Seismic Attribute Mapping of Structure and Stratigraphy Kurt J. Marfurt (University of Oklahoma)
Geometric Attributes
5-1
Course Outline Introduction Complex Trace, Horizon, and Formation Attributes Multiattribute Display Spectral Decomposition Geometric Attributes Attribute Expression of Geology Impact of Acquisition and Processing on Attributes Attributes Applied to Offset- and Azimuth-Limited Volumes Structure-Oriented Filtering and Image Enhancement Inversion for Acoustic and Elastic Impedance Multiattribute Analysis Tools Reservoir Characterization Workflows 3D Texture Analysis 5-2
Volumetric dip and azimuth After this section you will be able to: • Evaluate alternative algorithms to calculate volumetric dip and azimuth in terms of accuracy and lateral resolution, • Interpret shaded relief and apparent dip images to delineate subtle structural features, and • Apply composite dip/azimuth/seismic images to determine how a given reflector dips in and out of the plane of view.
5-3
Definition of reflector dip z
θ (dip magnitude) φ (dip azimuth) n a θx (inline dip)
x 5-4
θy (crossline dip)
ψ (strike)
y
(Marfurt, 2006)
Alternative volumetric measures of reflector dip 1. 3-D Complex trace analysis 2. Gradient Structure Tensor (GST) 3. Discrete scans for dip of most coherent reflector
5-5
•
Cross correlation
•
Semblance (variance)
•
Eigenstructure (principal components)
1. 3-D Complex Trace Analysis (Instantaneous Dip/Azimuth) Instantaneous phase Instantaneous frequency
Instantaneous in line wavenumber Instantaneous cross line wavenumber Instantaneous apparent dips 5-6
φ = tan
−1
(d
Hilbert transform
H
/d)
∂H d ∂d d − d ∂φ t t ∂ ∂ = 2π ω = 2π f = 2π 2 ∂t d2 + dH
( )
kx
∂H d ∂d d − d ∂φ x x ∂ ∂ = = 2 ∂x d2 + dH
ky =
p =
H
( )
∂φ = ∂y
kx
ω
∂H d ∂d d − d ∂y ∂y
;q =
( )
d2 + d ky
ω
H 2
H
H
Seismic data 7.5 km 1500
Amp
Depth (m)
pos
0
neg
4500 Vertical slice
5-7
Depth slice
(Barnes, 2000)
Instantaneous Dip Magnitude 7.5 km 1500
Depth (m)
Dip (deg) high
0
4500 Vertical slice
5-8
Depth slice
(Barnes, 2000)
The analytic trace Quadrature Original (imaginary data (real component component) )
Envelope
2
H
2
e(t) = {[d(t)] + [d (t)] }
1/2
d(t)
H
d (t)
Phase
+180 -1
H
φ(t) = tan [d (t)/d(t)]
0
Frequency
-180
5-9
Weighted average frequency
f(t) = dφ(t) /dt
(Taner et al, 1979)
Instantaneous Dip Magnitude (sensitive to errors in ω, kx and ky!) 7.5 km 1500
Depth (m)
Dip (deg) high
0
4500 Vertical slice
5-10
Depth slice
(Barnes, 2000)
Weighted average dip (5 crossline by 5 inline by 7 sample window) 7.5 km 1500
Depth (m)
Dip (deg) high
0
4500 Vertical slice
5-11
Depth slice
(Barnes, 2000)
Instantaneous Azimuth 7.5 km 1500
Depth (m)
Azim (deg) 360
180
0
4500 Vertical slice
5-12
Depth slice
(Barnes, 2000)
Weighted average azimuth (5 crossline by 5 inline by 7 sample window) 7.5 km 1500
Depth (m)
Azim (deg) 360
180
0
4500 Vertical slice
5-13
Depth slice
(Barnes, 2000)
2. Gradient Structure Tensor (GST)
TGS
=
∂u ∂x ∂u ∂x ∂u ∂x
∂u ∂x ∂u ∂y ∂u ∂z
∂u ∂y ∂u ∂y ∂u ∂y
∂u ∂x ∂u ∂y ∂u ∂z
∂u ∂z ∂u ∂z ∂u ∂z
∂u ∂x ∂u ∂y ∂u ∂z
The eigenvector of the TGS matrix points in the direction of the maximum amplitude gradient
5-14
(Bakker et al, 2003)
3. Discrete scans for dip of most coherent reflector Minimum dip tested (-200)
Dip with maximum coherence (+50)
Analysis Point
Maximum dip tested (+200)
Instantaneous dip = dip with highest coherence 5-15
(Marfurt et al, 1998)
3-D estimate of coherence and dip/azimuth
5-16
(Marfurt et al, 1998)
Searching for dip in the presence of faults
Time (s)
Amp pos
C
L
0
R neg
Single window search
5-17
Multi-window search
(Marfurt, 2006)
inline
Search for the most coherent window containing the analysis point
crossline 5-18
(Marfurt, 2006)
Search for the most coherent window containing the analysis point
Time (s)
crossline
5-19
(Marfurt, 2006)
A′
0 A 2 km
A
A′ Comparison of dip estimates on vertical slice
Time (s)
1 2 3 4 0
seismic
inst. Inline dip
dip (µs/m) +.2
Time (s)
1 2
0 3
5-20
4
smoothed inst. Inline dip
multi-window inline dip scan
-.2 (Marfurt, 2006)
2 km Comparison of dip estimates on time slice (t=1.0 s) A′′
A seismic
inst.dip
dip (µs/m) +.2 0 -.2
5-21
smoothed inst. dip
multi-window dip scan
(Marfurt, 2006)
Vertical Slice through Seismic 5 km 0.25
B
B′′
Amp neg
0.50 0
0.75 Time (s)
pos
1.00
Caddo
1.25 Ellenburger 1.50 Basement? 1.75 5-22
Time/structure of Caddo horizon 5 km
B′′ Time (s) 0.70 0.75
0.80 0.85
B 5-23
Dip magnitude from picked horizon 5 km
B′′ Dip (s/km) 0.00
0.06
5-24
B
5 km
NS dip from picked horizon B′′
Dip (s/km) +0.06
0.00
-0.06
5-25
B
NS dip from multi-window scan 5 km
B′′
Dip (deg) +2
0
-2
5-26
B
5 km
EW dip from picked horizon B′′
Dip (s/km) +0.06
0.00
-0.06
5-27
B
EW dip from multi-window scan 5 km
B′′
Dip (deg) +2
0
-2
5-28
B
Shaded illumination
on a surface
5-29
on a time slice through dip and azimuth volumes
(after Barnes, 2002)
Time slices through apparent dip (t=0.8s) Dip (deg) +2
0
-2
000 =30 ϕ=150 =0 =60 =90 =120 5-30
Time slices through apparent dip (t=1.2s)
Dip (deg) +2
0
-2
0 0 00 ϕϕ=150 =30 =60 =90 =0 =120 5-31
Volumetric visualization of reflector dip and azimuth Dip Azimuth Hue
N
0
180
360
Dip Magnitude Saturation
High
0 N
(s) e Tim
1.2 E
W 1.4
S (c)
5-32
Volumetric visualization of reflector dip and azimuth Dip Azimuth Hue
1 .2 s) Time (
0
180
360
Dip Magnitude Saturation
High
1. 4
Transparent 0 N N
W
Transparent
S
5-33
E
0.0
divergent
t (s)
Towards 3-D seismic stratigraphy…
convergent
divergent 1.5
convergent
5-34
(Barnes, 2002)
Volumetric Dip and Azimuth In Summary: • Dip and azimuth cubes only show relative changes in dip and azimuth, since we do not in general have an accurate time to depth conversion • Dip and azimuth estimated using a vertical window in general provide more robust estimates than those based on picked horizons • Dip and azimuth volumes form the basis for volumetric curvature, coherence, amplitude gradients, seismic textures, and structurally-oriented filtering • Dip and azimuth will be one of the key components for future computeraided 3-D seismic stratigraphy
5-35
Coherence After this section you will be able to: • Summarize the physical and mathematical basis of currently available seismic coherence algorithms, • Evaluate the impact of spatial and temporal analysis window size on the resolution of geologic features, • Recognize artifacts due to structural leakage and seismic zero crossings, and • Apply best practices for structural and stratigraphic interpretation.
5-36
Seismic Time Slice
5 km
5-37
(Bahorich and Farmer, 1995)
Coherence Time Slice
salt
5-38
5 km
(Bahorich and Farmer, 1995)
cr
cr
os s
os s
lin e
lin e
Coherence compares the waveforms of neighboring traces
inline
5-39
inline
Cross correlation of 2 traces Trace #1 lag:
Shifted windows of Trace #2 -4
-2
0 +2
+4
Cross correlation
40 ms
Maximum coherence
5-40
AAA high
Time slice through average absolute amplitude
0
coh
high
Time slice through coherence (early algorithm)
low
5-41
(Bahorich and Farmer, 1995)
A
A′′
Vertical slice through seismic
amp pos
0
neg
A coh
high
Time slice through coherence (later algorithm)
low
5-42
A′′
(Haskell et al. 1995)
Appearance faults perpendicular and parallel to strike N
3 km
5-43
coherence
seismic
Alternative measures of waveform similarity • cross correlation • semblance, variance, and Manhattan distance • eigenstructure • Gradient Structural Tensors (GST)
5-44
Semblance estimate of coherence
Analysis window
energy of average traces 5. coherence≡ ≡ energy of input traces 1. Calculate energy of input traces 2. Calculate the average wavelet within the analysis window.
t-K∆t dip
3. Estimate coherent traces by their average 4. Calculate energy of average traces 5-45
t+K∆t
The ‘Manhattan Distance’: r=|x-x0|+|y-y0|
5-46
The ‘as the crow flies’ (or Pythagorian) distance’ r=[(x-x0)2+(y-y0)2]/1/2
New York City Archives
Pitfall: Banding artifacts near zero crossings
8 ms
5-47
Solution: calculate coherence on the analytic trace
Coherence of real trace 5-48
Coherence of analytic trace
Eigenstructure estimate of coherence energy of coherent compt 5. coherence ≡ energy of input traces
Analysis window
2. Calculate the wavelet that 1. Calculate energy of input traces best fits the data within the analysis window.
t-K∆t dip
3. Estimate coherent compt of traces 4. Calculate energy of coherent compt of traces 5-49
t+K∆t
Eigenstructure coherence: Time slice through seismic
5-50
Eigenstructure coherence: Time slice through total energy in 9 trace, 40 ms window
scour
salt
5-51
Eigenstructure coherence: Time slice through coherent energy in 9 trace, 40 ms window
scour
salt
5-52
Eigenstructure coherence: Time slice through ratio of coherent to total energy
faults scour
salt
5-53
Coherence algorithm evolution
Seismic
Crosscorrelation Canyon
Salt
Channels
Semblance 5-54
Eigenstructure (Gersztenkorn and Marfurt, 1999)
seismic
dip scan coherence inline slice crossline slice
Comparison of Gradient Structure Tensor and dip scan eigenstructure coherence
GST coherence
time slice
5-55
(Bakker, 2003)
Coherence artifacts due to an ‘efficient’ calculation without search for structure
Coherence computed along a time slice 5-56
Coherence computed along structure
0.4 s
Seismic
0.6 s A
A′′
t (s)
0.4 0.6 0.8 0.8 s
1.0 1.2 1.4 Seismic section
1.0 s
1.2 s
5-57
1.4 s
0.4 s
Coherence without dip search
0.6 s A
A′′
t (s)
0.4 0.6 0.8 0.8 s
1.0 1.2 1.4 Coherence section
1.0 s
1.2 s
5-58
1.4 s
0.4 s
Coherence with dip search
0.6 s A
A′′
t (s)
0.4 0.6 0.8 0.8 s
1.0 1.2 1.4 Coherence section
1.0 s
1.2 s
5-59
1.4 s
Impact of lateral analysis window radius = 12.5 m
5-60
radius = 37.5 m
radius = 25 m
radius = 50 m
Impact of vertical analysis window On a stratigraphic target
Temporal aperture = 8 ms
Temporal aperture = 32 ms
On a structural target
5-61
Temporal aperture = 8 ms
Temporal aperture = 40 ms
Impact of vertical analysis window (time slice at t = 1.586 s)
+/- 6 24ms 12 ms 5-62
Impact of vertical analysis window Fault on coherence green time slice is shifted by a stronger, deeper event
Time (s)
0.5
1.0
1.5 Steeply dipping faults will not only be smeared by long coherence windows, but may appear more than once! 5-63
Time slices vs. horizon slices
Coherence time slice (better for fault and salt analysis) salt
Figure 3.45b 5 km
Coherence horizon slice (better for stratigraphic analysis) 5-64
5 km
time (ms)
Figure 3.46
Impact of height of analysis window
+32 +24 +16 +8 0 -8 -16 -24 -32
analysis window
time (ms)
5 km
5-65
+32 +24 +16 +8 0 -8 -16 -24 -32
analysis window
Coherence In summary, coherence: • Is an excellent tool for delineating geological boundaries (faults, lateral stratigraphic contacts, etc.), • Allows accelerated evaluation of large data sets, • Provides quantitative estimate of fault/fracture presence, • Often enhances stratigraphic information that is otherwise difficult to extract, • Should always be calculated along dip – either through algorithm design or by first flattening the seismic volume to be analyzed, and • Algorithms are local - Faults that have drag, are poorly migrated, or separate two similar reflectors, or otherwise do not appear locally to be discontinuous, will not show up on coherence volumes.
In general: • Stratigraphic features are best analyzed on horizon slices, • Structural features are best analyzed on time slices, and • Large vertical analysis windows can improve the resolution of vertical faults, but smears dipping faults and mixes stratigraphic features. 5-66
Volumetric curvature and reflector shape After this section you will be able to: • Use the most positive and negative curvature to map structural lineaments, • Choose the appropriate wavelength to examine rapidly varying vs. smoothly varying features of interest, • Identify domes, bowls, and other features on curvature and shape volumes, • Integrate curvature volumes with coherence and other geometric attributes, and • Choose appropriate curvature volumes for further geostatistical analysis. 5-67
Statistical measures based on vector dip • Reflector divergence and/or parallelism • angular unconformities • stratigraphic terminations? • Reflector curvature • flexures and folds • unresolved or poorly migrated faults • differential compaction • Reflector rotation • data quality • wrench faults 5-68
Sign convention for 3-D curvature attributes: Anticlinal: k > 0 Planar: k=0 Synclinal: k < 0
5-69
(Roberts, 2001)
3D Curvature and Topographic Mapping
5-70
(http://www.srs.fs.usda.gov/bentcreek)
3D Curvature and Molecular Docking
5-71
(http://en.wikipedia.org/wiki/Molecular_docking)
kneg > 0
kneg < 0 kpos < 0 kpos > 0
kpos > 0 kneg = 0
5-72
3D Curvature and Biometric Identification of Suspicious Travelers
Geometries of folded surfaces kpos < 0
kpos = 0
kpos > 0
synform
kneg < 0 saddle
bowl
antiform
kneg = 0 plane
kneg > 0 dome
5-73
(Bergbauer et al., 2003)
A multiplicity of curvature attributes! 1. Mean Curvature 2. Gaussian Curvature Measures validity of 3. Rotation quadratic surface 4. Maximum curvature Mathematical Basis Established use in 5. Minimum curvature fracture prediction 6. Most positive curvature Most useful for structural 7. Most negative Curvature interpretation 8. Dip curvature 9. Strike curvature 10. Shape index Established use in biometric 11. Curvedness ID and molecular docking 12. Shape index modulated by curvedness
See Roberts (2001) for definitions! 5-74
Curvature of picked horizons
5-75
kx-ky transform of time picks Seismic horizon -0.02
Short wavelength 50 00
x
50
(m
y
) 0 0 00 0 1
high
power
ky (cycles/m)
00 0 -2 00
Long wavelength
0
z (m)
+2
kx-ky spectrum
0.00
Footprint!
00
+0.02 -0.02
+0.00 kx (cycles/m)
+0.02
0
The horizon exhibits different scale structures such as domes and basins on the broad-scale, faults on the intermediate-scale, and smaller scale undulations. 5-76
(Bergbauer et al, 2003)
kx-ky transform of time picks after bandpass filter -0.02
high
-0.02 5-77
Long wavelength
0.00
+0.02
Short wavelength
Power
ky (cycles/m)
Short wavelength
Short wavelength
Short wavelength
+0.00 kx (cycles/m)
0
+0.02 (Bergbauer et al, 2003)
Maximum curvature after kx-ky bandpass filter kmax +5 Fa ul
ts
y (m)
5000 0
Contours
-5
0 0
5-78
5000 x (m)
10000 (Bergbauer et al, 2003)
Typical workflow for curvature calculated along picked horizons 1. pick horizon 2. smooth horizon 3. calculate curvature on tight grid for short wavelength estimates 4. smooth horizon some more 5. calculate curvature on coarse grid for long wavelength estimates
5-79
Multispectral estimates of volumetric curvature
Motivation:
• Structural Geology models relate curvature to fractures • Geologic features have different spectral lengths – short wavelength faults, moderate wavelength compaction over channels, longer wavelength domes and sags • Seismic artifacts have different spectral lengths – short wavelength footprint, moderate wavelength migration smears about faults • Current horizon-based curvature calculations are both tedious to generate and overly sensitive to picking errors • We have very accurate dip and azimuth volumes – can we use them to generate more robust curvature volumes? • Can we design x-y operators to produce results similar to Bergbauer et al.’s (2003) kx-ky transforms to avoid slicing the dip and azimuth volumes? 5-80
Radius of Curvature 3 km
Time (s)
1.0
1.2 5-81
Thermal imagery with sun-shading
5-82
(Cooper and Cowan, 2003)
Fractional derivatives with sun-shading
Red=0.75 Green=1.00 Blue=1.25
5-83
(Cooper and Cowan, 2003)
2D curvature estimates from inline dip, p: 1st derivative
dp/dx = F-1[ ikx F(p) ] fractional derivative (or 1st derivative followed by a low pass filter)
dαp/dxα ≈ F-1[i(kx )α F(p) ]
5-84
(al-Dossary and Marfurt, 2006)
“Fractional” derivatives α=1.25 α=1.00
α=1.00
α=0.75
α=0.75
α=0.50
α=0.50
α=0.25
α=0.25
α=0.01
α=0.01
0.0
-1.0
0.0 -20
0
+20
distance in grid points
convolutional operator in space 5-85
α=1.25
0.5
amp
amp
+1.0
0.00
0.25
0.50
kx wavenumber (cycles/grid point)
operator wavenumber spectrum (al-Dossary and Marfurt, 2006)
Filter applied to 1st derivative
Interpretation of ‘fractional derivative’ as a filter
ize l ea d I
3
d
0.80
2
1.00
1
Wavelength, λ 2∆x
4∆x
8∆x
16∆x
Wavenumber, k π/∆x
π/2∆x
π/4∆x
π/8∆x
32∆x π/16∆x infinite 0
0
5-86
0.25
4
ive t iva r de t s fir
(Chopra and Marfurt, 2007b)
Attributes extracted along a geological horizon
5-87
Vertical Slice – Fort Worth Basin, USA B′′ B
0.750 0.800
t (s)
Caddo
1.000
1.250
5-88
Ellenburger
(al-Dossary and Marfurt, 2006)
kmean=1/2(d2T/dx2+d2T/dy2) – Caddo (Horizon pick calculation) B′′ s/m2 -.25
0.0
+.25 5 km 5-89
B
kmean horizon slice – Caddo (volumetric calculation) B′′ s/m2 -.25
0.0
+.25 5 km 5-90
B
Coherence horizon slice – Caddo 5 km
B′′
1.0
0.9
.08
5-91
B
Attributes extracted along time slices
5-92
Vertical slice through seismic B
5 km
B′′
0.750 0.800
t (s)
Caddo
1.000
1.250
5-93
Ellenberger
(al-Dossary and Marfurt, 2006)
Time slice through coherence 5 km t=0.8 s B′′
1.0
0.9
0.8
5-94
B
(al-Dossary and Marfurt, 2006)
Most negative curvature (α=1.00) 5 km t=0.800 sB′′ s/m2 +.25
0.0
-.25
α=1.00 5-95
B
(al-Dossary and Marfurt, 2006)
Spectral estimates of most negative 5 km curvatureB′′ t=0.8 s s/m2 +.25
0.0
-.25
α=0.25 =2.00 =1.75 =1.50 =1.25 =1.00 =0.75 =0.50 5-96
B
(al-Dossary and Marfurt, 2006)
Principal component coherence 5 km t=0.8 s B′′
1.0
0.9
0.8
5-97
B
(al-Dossary and Marfurt, 2006)
Principal component coherence 5 km t=1.2 s B′′
1.0
0.9
0.8 B 5-98
(al-Dossary and Marfurt, 2006)
5 km
Most negative curvature (α=0.25) t=1.2 B′′ s
s/m2 +.25
0.0
-.25 B 5-99
(al-Dossary and Marfurt, 2006)
5 km
Most positive curvature (α=0.25) t=1.2B′′s
s/m2 -.25
0.0
+.25 5-100
B
(al-Dossary and Marfurt, 2006)
Zero-crossings are less sensitive to noise than peaks or troughs
noise pick error
signal
pick error
pick error
signal+noise
pick error
pick error
5-101
(Blumentritt et al., 2005)
Vertical slice through seismic volume showing faults having small displacement (Alberta, Canada) A 1240 ms
Neg
Pos
A’
Picked horizon
1520 ms
5-102
(Chopra and Marfurt, 2007a)
Curvature computed from a picked, filtered horizon A
A 2.5 km
A’
A’
Most-positive 5-103
Neg
0
Most-negative Pos
(Chopra and Marfurt, 2007a)
Curvature computed from volumetric dip/azimuth A
A 2.5 km
A’
A’
Most-positive 5-104
Neg
0
Most-negative Pos
(Chopra and Marfurt, 2007a)
s=-1.0
The shape index, s: bowl
k 2 + k1 s = − ATAN( ) π k 2 − k1 2
synform
s=-0.5
k1 ≥ k 2
s=0.0 saddle
Principal curvatures
antiform
s=+0.5
s=+1.0 dome
5-105
(Bergbauer et al., 2003)
Shape index and biometric identification photographic image
distance scan
Shape indices
5-106
(Woodward and Flynn, 2004)
dome
ridge
curvedness
0.2
saddle
valley
bowl
2D color table
0.0 -1.0 5-107
plane -0.5
0.0 +0.5 Shape index
+1.0
(al-Dossary and Marfurt, 2006)
Shape index modulated by curvedness 5 km (α=0.25) 5 km N
B’
Dome
Ridge
1. 4
Saddle
Valley
Bowl
e (s Tim
)
1 .2
Curvedness
0.2 B
Plane
Shape index
+1.0
0.0
+0.5
5-108
-0.5
-1.0
0.0
(Guo et al., 2007)
Shape index modulated by curvedness 5 km (α=0.25) 5 km N
B’
Dome
1. 4
Ridge
Saddle
Valley
Bowl
e (s Tim
)
1 .2
Curvedness
0.2 B
Transparent Plane
Shape index
+1.0
0.0
+0.5
5-109
-0.5
-1.0
0.0
(Guo et al., 2007)
Shape ‘components’ – an attempt to provide shape information amenable to geostatistical analysis
1.0
Filter response
bow l valle y 0.5
s addle ridge dom e
0.0 -1.0
-0.5
0.0
0.5
1.0
Shape index 5-110 Figure 3.68f
(al-Dossary and Marfurt, 2006)
Shape components (α=0.25) 5 km t=1.2 s
high
0
Ridge Dome Saddle Valley Bowl 5-111
(al-Dossary and Marfurt, 2006)
valley
‘Lineament’ attribute – an attempt to use shapes to accentuate linear features
Filter response
1.0
dome
ridge
saddle
bowl
0.5
0.0 -1.00
5-112
-0.50 0.00 Shape index
0.50
1.00
(Guo et al., 2007)
2D color bar for lineament attribute (strike=azimuth of minimum curvature) Strike 0
+90
Lineament
-90 strong
weak 5-113
(al-Dossary and Marfurt, 2006)
Valley component
Curvature lineaments colored by azimuth
-90 0.1
0.0
1.0 1.2 1.1 1.0 0.9 0.8sss s 5-114
-45
Strike 0
45
90
Volumetric view of lineament attribute (α=0.25) Strike 0
+90
Lineament
-90
transparent
5 km N
Time (s)
0.8
1.0
1.2
5-115
(Guo et al., 2007)
Example: Attribute time slices through Vinton Dome, Louisiana, USA
5-116
Coherence time slice at t=1.0 s 2km Coh 1.0
A’
0.9 N
A 5-117
0.8
Most negative curvature time slice at t=1.0 s 2km A’ +.25
0.0 N
A 5-118
-.25
Most positive curvature time slice at t=1.0 s 2km A’ -.25
0.0
A 5-119
+.25
Reflector rotation time slice at t=1.0 s 2km A’ 1.0
0.0
A 5-120
-1.0
Vertical seismic slice A
A’
0.5
Time (s)
2km
1.0
1.5 5-121
Computational vs. Interpretational curvature
Normal fault seen by curvature 5-122
Strike slip fault not seen by curvature
Computational vs. Interpretational curvature
Channel not seen by curvature Channels seen by curvature 5-123
Curvature In Summary: • Volumetric curvature extends a suite of attributes previously limited to interpreted horizons to the entire uninterpreted cube of seismic data. • The most negative and most positive curvatures appear to be the most unambiguous of the curvature images in illuminating folds and flexures. • Curvature attributes are a good indicator of paleo rather than present-day stress regimes. • Open fractures are a function of the strike of curvature lineaments and the azimuth of minimum horizontal stress. • Channels appear in curvature images if there is differential compaction. • Faults appear in curvature images if there is a change in reflector dip across the fault, reflector drag, if the fault displacement is below seismic resolution, or if the fault edge is over- or under-migrated. 5-124
Lateral Changes in Amplitude and Pattern Recognition After this section you will be able to: • Relate lateral changes in amplitude to thin bed tuning, • Identify channels and other thin stratigraphic features on amplitude gradient images, • Predict which geologic features can be seen best by amplitude gradients, textures, curvature, and coherence attributes, and • Apply best practices for stratigraphic interpretation.
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Thin bed tuning and the wedge model 0
thickness (ms)
50 0
impedance
100
Time (ms)
50
150 0 50
reflectivity
+0.1
100
Time (ms)
-0.1
150 0 50 100
Time (ms)
seismic
150
env 2
0
100 150
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Time (ms)
envelope
50
0
(Partyka, 2001)
Thin bed tuning and the wedge model
50
-0.025
40
-0.020
30 20 10 0 0
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Tuning thickness
amplitude of trough
Trough to peak thickness (ms)
Tuning thickness
10 20 30 40 50 Temporal thickness (ms)
-0.015 -0.010 -0.005 -0.000
0
10 20 30 40 50 temporal thickness (ms)
Sobel edge detector (numerical approximation to first derivative) ∂u u ( x + ∆x) − u ( x − ∆x) = lim ∂x ∆x − >0 2∆x
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