why seismic processing ?
DESCRIPTION
Processing Steps. Why seismic processing ?. + Noise (t) =. Seismic trace. R(t). S(t). Rf(t). Sandstone. Coal. Carbonate. Salt. Shale. What’s a seismic trace?. *. *. Deconvolution. Filtering Stacking. f(t). g(t). *. f(t). g(t). *. Wave propagation. Marine data. - PowerPoint PPT PresentationTRANSCRIPT
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By:
Ali
Mis
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Why seismic processing ?
Processing Steps
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By:
Ali
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aghi What’s a seismic trace?
Sandstone
Coal
Carbonate
Salt
Shale
* S(t) * R(t) Seismic trace + Noise (t) =Rf(t)
Filtering
Stacking
.
.
.
Deconvolution
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*
g(t)
f(t)
*
g(t)
f(t)
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Wave propagation
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aghi Land dataMarine data
Split shot gather
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0.0
0.2
0.4
0.6
0.8
1.0
0 500 1000 1500 2000 2500
X (m)
T (s)
Direct
Ground roll
Head wave (refraction)First multiple
Primary
R1 R2
Seismic eventsNon-primary events
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Primary
Earth’s surface
Subsurface reflector
S R1
Ground roll Direct P-wave
R2
Head wave (refraction)
First multiple
Seismic eventsNon-primary events
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CDP Fold =Number of receivers x receiver interval
2 x shot interval
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CDP gather NMO Stack
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Migration
“The goal of migration is to make the stacked section appear similar to the
geologic cross-section”
Oz Yilmaz
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A step in seismic processing in which reflections in seismic data are moved to their correct locations in the x-y-time space of seismic data, including two-way traveltime and position relative to shotpoints
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m
n
Zn Zm
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Typical ProMax flow for velocity analysis.
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Examining the normal moveout equation, it is possible to analyze NMO velocities by plotting reflections in T2 X2 space
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Concept of Constant Velocity Stack as an aid to stackingvelocity estimation.
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One method to determine stacking velocity is to use a Constant Velocity Stack (CVS) for several CDP gathers
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Same CVS panel of traces as before switching to variabledensity color for the traces to utilize dynamic range
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Same as previous color panels with velocity range nowhalved to better pick correct velocities
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Another term for Normal Moveout Equation.
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Options in the ProMax Velocity Analysis Routine.
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Demonstration of the velocity spectra
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Options in the ProMax Velocity Analysis Routine.
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CDP gather with NMO applied (center) surrounded by panelshaving progressively lower velocity (left) or higher velocity.
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Options in the ProMax Velocity Analysis Routine.
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Options in the ProMax Velocity Analysis Routine.
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From left to right are panels for Semblance, Gather, DynamicStack, Flip Stacks, and Velocity Function Stack.
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The ProMax routine ‘Velocity Analysis’ has it all – from left toright: velocity spectra, interactive cursor with CDP gather,dynamic stack, and a variation on CVS
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The Semblance Panel shows the semblance plot, the pickedvelocity function, guide functions, and the interval velocitycomputed from the picked function.
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Dix equation converts stacking velocities to interval velocities.
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aghi However, you get RMS velocities, one can continue to
calculate interval velocities, interval thicknesses, and average velocities.
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Remaining three panels in Velocity Analysis routine.
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Use of ProMax routine Velocity Viewer and Editor
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A common problem with stacking is residual NMO on theCDP gathers resulting from imperfect velocity specification.
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Example of the data/velocity Interleave Display usingLandmark’s SeisCube program.
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Progressive Mute Analysis
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Prestack CDP gather with a horizon plotted along an eventthat is not perfectly flattened by NMO; other causes might bestatics, noise, and/or lithology that is affecting the phase.
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ProMax routine CDP/Ensemble Stack vertically stacksinput ensembles of traces.
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Definition of multiplies as it applies to processingseismic reflection data using ProMax.
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Example of a surface multiple on left in red and intrabedmultiple on the right in blue.
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Comparison of short-path and long-path multiples.
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Conceptual abstraction of the Tau – P domain
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Organizing seismic reflection data into ray-parameter domainhas certain advantages that are elaborated here.
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Working definition of the Radon Filter commonly used for multiple suppression – working in the intercept-time (T) / ray parameter (p) or slowness domain.
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Use of the radon transform for the removal of multiples bydiscriminating on the basis of moveout – here no rejection.
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Use of the radon transform for the removal of multiples bydiscriminating on the basis of moveout – rejection shown.
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More on the use of the Radon Filter.
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Migration
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aghi Migration
“Migration is an inversion operation involving rearrangement of seismic information elements so that
reflections and diffractions are plotted at their true locations.”
R.E Sheriff
“The goal of migration is to make the stacked section appear similar to the geologic cross-section”
Oz Yilmaz
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Unmigrated
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Migrated
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Migration
• Collapses diffractions
• Corrects for dip– Moves dipping events in the updip direction
• Removes effects of surface curvature– “unties the bowties”
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aghi Reconstructing the wavefield
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Constant velocity migration
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aghi Schematic that shows the imaging problem for a
simple anticline.
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aghi Schematic that shows the imaging problem for a
simple syncline.
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Schematic that shows the imaging problem for a vertical fault.
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aghi Schematic that shows the imaging problem for a 30-
degree fault.
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aghiSchematic that shows the imaging problem for a reef model.
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• Kirchoff migration (diffraction stacking)
• Finite difference method
• F-K migration
– integral solution of wave equation
– derivative solution of wave equation
– Fourier domain solution of wave equation
Migration Methods
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Kirchoff Migration(Diffraction Summation)
For every point (x,z), collapse all energy from hyperbola with vrms
AB
C O
t0
x
t
2
220
2 4rmsv
xtt
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Kirchoff Migration(Diffraction Summation)
Factors to consider before summing energy in diffraction:
• Obliquity factor– A cos
• Spherical divergence factor– A 1/r
• Wavelet shaping factor– phase correction
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Migration collapses diffractions to reveal structure
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Migration collapses diffractions to reveal structure
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Finite Difference Migration
• Solving the wave equation by stepping down discrete intervals from z=0
• Downward continue wavefield to “exploding reflector”
• Define an angle for width of cone for to be included in migration for each point– wider cone more accurate– narrow cone faster, better approximations
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sintan a
Migration steepens and moves dipping reflectors
Apparent dip in time section is related to true dip:
(migrator’s equation)
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aghi Collapsing diffraction and relocating
dipping surface
Diffraction D Apex P
Reflector B A
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F-K Migration• Events can be separated by their
dips in F-K space
• Transform according to migrator’s equation tan a=sin
• Advantage: very computationally efficient!
• Disadvantage: only works for constant velocity (without modifications that compromise its efficiency)
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aghi Migration removes multiple-
branch reflections
• Synclines get broader
• Anticlines get narrower
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“Untying the bowties”
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Limitations of Migration• Insufficient spatial resolution will result in aliasing• 2-D slice of 3-D wavefield (need 3-D migration!)• Edge effects• Coherent noise• Requires knowledge of velocity structure• Time migration methods assume lateral velocity varies
slowly (otherwise need depth migration)
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3-D Processing
• Binning by common midpoints in cells on a grid
• Migration can be two stage 2-D migration (in-line direction, then cross-line direction) or full 3-D wavefield solution
• Most other processing operations are unchanged
• Display is more difficult (and more fun!)
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Why Deconvolution?
• Decreases ringing
• Increases resolution
• Improves appearance of stacked section and makes it easier to interpret
• Section is more like the earth and less like the seismic source
• Can remove multiples
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Convolutional model of a seismograms=w*e+n
source wavelet
11v
22v
33v
44v
55v
Earth response function seismogram
*
(+noise)
=
![Page 88: Why seismic processing ?](https://reader033.vdocuments.us/reader033/viewer/2022061602/56814667550346895db389cf/html5/thumbnails/88.jpg)
By:
Ali
Mis
aghi
Spiking Filter
• Take existing wavelet and transform to a unit impulse (delta function)
• Also called “whitening” because it aims to create a white spectrum
![Page 89: Why seismic processing ?](https://reader033.vdocuments.us/reader033/viewer/2022061602/56814667550346895db389cf/html5/thumbnails/89.jpg)
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Mis
aghi
Predictive Deconvolution
• Deconvolution with a built-in time lag
• Use to remove – Multiples– Bubble pulse
![Page 90: Why seismic processing ?](https://reader033.vdocuments.us/reader033/viewer/2022061602/56814667550346895db389cf/html5/thumbnails/90.jpg)
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Mis
aghi
Deconvolution Example
![Page 91: Why seismic processing ?](https://reader033.vdocuments.us/reader033/viewer/2022061602/56814667550346895db389cf/html5/thumbnails/91.jpg)
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Mis
aghi
Raw gather decon Bandpassfiltered
autocorrelograms
![Page 92: Why seismic processing ?](https://reader033.vdocuments.us/reader033/viewer/2022061602/56814667550346895db389cf/html5/thumbnails/92.jpg)
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Mis
aghi
Raw gathers
![Page 93: Why seismic processing ?](https://reader033.vdocuments.us/reader033/viewer/2022061602/56814667550346895db389cf/html5/thumbnails/93.jpg)
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Mis
aghi
After decon
![Page 94: Why seismic processing ?](https://reader033.vdocuments.us/reader033/viewer/2022061602/56814667550346895db389cf/html5/thumbnails/94.jpg)
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Mis
aghi
![Page 95: Why seismic processing ?](https://reader033.vdocuments.us/reader033/viewer/2022061602/56814667550346895db389cf/html5/thumbnails/95.jpg)
By:
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Mis
aghi
Deconvolution
• Deterministic Inverse Filtering
• Deghosting
• Least Squares (Optimum) Filtering
• Spiking filter
• Wavelet shaping
• Predictive Deconvolution
![Page 96: Why seismic processing ?](https://reader033.vdocuments.us/reader033/viewer/2022061602/56814667550346895db389cf/html5/thumbnails/96.jpg)
By:
Ali
Mis
aghi Convolutional model of a
seismogram
s=w*e+n
One equation with 3 unknowns
How can we possibly find e?
We make assumptions:– e, n are white (random)– w is minimum phase
![Page 97: Why seismic processing ?](https://reader033.vdocuments.us/reader033/viewer/2022061602/56814667550346895db389cf/html5/thumbnails/97.jpg)
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Mis
aghi
w
e
s
![Page 98: Why seismic processing ?](https://reader033.vdocuments.us/reader033/viewer/2022061602/56814667550346895db389cf/html5/thumbnails/98.jpg)
By:
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Mis
aghi
Am
plitu
deS
pect
rum
Aut
ocor
rela
tion
Am
plitu
deEarth response Wavelet Seismogram
![Page 99: Why seismic processing ?](https://reader033.vdocuments.us/reader033/viewer/2022061602/56814667550346895db389cf/html5/thumbnails/99.jpg)
By:
Ali
Mis
aghi
Deterministic DeconvolutionAssume that an operator f(t) exists such that
)()(*)( ttftw
In the Fourier domain:
1)()( FW
f
w
if
iw
eAF
eAW
)()(
)()(
wiw eA
F)(
1)( so
and
)()(
)(/1)(
wf
wf AA
The inverse operator f(t) has opposite phase and inverse amplitude spectrum from the source wavelet w(t)
![Page 100: Why seismic processing ?](https://reader033.vdocuments.us/reader033/viewer/2022061602/56814667550346895db389cf/html5/thumbnails/100.jpg)
By:
Ali
Mis
aghi
Deterministic Deconvolution
• Assumptions:1 source wavelet is minimum phase2 noise is zero3 wavelet is known
• Not true, especially 2
• In practice, the Fourier domain implementation is not very good if assumptions are not met
• Other methods are more stable
![Page 101: Why seismic processing ?](https://reader033.vdocuments.us/reader033/viewer/2022061602/56814667550346895db389cf/html5/thumbnails/101.jpg)
By:
Ali
Mis
aghi
Deghosting
• Eliminate source & receiver ghosts by considering them as time delayed copies of the source (and with known depths the time delays are known)
• Alternatively, hydrophones and geophones with different responses can be combined to eliminate ghosting effects
![Page 102: Why seismic processing ?](https://reader033.vdocuments.us/reader033/viewer/2022061602/56814667550346895db389cf/html5/thumbnails/102.jpg)
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Mis
aghi
Correlation
Autocorrelation
Cross-correlation
1,,1,01
)(1
0
Nkxx
Nxr
kN
tkttk
1,,1,01
),(1
0
Nkyx
Nyxg
kN
tkttk
![Page 103: Why seismic processing ?](https://reader033.vdocuments.us/reader033/viewer/2022061602/56814667550346895db389cf/html5/thumbnails/103.jpg)
By:
Ali
Mis
aghi
Wavelet Estimation
• In general, the source wavelet is unknown
• Source wavelet can be estimated from seismogram alone assuming:– minimum phase wavelet– white earth response spectrum
ewx rrr * autocorrelation
(with white earth response)wx rrr 0
Autocorrelation of seismogram is the autocorrelation of source wavelet (within a constant)
![Page 104: Why seismic processing ?](https://reader033.vdocuments.us/reader033/viewer/2022061602/56814667550346895db389cf/html5/thumbnails/104.jpg)
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Mis
aghi
Optimum Weiner Filters
Want to find the optimum filter components fi that minimize the error between the desired and actual outputs in a least-squares sense:
t
ttt xfdL 2)(
)1(,,2,1,0,0
nif
L
i
By setting
so 022
itt t
titti
xxfxdf
L
Recognizing the terms for auto- and cross-correlation,
ii grf
itt t
titt xdxxf
or
![Page 105: Why seismic processing ?](https://reader033.vdocuments.us/reader033/viewer/2022061602/56814667550346895db389cf/html5/thumbnails/105.jpg)
By:
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Mis
aghi
Optimum Weiner Filters
ii grf
Or, in matrix form,
1
2
1
0
1
2
1
0
0321
3012
2101
1210
nnnnn
n
n
n
g
g
g
g
f
f
f
f
rrrr
rrrr
rrrr
rrrr
The autocorrelation matrix is a Toeplitz matrix, and can be inverted by Levinson recursion
are called the normal equations
![Page 106: Why seismic processing ?](https://reader033.vdocuments.us/reader033/viewer/2022061602/56814667550346895db389cf/html5/thumbnails/106.jpg)
By:
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Mis
aghi
Optimum Weiner Filters
1
2
1
0
1
2
1
0
0321
3012
2101
1210
nnnnn
n
n
n
g
g
g
g
f
f
f
f
rrrr
rrrr
rrrr
rrrr
The gi terms are the cross-correlation of the desired wavelet with the input wavelet (seismogram).
0
0
0
1
1
2
1
0
0321
3012
2101
1210
nnnn
n
n
n
f
f
f
f
rrrr
rrrr
rrrr
rrrr
In the case of spiking deconvolution, the normal equations take the form
![Page 107: Why seismic processing ?](https://reader033.vdocuments.us/reader033/viewer/2022061602/56814667550346895db389cf/html5/thumbnails/107.jpg)
By:
Ali
Mis
aghi
Wavelet Processing
• Attempt to shift source wavelet to some other known wavelet, to accomplish one or more of:
• Reduce variation of source (between shots, between receivers)
• Shift to another known wavelet– e.g., hydrophone response to match seismometer
• Separate wavelet and earth response more clearly
![Page 108: Why seismic processing ?](https://reader033.vdocuments.us/reader033/viewer/2022061602/56814667550346895db389cf/html5/thumbnails/108.jpg)
By:
Ali
Mis
aghi
Wavelet Processing
![Page 109: Why seismic processing ?](https://reader033.vdocuments.us/reader033/viewer/2022061602/56814667550346895db389cf/html5/thumbnails/109.jpg)
By:
Ali
Mis
aghi
Wavelet Processing
Transform to zero phase and broaden spectrumIncrease resolution
![Page 110: Why seismic processing ?](https://reader033.vdocuments.us/reader033/viewer/2022061602/56814667550346895db389cf/html5/thumbnails/110.jpg)
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Mis
aghi
![Page 111: Why seismic processing ?](https://reader033.vdocuments.us/reader033/viewer/2022061602/56814667550346895db389cf/html5/thumbnails/111.jpg)
By:
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Mis
aghi
1) Shots : 2 – 548
2) Minimum phase
3) Traces have been resampled (2ms >4ms) and decimated (384 > 192)
4) Fk filter
5) Geometry has been applied
6) Velocity file is available(By Geco)
Real data
12.5 m
91o
25 m
7.5 m 8.5 m
![Page 112: Why seismic processing ?](https://reader033.vdocuments.us/reader033/viewer/2022061602/56814667550346895db389cf/html5/thumbnails/112.jpg)
By:
Ali
Mis
aghi
Check the muteVelocity Analysis
NMO
Stack
Real data work flowSorting
Pick mute
True Amplitude Recovery
Deconvolution
Velocity Manipulation
Migration
Demultiple
![Page 113: Why seismic processing ?](https://reader033.vdocuments.us/reader033/viewer/2022061602/56814667550346895db389cf/html5/thumbnails/113.jpg)
By:
Ali
Mis
aghi Real data
-A report:
-Explanation of the processing steps with proper and related snap shots(Mute, TAR, Decon, NMO, Demultipling, Stacking, Migration,etc
-Final results(a comparison study)
-Brute-stack section(s)
-Demultipled stack section(s)
-Migrated section(s)
Project results: