oil based mud components (C15-C20).

14. QC FT: Poor Compositions
9950
10000
10050
10100
10150
10200
10250
10300
10350
10400
5550 5600 5650 5700
TVD
SS
ft
Pressure (psia)
Data PVT Report Oil
Use sample composition in an EOS
Analysis to compare predicted and
measured values for
• Surface GOR
• Phase Behaviour
Compare PVT Lab Densities with
Densities from Pressure Gradients

15. QC: Surface Sampling

16. 0.0
2.0
4.0
6.0
8.0
10
20
30
40
50
60
70
80
90
100
110
120
130
140
150
160
170
180
30 C
50 C
Separator
GOR is highly
dependent on
surface
conditions.
Should not
affect
recombined
fluid.
QC: Surface Sampling
Pressure, Bar
bbl/MMscf
One lean condensate at different conditions

17. 0
1,000
2,000
3,000
4,000
5,000
6,000
7,000
0 500 1000 1500 2000
GOR
Mscf/bbl
WHP psia
CGR vs Sep Press
What causes CGR scatter?
Conditions?
Wellstream?
QC: Surface Sampling

18. CO2
N2
C1
C2
C3
iC4
nC4
iC5
nC5
C6
Benz
C7
Tol
C8
0.5
1
1.5
2
2.5
3
3.5
4
4.5
-2 0 2 4 6
Log
10
(K*P)
Temp Function
Hoffmann-Hocott Equilibrium Plot
Data
Theory
QC: Surface Sampling
Equilibrium Plot
Between Surface Liquid
and Gas Compositions.
Identifies
• Liquid Carry-over
• Sample Handling
Loss of heavy ends
• Poor Temperature/
Pressure Readings
Trend for Carryover
Trend for Heavy
end Losses

19. Slide 19
What if after QC of Surface and BH samples, there are no
obvious errors but the Compositions Disagree AGAIN!
QC Data in Context: Strange GC
0.001
0.01
0.1
1
10
100
%
MOL
BHS1 BHS2 BHS3 Separator

20. Slide 20
• Initial GOR was steady at
around 8,000 scf/bbl and
samples gave a typical
Gas Condensate
behaviour
• However recombined
Separator Sample gives
Psat> Pres
• This was a low
Permeability Formation
with high drawdown
0
1,000
2,000
3,000
4,000
5,000
6,000
7,000
8,000
9,000
10,000
1 2 3 4 5 6 7 8 9 10
GOR
scf/bbl
Flow Period
Pres
QC Data in Context: Strange GC

21. Slide 21
• At lower rates and lower
drawdowns the tested
GOR reduced
• The API and Liquid
colour suggested the
fluid maybe a Volatile oil
• An EOS analysis giving a
fluid with Psat= Pres gas a
Volatile Oil with GOR
value of 2000 scf/bbl
• FLUID IS VOLATILE OIL!
0
2,000
4,000
6,000
8,000
10,000
0 2 4 6 8
GOR
scf/bbl
Gas Rate mmscf/d
QC Data in Context: Strange GC

22. QC for Sample Storage and PVT
Measurements
Maximise the Value of PVT Information

23. Pressures of Sample Bottles drop during storage due to cooling
• Wheregroups of samples available the highest pressure sample is
less likely to have suffered leakage and compositional changes
• With pressure drop can get deposition of asphaltenes/ sometimes
reversible
Contaminant absorbtion a problem in non conditioned bottles
QC:Sample Storage

24. Consistency Checks used for Common Lab Measurements
• CVD/DLMaterial Balance
• EOS Modelling for reality checks
Consistency Checks routinely carried out by PVT labs, data quality now
generally excellent. However historical data and data from unknown labs
can still have errors.
QC:PVT Lab Measurements
AT P=0, Z-factor
approachesUnity

25. Modelling Key Information
Maximising the Value of PVT Information

26. Which data do you match to? The “Best Fit” may not match
well in the are of interest, e.g. if the reservoir does not drop
below the saturation pressure
Modelling Key Information

27. PVT labs measure
volumetrics well, however
EOS can struggle with
compressibilities.
EOS models are particularly
limited in modelling near
critical fluids. Unrealistic
phase envelopes can arise.
Beware of using different
compositions in a well
matched EOS!
Modelling Key Information
0.660
0.670
0.680
0.690
0.700
0.710
0.720
0.730
0.740
0.750
5000 6000 7000 8000
Density
g/cc
Pressure(psia)
DATA
SRK
Unlikely
Critical
Behaviour

28. Conversion difficulties in transferring from reservoir modelling software to
processing modelling software!
Reservoir Engineer's Process Engineer’s
perspective perspective
Modelling Key Information

29. Matching viscosity using the LBC correlation is highly
dependent on densities.
Poor densities gives poor viscosities away from control
points, and also for the gas !
PVT Modelling Errors -Viscosities
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
0 200 400 600 800
Viscosity
cp
Rs scf/bbl
B&R
Kartoatmodjo
Kartoatmodjo HO
P&F
Liquid viscosities are not
well predicted by EOS and
so often correlations are
used. For heavy oil the
errors can be >100%.

30. PVT Modelling Errors - MW
• Samples are prepared
Gravimetrically
• Response of GC Detectors are
Proportional to Mass
– Internal standards are added by
weight
30
Increasing MW
Oil

31. PVT Modelling Errors - MW
SCN31
• Average Molecular Weight for a Fraction not Known
• Each Fraction has Complex Mix of Compounds
• Different Service Companies may use Different Sets
100
120
140
160
180
200
220
240
260
280
C9 C10 C11 C12 C13 C14 C15 C16 C17 C18 C19
Fraction
MW
Core Labs Petrobras Expro

32. • Volumetrics for economics
• Measured GORs
• Phase Behaviour for Reservoir Simulation
• Contaminants, Flow Assurance Issues
• Viscosities
• Compositions
Which PVT uncertainties data can
most affect Development?

33. PVT labs measure reservoir
condition densities to better
than 1%. Insensitive to
compositional errors from
sampling. Errors small
compared to GRV and Sw
errors.
However, surface liquid
volumes and hence STOIIP
strongly influenced by
Separator Conditions.
Volumetrics
0.0
2.0
4.0
6.0
8.0
10
20
30
40
50
60
70
80
90
100
110
120
130
140
150
160
170
180
30 C
50 C
CGR vs Sep Press

34. The GOR is often chosen for modelling from a
single recombined sample! Is this sample
consistent with the rest of the test data? Often
ignore much relevant test data.
GOR
0
1,000
2,000
3,000
4,000
5,000
6,000
7,000
0 500 1000 1500 2000
GOR
Mscf/bbl
WHP psia

35. PVT labs measure
volumetrics well, however still
a need to QC particularly old
data
Sampling errors can lead to
unrepresentative phase
behaviour.
Phase Behaviour for Reservoir Simulation
Pres

36. PVT labs measure/calculate gas viscosities to +/- 1%. Liquid
viscosities to +/- 5%. Unlikely to be important even in tight
formations as permeability errors are larger.
Matching viscosities using the LBC correlation gives values
are highly dependent on densities, poor densities gives poor
viscosities away from control points, and also for the gas !
Viscosities
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
0 200 400 600 800
Viscosity
cp
Rs scf/bbl
B&R
Kartoatmodjo
Kartoatmodjo HO
P&F
Liquid viscosities are not
well predicted by EOS and
so often correlations are
used. For heavy oil the
errors can be >100%,
beware!

37. Contaminants and flow assurance issues can lead to
costly topsides processing facilities and can constrain
export options. Huge cost implications, cf Buzzard.
Contaminants and Flow Assurance
H2S against Cumulative Gas Production
0
5
10
15
20
25
30
35
40
45
50
0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0
Cumulative gas (MMSCF)
H2S
(PPM)

38. A Surprise!

39. Contaminants and Flow Assurance
Conditions Classification by
Habitat
Compounds Possible
Biogenic
compounds formed
< 70 oC
Me3As, Hg (element) and Me2Hg, MeSH, Me2S,
Maturation
products formed <
~140 oC
CO2, H2O, H2S, R-SH, R-S-R’, R-S-S-R’
Thiophenes, tetrahydrothiophenes, benzothiophenes
(R and R’ are alkyl groups, methyl, ethyl propyl etc)
Thermally stable
products
> ~140 oC
S(vap), Hg, CO2, H2S, COS, N2, H2O (as steam or liquid)
Deeper, hotter &
high pressure

40. Conclusions
Main Problems!
• GOR measurements
•Phase behaviour from poor samples
•Poor modelling of heavy oil viscosities and compressibilities
Be aware!
•Contaminants
•Wax, scale and asphaltene deposition
•Compositions to indicate compartmentalisation
•Small heavy end compositions
Placing the PVT data in context is one of the best methods of Data
QC

41. Capture the Real Picture!

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info@petrophase.com
The End - Thank you!