Imaging Conditions for Primary Reflections and for Multiple Reflections

1
Imaging Conditions for Primary Reflections and
for Multiple Reflections
Jianming Sheng, Hongchuan Sun, Yue Wang and
Gerard T. Schuster
University of Utah
2
Outline

• Introduction

• Primary-Only Imaging Condition

• Multiple-Only Imaging Condition

• Conclusions

3
Introduction
Other de-multiple methods (prior to migration
imaging) (1) Exploit moveout differences (2)
Predict and subtract multiples
4
Introduction
Our two new approaches (during migration imaging)
(1) Primary-Only Imaging Condition
(1) POIC
Migrate primary reflections Discard
multiple reflections
(2) MOIC
(2) Multiple-Only Imaging Condition
Migrate multiple reflections Discard
primary reflections
5
Outline

• Introduction

• Primary-Only Imaging Condition

• Multiple-Only Imaging Condition

• Conclusions

6
Primary-Only Imaging Condition

• Methodology
• Synthetic Data Example
• Unocal Field Data Example

7
Forward Modeling
Primary
Multiple
S
S
R
R
Depth
Offset
Offset
8
Forward Modeled Data
Primary
Multiple
Data
P1
P1

Time (s)
Offset
Offset
Offset
9
Problem in Kirchhoff Migration
Data ( primary multiple )
Standard imaging condition
Image ( primary multiple )
10
Objective of POIC Migration
Data ( primary multiple )
Primary-only imaging condition
Image ( primary multiple )
11
Migration with POIC
Key Steps (1) pick seismic events
automatically ?obs
12
Migration with POIC
13
Migration with POIC
S
R
?
Key Steps (3) Shoot ray from the source using
shooting angle ?
Depth
Offset
14
Migration with POIC
S
R
Key Steps (4) Shoot ray from the receiver using
incidence angle ?
?
Depth
Offset
15
Migration with POIC
S
R
Key Steps (5) Find the crossing point P, whose
traveltime is ?SP ?RP
?
?
Depth
P
Offset
16
POIC Constraint
An event is a primary reflection only if
?obs ?SP ?RP Primary
reflections are migrated
calculated
picked
17
Multiple Reflection
S
R
?
?
Depth
P
Offset
18
Multiple Reflection
An event is a multiple reflection if
?obs ?SP ?RP Multiple reflections are
discarded
19
Migration with POIC
Data ( primary multiple )
Primary-Only Imaging Condition ?obs , ? and ?
Image ( primary multiple )
20
Primary-Only Imaging Condition

• Methodology
• Synthetic Data Example
• Unocal Field Data Example

21
5-Layer Model
A Shot Gather
0
P1
P2
Time (s)
P3
P4
4
Distance (km)
0
3
22
Kirchhoff Image
POIC Image
0
Multiple
Depth (km)
6
23
Primary-Only Imaging Condition

• Methodology
• Synthetic Data Example
• Unocal Field Data Example

24
Stack Before Multiple Removal
0
M1
Time (s)
M2
M2
4
313
1400
CDP Number
25
Stack After ?-p Multiple Removal
0
M1
Time (s)
M2
M2
4
313
1400
CDP Number
26
Kirchhoff Image
0
M1
M2
Depth (km)
M2
4
9
2
Distance (km)
27
POIC Image
0
M1
M2
Depth (km)
M2
4
9
2
Distance (km)
28
Outline

• Introduction

• Primary-Only Imaging Condition

• Multiple-Only Imaging Condition

• Conclusions

29
Multiple-Only Imaging Condition

• Methodology
• Nine-layered Model
• SEG/EAGE Salt Model

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Step1 Create crosscorrelograms
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Step2 Migrate crosscorrelograms
With Imaging Condition
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Key Idea of MOIC
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Key Idea of MOIC
True reflectors
Step3 Multiply the crosscorrelogram image by
the Kirchhoff image
34
Multiple-Only Imaging Condition

• Methodology
• Nine-layered Model
• SEG/EAGE Salt Model

35
Nine-Layered Model
Model
Crosscorrelogram image
Distance (km)
Distance (km)
3.0
3.0
0
0
36
Nine-Layered Model
Kirchhoff Image
Product Image
artifacts
Distance (km)
Distance (km)
3.0
3.0
0
0
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Multiple-Only Imaging Condition

• Methodology
• Nine-layered Model
• SEG/EAGE Salt Model

38
SEG/EAGE Salt Model
0
0.6
1.2
Depth (km)
1.8
2.4
3.0
3.6
0 5.0
10.0 15.0
Distance (km)
39
Crosscorrelogram Image
0
0.6
1.2
Depth (km)
1.8
2.4
3.0
3.6
0 5.0
10.0 15.0
Distance (km)
40
Kirchhoff Image
0
0.6
1.2
Depth (km)
1.8
2.4
3.0
3.6
0 5.0
10.0 15.0
Distance (km)
41
Product Image
0
0.6
1.2
Depth (km)
1.8
2.4
3.0
3.6
0 5.0
10.0 15.0
Distance (km)
42
Outline

• Introduction

• Primary-Only Imaging Condition

• Multiple-Only Imaging Condition

• Conclusions

43
Conclusions
POIC Multiples are effectively attenuated
during the imaging process
MOIC Multiples are considered as signal and
correctly imaged
44
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.
45
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.