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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
Society of Petroleum Engineers
Distinguished Lecturer Programwww.spe.org/dl
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Tao YangStatoil ASA
Estimation of Shale Gas and Oil Properties Based on Field Data
Outline
• Fluid properties (PVT) for conventional reservoirs
• Challenges to obtain shale gas and oil properties
• Can we estimate shale gas and oil properties based on
field data?
• Application examples
• Conclusions
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Fluid properties (PVT) for conventional reservoirs
• Empirical correlations
– Standing correlation from 1940s
• Standard/modern approach
– Sampling, laboratory measurement and Equation of
State (EOS) modeling
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Surface Gas
Surface OilLive Oil
Challenges to obtain shale fluid properties
• Difficult to take in-situ
representative samples
• Very little published PVT
data available
• Uncertainty of geochemical
basin modeling
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Cander, H., 2012
GOR - Gas oil ratio
Review of recent activities
• In-situ representative samples
– Pressure coring
– History matching of production data
• Impact of Nano-sized porous media on PVT properties
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Can we estimate shale gas and oil properties
based on field data?
• Most productions have no PVT samples, no PVT
measurements, but there are some field data available.
• For shale play evaluation, drilling target and well
completion optimization, timely decision has to be made
before detailed fluid data are available.
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A large PVT program for Eagle Ford Shale
• Designed for understanding fluid properties in shale play
• Highly undersaturated reservoir fluids range from dry gas, gas
condensate, volatile oil to black oil
• Surface recombined samples from early production
• 50+ wells covering large areas
• Fluid sampling and PVT laboratory measurement over two years
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Unique findings for shale fluid properties
• Consistent PVT correlations field-wise even though the
reservoir fluids cover very wide range.
• Most fluid properties can be estimated from only one
parameter – GOR.
• What made shale reservoir fluid properties unique?
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Simplified petroleum alteration
process in shale reservoirs
• Extremely low permeability limits fluid communication
and migration.
• Reservoir fluid differences are dominantly determined by
thermal maturity.
• Reservoir fluid system in shale reservoir is a closed
system at chemical equilibrium.
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New method for fluid property estimation
• Closed system in shale reservoir makes a simplification
of fluid property estimation possible.
• Reservoir fluid composition and PVT properties can be
estimated based on correlations with GOR.
• PVT correlations are fluid system (field/play) dependent.
• Supported by fluid property data and field operations
from many US shale plays.
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How to obtain GOR?
• Before production
– Mud logging gas composition
• Production phase
– GOR data
– API gravity of surface oil/condensate (stock tank oil)
– Surface gas composition
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Flowchart for estimating shale gas
and oil properties
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Automatically updated fluid
property correlations
GOR
API gravity
Surface gas ormud logging gascomposition
Reservoir fluid composition
Black oil properties
Natural gas liquids
One of the field data input
All outputs
Applications
• Simplification of fluid property estimation for shale
reservoirs is a small step but opens the possibility of
broad practical applications.
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Drilling and completion Production
Mud logging gas composition
GORAPI gravitySurface gas composition
Example 1: Production optimization
• Surface gas analysis is available for all wells.
• Fluid properties from each well are required input for
production optimization (e.g. network modeling).
• Estimating PVT properties from surface gas composition is a
practical approach.
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28Surface gas Well stream
Mole%
N2 Nitrogen 0.06
CO2 Carbon Dioxide 1.81
C1 Methane 79.27
C2 Ethane 10.59
C3 Propane 3.60
iC4 i-Butane 0.98
nC4 n-Butane 1.07
iC5 i-Pentane 0.49
nC5 n-Pentane 0.28
C6 Hexane 0.95
C7+ Heptane Plus 0.90
Components
Surface Gas Composition Mole%
Molecular
Weight
(kg/kmol)
(Apparent)
Gravity
N2 Nitrogen 0.07 28.0 0.808
CO2 Carbon Dioxide 1.59 44.0 0.827
C1 Methane 74.20 16.0 0.300
C2 Ethane 10.97 30.1 0.356
C3 Propane 4.12 44.1 0.508
iC4 i-Butane 1.25 58.1 0.563
nC4 n-Butane 1.53 58.1 0.584
iC5 i-Pentane 0.83 72.2 0.625
nC5 n-Pentane 0.62 72.2 0.631
C6 Hexane 0.92 86.2 0.664
C7+ Heptane Plus 3.88 134.8 0.775
Components
Estimated Well Stream
Saturation Pressure (psia) 3848
Oil or Condensate Gravity (°API) 58.8
Gas Oil Ratio (scf/stb) 14640
Formation Volume Factor (rcf/scf) Wet 0.0031487
Dry 0.0033273
Estimated PVT Properties
Yang et al., 2014
Example 2: Completion optimization
• Fixed completion design often leads to low efficiency for
liquid rich shale production.
• Mud logging gas composition can be used to make real time
interpretation of in-situ reservoir fluid properties while
drilling.
• The timely PVT input provides important inputs to optimize
completion design.
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Shale play evaluation
• For shale play evaluation, condensate gas ratio (liquid yield) is
most critical parameter.
• For most early phase evaluations, such data is not available.
• It is a robust approach to integrate geochemical prediction and
PVT estimation based on field data.
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• Low stock tank oil (STO) API gravity
• Basin modeling indicates both gas and oil in
the play.
• Geochemical arguments for gas or oil cases
are not conclusive for target area.
Gas or oil prospect?
Yang et al., 2014
Conclusions
• Thanks to low permeability in shale reservoir,
closed system makes a simplification of fluid
property estimation possible.
• Wide range of field data can be used to estimate
shale gas and oil properties with reasonable
accuracy.
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Acknowledgements
• Statoil and Talisman (Repsol) management
• Colleagues from Statoil shale assets (USA) and technology central (Norway)
– Remy Basquet, Lisset Sousa
– Angel Callejon, Jan Joost Van Roosmalen, Bob Bartusiak
– Knut K. Meisingset, Timothy P. Isom, Svein Tollefsen, Inge Brandsæter
• Peers nominated and supported me for SPE distinguished lecturer program
– Karen S. Pedersen (Calsep), Julian Y. Zuo (Schlumberger)
• Special thanks for kind help to improve the presentaion
– Sunil Kokal (Saudi Aramco), Stephen Begg (University of Adelaide)
– Curtis H. Whitson (NTNU)
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