Dimensionality Reduction for Seismic Attribute...
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Dimensionality Reduction for Seismic Attribute Analysis Bradley C. Wallet, Ph.D. University of Oklahoma ConocoPhillips School of Geology and Geophysics
Transcript of Dimensionality Reduction for Seismic Attribute...
Dimensionality Reduction for Seismic Attribute Analysis
Bradley C. Wallet, Ph.D. University of Oklahoma
ConocoPhillips School of Geology and Geophysics
Where oil is first found is in the minds’ of men - Wallace Pratt
Motivation
Motivation
Outline
• Seismic data • Seismic attributes • PCA • Image grand tour • Non-linear methods • Conclusions • Acknowledgements
Outline
• Seismic data • Seismic attributes • PCA • Image grand tour • Non-linear methods • Conclusions • Acknowledgements
Why care about seismic data?
• Single pre-stack data sets can be 10’s – 100’s of terabytes in size • Provide good spatial coverage exploration area • Used to make high dollar decisions
Seismic shot
Courtesy of Bin Lyu
Common midpoint gather
Migration
Convolutional model
Velocity Density Impedance
= x Shale
Sand
Shale
Sand
Shale
Lithology Reflection Coefficients
⇒ * ⇒
Wavelet
Seismic data
(Elebiju et al., 2009)
Outline
• Seismic data • Seismic attributes • PCA • Image grand tour • Non-linear methods • Conclusions • Acknowledgements
These are features
From one comes many Seismic data
Attribute 2 Attribute 3 Attribute 4 Attribute 5 Attribute 6 Attribute 7 Attribute 8 Attribute 1
Coherence
inline inline
Seismic
(Bahorich and Farmer, 1995)
5 km
Coherence
(Bahorich and Farmer, 1995)
salt 5 km
1.0
0.6
Coh
Spectral decomposition Synthetic Reflectivity CWT Magnitude Voices
CWT magnitude
0
pos
(Matos and Marfurt, 2011)
Σ
Le Nozze di Figaro
Spectral decomposition
(Laughlin et al., 2002)
A
A′
15 Hz Map
A′
A
30 Hz Map
30 Hz 15 Hz A A′
Tim
e (s
)
Spectral decomposition
18 Hz Red 24 Hz Green 36 Hz Blue
(Bahorich et al., 2002)
Dip attributes
y
z
x
θy (crossline dip) θx
(inline dip)
a
φ (dip azimuth) θ (dip magnitude)
ψ (strike)
n
(Marfurt, 2006)
Dip attributes
Instantaneous dip = dip with highest coherence (Marfurt et al, 1998)
Analysis Point
Minimum dip tested (-200)
Maximum dip tested (+200)
Dip with maximum
coherence (+50)
Dip attributes Dip Azimuth Hue
180 360 0
Dip
Mag
nitu
de
Sat
urat
ion
0
High
N
E
S
W
(c)
1.2
1.4
(Guo et al., 2008)
How do we “assimilate” all these attributes?
Outline
• Seismic data • Seismic attributes • PCA • Image grand tour • Non-linear methods • Conclusions • Acknowledgements
PCA
• Rotates attribute space • New dimensions are called principal components • Var(pc1) > Var(pc2) > … > Var(pc d) • Defines variance as information
PCA
(Wikapedia)
Watonga survey
Complex PCA
Complex PCA
PCA
PCA
PCA
Outline
• Seismic data • Seismic attributes • PCA • Image grand tour • Non-linear methods • Conclusions • Acknowledgements
Linear projections
Poorly separated Somewhat separated
∑=
=d
iiiproj
1)( ξαξ
Well separated
The Grand Tour (1750-1880’s)
Image Grand Tour 7.005 10.95 -6.215
View Locked Color IGT
Outline
• Seismic data • Seismic attributes • PCA • Image grand tour • Non-linear methods • Conclusions • Acknowledgements
Latent spaces
a)
b)
N
Cartoon illustration of GTM
Generative topographical maps
Canterbury Basin, offshore New Zealand
170°
30’
E
173°
00’
E
45° 30’ S
46° 30’ S
(Figure by Origin Energy) (Modified from Mitchell and Neil, 2012)
Waka 3D
36
Seismic
Peak Frequency
38
Peak spectral magnitude
39
Curvedness
40
GLCM homogeneity
41
Co-rendering
GTM
Waveforms as attributes
(Wallet et al, 2009)
Watonga revisited
(Wallet et al, 2009)
Diffusion maps
Form n-by-n similarity matrix
Normalize rows to sum to 1
Perform PCA on diffusion matrix
Diffusion maps
Advantages • Closed form solution • Direct calculation of inter-point
distances • Not tied to a Euclidean space • Eigenvalues
Disadvantages • Computationally intractable for
reasonable sized data sets • Out of training set data are not
defined in mapping
Diffusion maps
Outline
• Seismic data • Seismic attributes • PCA • Image grand tour • Non-linear methods • Conclusions • Acknowledgements
Conclusions
• The human is still the best interpreter we have • Attribute overload can overwhelm interpeters • Dimensionality reduction produces highly interpretable images
Acknowledgments
• Prof. Kurt Marfurt (University of Oklahoma) • Mr. Victor Aarre (Schlumberger Norway Technology Center) • Mr. Tao Zhao (OU) • Dr. Marcilio de Matos (Petrobras) • CGG Veritas, Chesapeake Energy, Anadarko Petroleum, and the
Government of New Zealand
Acknowledgments
