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Elastic least-squares migration Yuting Duan * , Antoine Guitton, and Paul Sava Center for Wave Phenomena Colorado School of Mines * presently at Shell International Exploration and Production Inc.
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Transcript of Yuting Duan , Antoine Guitton, and Paul Sava Center for Wave...

Elastic least-squares migration

Yuting Duan, Antoine Guitton, and Paul SavaCenter for Wave Phenomena

Colorado School of Minespresently at Shell International Exploration and Production Inc.

= V 2p

= V 2p

= V 2s

= V 2s

= V 2p

= V 2s

higher resolutiond

fewer artifactsd

improved amplitudes

objective function

J =e

1

2Fm d2

I F: demigration

I FT: migration

isotropic wave equation

u ( u) + ( u) = f

I = +2I =

I u (e, x, t): background wavefield

I f (e, x, t): source

, u

u ( u) + ( u)= ( u) ( u)

Born

I : perturbation

I : perturbation

I u: background wavefield

I u (e, x, t): perturbed wavefield

, u

u ( u) + ( u)= ( u) ( u)

Born

I : perturbation

I : perturbation

I u: background wavefield

I u (e, x, t): perturbed wavefield

, u

u ( u) + ( u)= ( u) ( u)

Born

I : perturbation

I : perturbation

I u: background wavefield

I u (e, x, t): perturbed wavefield

, u

u ( u) + ( u)= ( u) ( u)

= [( u) d ( u)][

]= Bm

, u

u ( u) + ( u)= ( u) ( u)

= [( u) d ( u)][

]= Bm

, u

u ( u) + ( u)= ( u) ( u)

= [( u) d ( u)][

]= Bm

, u

u ( u) + ( u)= ( u) ( u)

= [( u) d ( u)][

]= Bm

forward operator (demigration)

dK = KKKKKKKKKKPKKKKKBKKKm

data forward Bornextraction modeling source

I m: model perturbation

[

]I d: data

forward operator (demigration)

dK = KKKKKKKKKKPKKKKKBKKKm

data forward Bornextraction modeling source

I m: model perturbation

[

]I d: data

forward operator (demigration)

dK = KKKKKKKKKKPKKKKKBKKKm

data forward Bornextraction modeling source

I m: model perturbation

[

]I d: data

forward operator (demigration)

dK = KKKKKKKKKKPKKKKKBKKKm

data forward Bornextraction modeling source

I m: model perturbation

[

]I d: data

adjoint operator (migration)

mK = KKKBTKKKKKPTKKKKKKTKKKd

imaging backward datacondition modeling injection

I m: model perturbation

[

]I d: data

adjoint operator (migration)

mK = KKKBTKKKKKPTKKKKKKTKKKd

imaging backward datacondition modeling injection

I m: model perturbation

[

]I d: data

adjoint operator (migration)

mK = KKKBTKKKKKPTKKKKKKTKKKd

imaging backward datacondition modeling injection

I m: model perturbation

[

]I d: data

imaging condition

=e,t

[( us)] ur

=e,t

[ ( us)] ur

I us : source wavefield

I ur : receiver wavefield

objective function

J =e

1

2W (KBFm d) 2

I W (e, x, t): data weighting

objective function

J =e

1

2W (KPBm d) 2

I W (e, x, t): data weighting

objective function

J =e

1

2W (KPBm d) 2

I W (e, x, t): data weighting

example

Marmousi

Marmousi

recorded dz

recorded dz

Volve data

I 240 receivers

I 242 shots

I Models: Vp, Vs , ,

I frequency: 0 15Hz

Volve data

I 240 receivers

I 242 shots

I Models: Vp, Vs , ,

I frequency: 0 15Hz

Volve data

I 240 receivers

I 242 shots

I Models: , , ,

I frequency: 0 15Hz

model

model

bandpass

3D to 2D compensation

data weighting

data

@@@R

@@@R

elastic RTM image

@@@R

@@@R

elastic LSRTM image

@@@R

@@@R

elastic RTM image

elastic LSRTM image

vertical component: observation

vertical component: prediction with & image

@@@R

vertical component: data misfit

@@@R

summary

elastic imaging conditionelastic model perturbationno polarity reversal

elastic LSM methodhigher resolutionimproved amplitudes

acknowledgement

d

Statoil ASA and the Volve license partnersExxonMobil E&P Norway ASBayerngas Norge AS

d

dd Disclaimer: The views expressed in this presentation are the views of the authors anddo not necessarily reflect the views of Statoil ASA and the Volve field license partners.

vertical component: prediction with image

vertical component: prediction with image