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Wavefield Prediction of Water-layer Multiples. Ruiqing He University of Utah Oct. 2004. Outline. Introduction Theory Synthetic experiments Application to real data Conclusion. Introduction. Multiple classification. Free-surface multiples (FSM). - PowerPoint PPT Presentation
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Wavefield Prediction Wavefield Prediction of Water-layer Multiplesof Water-layer Multiples
Ruiqing HeRuiqing He
University of UtahUniversity of Utah
Oct. 2004Oct. 2004
OutlineOutline
• IntroductionIntroduction
• TheoryTheory
• Synthetic experimentsSynthetic experiments
• Application to real dataApplication to real data
• ConclusionConclusion
IntroductionIntroduction
•Multiple classification.Multiple classification.
•Free-surface multiples (FSM).Free-surface multiples (FSM).
- Delft, multiple series theories, etc.- Delft, multiple series theories, etc.
•Water-layer multiplesWater-layer multiples (WLM). (WLM).
- Berryhill, Wiggins, et al.- Berryhill, Wiggins, et al.
Berryhill’s ApproachBerryhill’s Approach
•The prediction of WLM is obtained by propagating The prediction of WLM is obtained by propagating the received data once within the water layer.the received data once within the water layer.
- - Kirchhoff integral, Finite-Difference, Kirchhoff integral, Finite-Difference,
Gaussian beams, Phase-shift, etc.Gaussian beams, Phase-shift, etc.
•The prediction is emulation.The prediction is emulation.
- - Part of WLM.Part of WLM.
- - Half is exact; the other half is not exact.Half is exact; the other half is not exact.
•Multiple subtraction.Multiple subtraction.
OutlineOutline
• IntroductionIntroduction
• TheoryTheory
• Synthetic experimentsSynthetic experiments
• Application to real dataApplication to real data
• ConclusionConclusion
Seismic Wave RepresentationSeismic Wave Representation
gS: Ghost-source. s*: Twin-source.
f: visit of subsurface once. g: Receiver-side ghosting.
*
* *
st * *
nd * *
*
*
1
*
0
Source :
Primaries Interbeds :
1 order FSM :
2 order FSM :
...
( )
( ) ( )
Data : ( ) ( )
n
n
n
n
S gS S
PI fS gfS
fgfS gfgfS
fgfgfS gfgfgfS
PI f gf S
FSM f gf gf S
W PI FSM f gf gf S
Berryhill’s EmulationBerryhill’s Emulation
*
1
*
0
* *
0 1
( ) ( )
( ) ( )
Emulation:
' ( ) ( ) ( ) ( ) ( )
if:
then: '
n
n
n
n
n n
n n
FSM f gf gf S
W f gf gf S
FSM gf W gf f gf gf S f gf gf S FSM
gf fg
FSM FSM
FSM PredictionFSM Prediction* * *
* * *
1 1 1
*
1
( )
( ) ( ) ( ) ( )
Steps:
1:receiver_side_ghost_decomposition: ( )
2:forward_mode
u g
n n nu g
n n n
u u g g u g
ng g
n
PI f gf S fS gfS PI PI
FSM f gf gf S f gf S gf gf S FSM FSM
W PI FSM PI FSM PI FSM W W
D PI FSM gf S
*
1
ling: ( ) ( )
3 :
nu
n
u u
f D f gf S FSM
PI W D FSM
Subscript g: Receiver-side ghosts (RSG).
Subscript u: Upcoming data that generate RSG.
Multiple ClassificationMultiple Classification
• Level 1:– Water-Layer Multiple (WLM).
– Non-WLM multiples (NWLM).
• Level 2 (WLM):– Last reverberation WLM (LWLM).
– First reverberation WLM (FWLM).
– Middle reverberation WLM (MWLM).
• Definition priority.• Water-Bottom-Multiple (WBM).
Types of Water-Layer MultiplesTypes of Water-Layer Multiples
FWLM MWLM
Water bottom
LWLM
Water surface
Subsurface reflector
Seismic Data ClassificationSeismic Data Classification
Level 0
Seismic Data (W)
Level 1
Upcoming Waves (U) D
Level 2
WLM NWLM P
Level 3
LWLM FWLM MWLM
Note: Converted waves are not considered,
and direct waves have been removed.
LWLM PredictionLWLM Prediction
Data (W)
Upcoming
waves (U)
Downgoingghosts (D)
LWLMg
+
-
For synthetic data, the operator g, f can be exactly known.
By this design, LWLM can be exactly predicted.
f
OutlineOutline
• IntroductionIntroduction
• TheoryTheory
• Synthetic experimentsSynthetic experiments
• Application to real dataApplication to real data
• ConclusionConclusion
Synthetic Model Synthetic Model
DepthDepth (m)(m)
00
15001500
Offset (m)Offset (m)00 32503250
waterwater
SandstoneSandstone
Salt domeSalt dome
HydrateHydrate
Synthetic DataSynthetic Data
TimeTime (ms)(ms)
400400
25002500
Offset (m)Offset (m)00 32503250
Predicted LWLMPredicted LWLM
TimeTime (ms)(ms)
400400
25002500
Offset (m)Offset (m)00 32503250
Waveform ComparisonWaveform Comparisonbetween Data & RSG+LWLM between Data & RSG+LWLM
Am
pli
tud
eA
mp
litu
de
Time (ms)Time (ms)600600 24002400
DataData
RSG + LWLMRSG + LWLM
Elimination of RSG & LWLMElimination of RSG & LWLMby Direct Subtractionby Direct Subtraction
TimeTime (ms)(ms)
400400
25002500
Offset (m)Offset (m)00 32503250
Further Multiple AttenuationFurther Multiple Attenuationby Deconvolutionsby Deconvolutions
TimeTime (ms)(ms)
400400
25002500
Offset (m)Offset (m)00 32503250
OutlineOutline
• IntroductionIntroduction
• TheoryTheory
• Synthetic experimentsSynthetic experiments
• Application to real dataApplication to real data
• ConclusionConclusion
A Mobil dataA Mobil data
Predicted LWLMPredicted LWLM
Waveform ComparisonWaveform Comparison
WLM AttenuationWLM Attenuationwith Multi-Channel Deconvolutionwith Multi-Channel Deconvolution
Migration before demultipleMigration before demultiple Migration after demultipleMigration after demultiple
A Unocal DataA Unocal Data
Predicted LWLMPredicted LWLM
Waveform ComparisonWaveform Comparison
At a geophone above non-flat water bottom
At a geophone above flat water bottom
WLM AttenuationWLM Attenuationwith Multi-channel Deconvolutionwith Multi-channel Deconvolution
Migration before demultipleMigration before demultiple Migration after demultipleMigration after demultiple
OutlineOutline
• IntroductionIntroduction
• TheoryTheory
• Synthetic experimentsSynthetic experiments
• Application to real dataApplication to real data
• ConclusionConclusion
ConclusionConclusion• Berryhill’s approach does not need to know the Berryhill’s approach does not need to know the source signature, and can be performed in a single source signature, and can be performed in a single shot gather, but the prediction is emulation.shot gather, but the prediction is emulation.
• This method improves Berryhill’s approach by This method improves Berryhill’s approach by making clear classification among WLM, and making clear classification among WLM, and using receiver-side ghosts to predict LWLM.using receiver-side ghosts to predict LWLM.
• This method exactly eliminates LWLM for This method exactly eliminates LWLM for synthetic data, and successfully suppresses WLM synthetic data, and successfully suppresses WLM by multi-channel de-convolutions by multi-channel de-convolutions for field datafor field data ..
ThanksThanks
• This research is benefited from the This research is benefited from the discussions with Dr. Yue Wang and Dr. discussions with Dr. Yue Wang and Dr. Tamas Nemeth of ChevronTexaco Co..Tamas Nemeth of ChevronTexaco Co..
• I am also thankful to 2004 members of I am also thankful to 2004 members of UTAM for financial support.UTAM for financial support.
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