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Seismic reservoir characterisation
Unconventional reservoir (shale gas)
Robert Porjesz
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Overview
Introduction
Conventional seismic modelling – Reservoir rock physics
– Seismic data for sweet spots
– Sample case history
Unconventional (azimuthal) seismic modelling – Reservoir rock physics
– Seismic data for sweet spots
– Sample case history
Integration with – Quantitative mineralogy
– Microseismic
Financial impact/concluding thoughts
3
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Overview
Introduction
Conventional seismic modelling – Reservoir rock physics
– Seismic data for sweet spots
– Sample case history
Unconventional (azimuthal) seismic modelling – Reservoir rock physics
– Seismic data for sweet spots
– Sample case history
Integration with – Quantitative mineralogy
– Microseismic
Financial impact/concluding thoughts
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Introduction
Reservoir modelling/drilling plan, operational decision making
– Without seismic
Actual data
Model data
<25%
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Introduction / objectives
Actual data
Model data
<5%
Reservoir modelling/drilling plan, operational decision making
– With seismic
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Introduction
Seismic reservoir characterisation
No seismic
With seismic
But not only seismic
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Shale Gas in the US – Extensive drilling
Since 1997, more than 13,500 gas wells completed in the Barnett shale
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0
50,000
100,000
150,000
200,000
250,000
300,000
350,000
400,000
Jan-81 Jan-83 Jan-85 Jan-87 Jan-89 Jan-91 Jan-93 Jan-95 Jan-97 Jan-99 Jan-01 Jan-03 Jan-05 Jan-07 Jan-09 Jan-11 Jan-13
Ma
xim
um
ga
s 6
mo
. p
rod
uctio
n (
MC
F)
Date
Horizontal Vertical Directional
The shale learning curve
Barnett Shale Development
Multistage
Completions
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70% of unconventional wells in the U.S.
do not reach their production targets.*
60% of all fracture stages
are ineffective.**
73% of operators say they do not know
enough about the subsurface*
*Source: Welling & Company, 2012 **Source: Hart’s E&P, 2012
Uncertainty
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Shale resources are not homogenous
Shale development is capital-intensive
Lateral wells with multi-stage completions are expensive
Economic success rates are low with current approaches
Stimulation requires a significant amount of water
We can’t drill everywhere
The need for Shale Science
DRILL / COMPLETE SMART
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Shale Play Seismic Characterization in a nutshell
Shale Characterization
How will the fractures propagate ?
Is the rock brittle ? “Brittleness”
Stress
Reservoir Quality
Gas in Place Porosity Saturation TOC
y = -0.061x + 2.6671R² = 0.6511
1.6
1.8
2.0
2.2
2.4
2.6
2.8
3.0
0 2 4 6 8 10 12 14
Bul
k D
ensi
ty (G
RI),
g/c
c
Total Organic Carbon (TOC), wt %
BASIC ROCK PROPERTIES(GRI Method)
All JIP Wells
Johnson Trust 1 #2 (Bossier)
Johnson Trust 1 #2 (Haynesville)
Johnson Trust 1 #2 (Haynesville Lime)
585 Samples
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Overview
Introduction
Conventional seismic modelling – Reservoir rock physics
– Seismic data for sweet spots
– Sample case history
Unconventional (azimuthal) seismic modelling – Reservoir rock physics
– Seismic data for sweet spots
– Sample case history
Integration with – Quantitative mineralogy
– Microseismic
Financial impact/concluding thoughts
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Unconventional reservoir rock physics
What the (seismic driven) rock physics is?
What are the benefits/objectives of applying rock physics analysis for reservoir
modelling?
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Unconventional reservoir rock physics
What the (seismic driven) rock physics is?
Kimmeridge Oil Shale Photo: Dr. Ramues Gallois (2011)
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Unconventional reservoir rock physics
What are the benefits/objectives of applying rock physics analysis for reservoir
modelling?
Kimmeridge Oil Shale Photo: Dr. Ramues Gallois (2011)
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Unconventional reservoir rock physics
What the (seismic driven) rock physics is?
http://www.kgs.ku.edu/Publications/Oil/primer03.html
Pores / Fluid
Rock Matrix
• Rock frame bulk modulus • Porosity • Fluid saturation • Temperature • Pressure • Share modulus • Density • ….
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Unconventional reservoir rock physics
What the (seismic driven) rock physics is?
vp, vs, r
• Rock frame bulk modulus • Porosity • Fluid saturation • Temperature • Pressure • Share modulus • Density • ….
Rock physics
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Unconventional reservoir rock physics
What the (seismic driven) rock physics is?
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Unconventional reservoir rock physics
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Shale Play Seismic Characterization in a nutshell
Shale Characterization
How will the fractures propagate ?
Is the rock brittle ? “Brittleness”
Stress
Reservoir Quality
Gas in Place Porosity Saturation TOC
y = -0.061x + 2.6671R² = 0.6511
1.6
1.8
2.0
2.2
2.4
2.6
2.8
3.0
0 2 4 6 8 10 12 14
Bul
k D
ensi
ty (G
RI),
g/c
c
Total Organic Carbon (TOC), wt %
BASIC ROCK PROPERTIES(GRI Method)
All JIP Wells
Johnson Trust 1 #2 (Bossier)
Johnson Trust 1 #2 (Haynesville)
Johnson Trust 1 #2 (Haynesville Lime)
585 Samples
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Pre-stack Inversion
)21(3 KEDyn
Stat
Dyn
E
Ek
2
S
P
V
V
22
2
Workflow
• Seismic Data Conditioning • Seismic Interpretation • Well – Seismic Ties • Extract Wavelets • Model Building • Inversion
r
2VP
r
sV
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Pay Probability Map – Haynesville Shale Gas
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Multi Attribute - Production map
HIGH
LOW
HIGH
LOW
HIGH
LOW
HIGH
LOW
WPTOC (mean) Lambda Rho (min)
BRITINDX (max) S-impedance (mean)
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Multi Attribute - Production map
WPTOC (mean)
Lambda Rho (min)
BRITINDX (max)
S-impedance (mean)
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NO
N-E
CO
NO
MIC
Interpolation of Production Values
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EC
ON
OM
IC
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Calibrated Production Map E
CO
NO
MIC
N
ON
-EC
ON
OM
IC
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Overview
Introduction
Conventional seismic modelling – Reservoir rock physics
– Seismic data for sweet spots
– Sample case history
Unconventional (azimuthal) seismic modelling – Reservoir rock physics
– Seismic data for sweet spots
– Sample case history
Integration with – Quantitative mineralogy
– Microseismic
Financial impact/concluding thoughts
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Shale Play Seismic Characterization in a nutshell
Shale Characterization
How will the fractures propagate ?
Is the rock brittle ? “Brittleness”
Stress
Reservoir Quality
Gas in Place Porosity Saturation TOC
y = -0.061x + 2.6671R² = 0.6511
1.6
1.8
2.0
2.2
2.4
2.6
2.8
3.0
0 2 4 6 8 10 12 14
Bul
k D
ensi
ty (G
RI),
g/c
c
Total Organic Carbon (TOC), wt %
BASIC ROCK PROPERTIES(GRI Method)
All JIP Wells
Johnson Trust 1 #2 (Bossier)
Johnson Trust 1 #2 (Haynesville)
Johnson Trust 1 #2 (Haynesville Lime)
585 Samples
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13600
13800
14000
14200
14400
14600
14800
15000
0 45 90 135 180
Imp
ed
an
ce
Azimuth
Azimuthal Impedance
P-wave Azimuthal Anisotropy
Azimuth F High
stress
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AVAz methods:
– The near offset Rüger equation
– Azimuthal Fourier coefficients
– Simultaneous elastic inversion of Fourier Coefficients
Slide 30
Fractured media characterisation
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AVAz methods:
– The near offset Rüger equation
– Azimuthal Fourier coefficients
– Simultaneous elastic inversion of Fourier Coefficients
Slide 31
Fractured media characterisation
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A popular method to perform azimuthal AVO is the near offset approximation (Rüger and Tsvankin, 1997)
Where • R(,f): Data for a given angle of incidence and azimuth f • A: Intercept • Biso: Isotropic gradient • Bani: Anisotropic gradient • fiso : Azimuth of isotropy plane
Bani is often associated to the crack density (Hudson et al., 1981) and fiso is the fracture orientation.
Slide 32
fff 22 sin]sin[),( isoaniiso BBAR
The near offset Rüger equation
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AVAz methods:
– The near offset Rüger equation
– Azimuthal Fourier coefficients
– Simultaneous elastic inversion of Fourier Coefficients
Slide 33
Fractured media characterisation
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2nd order Fourier coefficients directly
related to the anisotropic gradient Bani
4th order Fourier coefficients provide
additional fracture information
Combination of Fourier coefficients
provide fracture properties
(e.g. weaknesses, compliances) and
unambiguous fracture strike
Azimuthally invariant part contains
both isotropic and fracture properties
(Downton, SEG 2011)
Slide 34
))(4cos())(2cos(),( 420 symsympp rrrR fffff
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f1
1
2
fN
n
2
1
n 2 1 f2
n
…
1
u2 v2 u4 v4
2
u2 v2 u4 v4
n
u2 v2 u4 v4
…
Azimuthal angle stacks Azimuthal Fourier Coefficients
0 50 100 150 200 250 300 350-3
-2
-1
0
1
2
3
4
5x 10
-3
Am
pli
tud
e
Azimuth (degrees)
Amplitude vs. Azimuth (40 degrees)
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AVAz methods:
– The near offset Rüger equation
– Azimuthal Fourier coefficients
– Simultaneous elastic inversion of Fourier Coefficients
Fractured media characterisation
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SEI of azimuthal angle stacks
δT: tangential weakness
δN: normal weakness
Φsym: symmetry axis
TWT, Ip, Is, ρ
δT, δN, Φsym
Inversion minimizes a three term cost function:
f1
1
fN n
2
1
n
f2
n
… 2 1
2 (Downton and Roure, 2010)
f1 f2 fN …
Data misfit Lateral continuity Prior model
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δT: tangential weakness
δN: normal weakness
Φsym: symmetry axis
g: (Vs/Vp)^2
TWT, g
δT, δN, Φsym
Inversion minimizes a three term cost function:
1
u2 v2 u4 v4
2
u2 v2 u4 v4
n
u2 v2 u4 v4
…
θ1 θ2 θN …
Data misfit Lateral continuity Prior model
(Roure and Downton, 2012)
SEI of Fourier coefficients
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Interpretation Crossplot
Static Young’s Modulus
H- h
H
Zone 1: Ductile (RED) Zone 2: Aligned Fractures (YELLOW) Zone 3: Hydraulic Fractures (GREEN) Zone 4: Transition (GREY)
Dif
fere
nti
al H
ori
zon
tal S
tres
s R
atio
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Plate orientation: direction of H
Combining Stress & Brittleness Seismic Predictions
BRITTLE
Plate size: H
hHDHSR
Young’s Modulus
low
high Hmax
hmin
Hmax
Pressure
hmin = Closure Stress
low high
DHSR
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Predicted average production calibrated to horizontal well length
1 2
3 4
5 6 7
9 10
11
12 13 14
15 16
17
18
20
21
19
22
23 24
25 26
27 28
High
Low
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Overview
Introduction
Conventional seismic modelling – Reservoir rock physics
– Seismic data for sweet spots
– Sample case history
Unconventional (azimuthal) seismic modelling – Reservoir rock physics
– Seismic data for sweet spots
– Sample case history
Integration with – Quantitative mineralogy
– Microseismic
Financial impact/concluding thoughts
43
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Introduction
Seismic reservoir characterisation
No seismic
With seismic
But not only seismic
Quantitative mineralogy
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Cuttings Based Spectral Gamma Curve
75 µm 75 µm
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Well log and XRD lithology with up-scaled and normalized RoqSCAN® carbonate data (X’s)
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Vertical Data Summary Log
Imported logs Comments Siliciclastics &
carbonates
Provenance &
marine
indicators Porosity
data Bulk minerals Heavy
minerals
Roq
SC
AN
SG
R
Re
do
x &
org
anic
pro
xie
s
Ro
qF
RA
C
Cuttings Based Spectral Gamma Curve
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Input to seismic attribute mapping and completions characterization
1000 ft
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RoqFRAC® (‘Brittleness’ Index) vs. Dynamic Young’s Modulus
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Introduction
Seismic reservoir characterisation
No seismic
With seismic
But not only seismic
Microseismic
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Correlation between SRV and Seismic-Derived Attributes
For each stage: 1) Compute SRV 2) Compute average of seismic
attribute inside SRV
Young’s Modulus
SRV
R = 0.68 (76 stages)
DHSR
SRV
R = - 0.60 (76 stages)
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Overview
Introduction
Conventional seismic modelling – Reservoir rock physics
– Seismic data for sweet spots
– Sample case history
Unconventional (azimuthal) seismic modelling – Reservoir rock physics
– Seismic data for sweet spots
– Sample case history
Integration with – Quantitative mineralogy
– Microseismic
Financial impact/concluding thoughts
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Original 13 stages Frac design and calibrated production map
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Targeted, 9 stages Frac design and calibrated production map
Optimized locations for frac stages
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Well Economics
($10.0)
($8.0)
($6.0)
($4.0)
($2.0)
$0.0
$2.0
$4.0
$6.0
$8.0
$10.0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37
Cu
mu
lati
ve C
ash
Flo
w (
$M
M)
Month
Base Case Test Case 1 Test Case 2
Original design
13 stages
9 stage design vs
original 13 stages
Optimized
9 stage design vs
original 13
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Final comments
The shale reservoir characterization workflow demonstrated
utilizes a combination of detailed well analysis, pre-stack
seismic inversion, and seismic anisotropy.
There is no single silver bullet; multi-attribute analysis is
required.
Validation will help refine seismic processes, mineralogy and
microseismic analysis show promising correlations.
Sweet Spot maps and volumes are statistically derived.
Judicious validation should be applied.
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Thank you