Transcript 3D ANISTROPIC TRAVELTIME SOLVER

Imaging Conditions for
Primary Reflections and for
Multiple Reflections
Jianming Sheng, Hongchuan Sun,
Yue Wang and Gerard T. Schuster
University of Utah
Outline
• Introduction
• Primary-Only Imaging Condition
• Multiple-Only Imaging Condition
• Conclusions
Introduction
Other de-multiple methods:
(prior to migration imaging)
(1) Exploit moveout differences
(2) Predict and subtract multiples
Introduction
Our two new approaches:
(during migration imaging)
(1) POIC
Primary-Only Imaging Condition:
Migrate primary reflections
Discard multiple reflections
(2) MOIC
Multiple-Only Imaging Condition:
Migrate multiple reflections
Discard primary reflections
Outline
• Introduction
• Primary-Only Imaging Condition
• Multiple-Only Imaging Condition
• Conclusions
Primary-Only Imaging Condition
• Methodology
• Synthetic Data Example
• Unocal Field Data Example
Forward Modeling
Primary
S
Multiple
S
R
Depth
R
Offset
Offset
Forward Modeled Data
Primary
Multiple
Time (s)
P1
P1
+
Offset
Data
M1
M1
M2
M2
M3
M3
Offset
Offset
Problem in Kirchhoff Migration
Data
( primary + multiple )
Standard imaging condition
Image
( primary + multiple )
Objective of POIC Migration
Data
( primary + multiple )
Primary-only imaging condition
Image
( primary + multiple )
Migration with POIC
Key Steps:
(1) pick seismic events automatically;
obs
Migration with POIC
Key Steps:
(2) calculate shooting angle  and
incidence angle  for event obs using
local slant stack;

Migration with POIC
S
(3) Shoot ray
from the
source using
shooting
angle ;

Depth
Key Steps:
Offset
R
Migration with POIC
S

Depth
Key Steps:
(4) Shoot ray
from the
receiver using
incidence
angle ;
R
Offset
Migration with POIC
S

 +
SP
RP

Depth
Key Steps:
(5) Find the
crossing
point P,
whose
traveltime is:
R
P
Offset
POIC Constraint
An event is a primary reflection
only if :
obs
picked
=


calculated
SP + RP
Primary reflections are migrated
Multiple Reflection
S
R

Depth

P
Offset
Multiple Reflection
An event is a multiple reflection
if :
obs
=
SP + RP
Multiple reflections are discarded
Migration with POIC
Data
( primary + multiple )
Primary-Only Imaging Condition
obs ,  and 
Image
( primary + multiple )
Primary-Only Imaging Condition
• Methodology
• Synthetic Data Example
• Unocal Field Data Example
5-Layer Model
A Shot Gather
0
Time (s)
Depth (km)
0
P1
P2
P3
P4
6
4
0
Distance (km)
5
0
Distance (km)
3
Kirchhoff Image
POIC Image
P1
P2
P1
P2
P3
P3
Depth (km)
0
Multiple
P4
P4
6
0
Distance (km)
5 0
Distance (km)
5
Primary-Only Imaging Condition
• Methodology
• Synthetic Data Example
• Unocal Field Data Example
Stack Before Multiple Removal
0
Time (s)
M1
M2
M2
4
313
CDP Number
1400
Stack After -p Multiple Removal
0
Time (s)
M1
M2
M2
4
313
CDP Number
1400
Kirchhoff Image
0
Depth (km)
M1
M2
M2
4
2
Distance (km)
9
POIC Image
0
Depth (km)
M1
M2
M2
4
2
Distance (km)
9
Outline
• Introduction
• Primary-Only Imaging Condition
• Multiple-Only Imaging Condition
• Conclusions
Multiple-Only Imaging Condition
• Methodology
• Nine-layered Model
• SEG/EAGE Salt Model
Step1: Create crosscorrelograms
Primary
S
G’
Ghost
Primary
S
G
G’
G
G
S
P
G’
VIRTUAL
SOURCE
X
X
X’
X
X’
X’
G
Step2: Migrate crosscorrelograms
With Imaging Condition
mx   e
Migration image
Trial image point
 i  G ' x  xG 
Key Idea of MOIC
Three-layered
Model
Crosscorrelogram
Image
Kirchhoff Image
Artifacts
Reflector
Reflector
Artifacts
Key Idea of MOIC
Step3:
True
reflectors
Multiply the
crosscorrelogram
image by the
Kirchhoff image
Multiple-Only Imaging Condition
• Methodology
• Nine-layered Model
• SEG/EAGE Salt Model
Nine-Layered Model
Model
Crosscorrelogram image
0
0.6
1.2
1.8
2.4
3.0
0
Distance (km)
3.0
0
Distance (km)
3.0
Nine-Layered Model
Kirchhoff Image
Product Image
0
0.6
artifacts
1.2
1.8
2.4
3.0
0
Distance (km)
3.0
0
Distance (km)
3.0
Multiple-Only Imaging Condition
• Methodology
• Nine-layered Model
• SEG/EAGE Salt Model
SEG/EAGE Salt Model
0
0.6
1.2
1.8
2.4
3.0
3.6
0
5.0
10.0
Distance (km)
15.0
Crosscorrelogram Image
0
0.6
1.2
1.8
2.4
3.0
3.6
0
5.0
10.0
Distance (km)
15.0
Kirchhoff Image
0
0.6
1.2
1.8
2.4
3.0
3.6
0
5.0
10.0
Distance (km)
15.0
Product Image
0
0.6
1.2
1.8
2.4
3.0
3.6
0
5.0
10.0
Distance (km)
15.0
Outline
• Introduction
• Primary-Only Imaging Condition
• Multiple-Only Imaging Condition
• Conclusions
Conclusions
POIC:
Multiples are effectively attenuated
during the imaging process
MOIC:
Multiples are considered as signal
and correctly imaged
Further Work
POIC:
1) Apply to other field data sets;
2) Develop more robust algorithms;
MOIC:
1) Attenuate crosscorrelogram artifacts;
2) Deal with high-order and internal multiples.
Acknowledgments
We thank the sponsors of University of
Utah Tomography and Modeling
/Migration (UTAM) Consortium for their
financial support . We are appreciative of
Yi Luo for his early insights into MOIC.