wavefield prediction of water-layer multiples

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


• TheoryTheory




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


• TheoryTheory




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


Predicted LWLMPredicted LWLM

TimeTime (ms)(ms)

400400

25002500


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


Further Multiple AttenuationFurther Multiple Attenuationby Deconvolutionsby Deconvolutions

TimeTime (ms)(ms)

400400

25002500


OutlineOutline


• TheoryTheory




A Mobil dataA Mobil data

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

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


• TheoryTheory




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.

wavefield prediction of water-layer multiples

Documents