4C Mahogony Data Processing and Imaging by LSMF Method
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Transcript of 4C Mahogony Data Processing and Imaging by LSMF Method
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4C Mahogony Data Processing 4C Mahogony Data Processing and Imaging by LSMF Methodand Imaging by LSMF Method
Jianhua Yu and Yue WangJianhua Yu and Yue Wang
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OutlineOutline• Motivation and ObjectiveMotivation and Objective
• LSMF MethodLSMF Method
• ExamplesExamples Graben ModelGraben Model Mahogany Field Data Mahogany Field Data
• SummarySummary
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OutlineOutline Motivation and ObjectiveMotivation and Objective
• LSMF MethodLSMF Method
• ExamplesExamples Graben ModelGraben Model Mahogany Field Data Mahogany Field Data
• SummarySummary
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Geological ObjectivesGeological Objectives
• Image Complex Structure Image Complex Structure • Detect Gas Reservoir OverDetect Gas Reservoir Over
SaltSalt
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ProblemsProblems • P-SV Conversion at ReflectorP-SV Conversion at Reflector ??
• How to GetHow to Get ““PurePure”” P-P and P-SVP-P and P-SV
• Strong Guided WavesStrong Guided Waves
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Problems for F-KProblems for F-K
Use only wave Use only wave moveoutmoveout
Strong guided Strong guided waveswaves
Near offset Near offset distortion distortion
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P-PP-PSourceSource
Point Scatterer Point Scatterer
P-SV P-SV
P-P and P-SV WavesP-P and P-SV Waves
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Particle Motion Particle Motion DirectionDirection++
SeparationSeparation
Tim
eT
ime
offsetoffset
MoveoutMoveout
Least Squares Migration Filtering Least Squares Migration Filtering
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ObjectiveObjective• Separate P-P & P-SSeparate P-P & P-S
• Suppress Guide WavesSuppress Guide Waves• Improve Migration ImageImprove Migration Image
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OutlineOutline• Motivation and ObjectiveMotivation and Objective
• LSMF MethodLSMF Method
• ExamplesExamples Graben ModelGraben Model Mahogany Field Data Mahogany Field Data
• SummarySummary
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Observed dataObserved data = > D= > Dpp pp ++
OffsetOffset
Tim
eT
ime
P-P waveP-P wave
P-S waveP-S wave
LSMF MethodLSMF Method
LLp-s p-s mmp-sp-s
LLpp pp mmpppp
ReflectivtyReflectivtyModelingModelingOperatorOperator
DDp-sp-s
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ddpppp = L = Lppppmmpppp ddp-sp-s = L = Lp-p-ssmmp-sp-s
P-P waveP-P wave
OffsetOffset
Tim
eT
ime
P-S waveP-S wave
OffsetOffset
Tim
eT
ime
LSMF MethodLSMF Method
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Conjugate Gradient Method:Conjugate Gradient Method:
dLLLm
mTT
sp
pp 1)(
SP
L,PP
LL wherewhere
LSMF MethodLSMF Method
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LSMF MethodLSMF Method
Operators are constructed based on Operators are constructed based on moveout and particle-motion directionmoveout and particle-motion direction
The migration operators are the The migration operators are the transposes of the modeling operatorstransposes of the modeling operators
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OutlineOutline• Motivation and ObjectiveMotivation and Objective
• LSMF MethodLSMF Method
• ExamplesExamples Graben ModelGraben Model Mahogany Field Data Mahogany Field Data
• SummarySummary
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ExamplesExamples
• Graben Model Graben Model • Mahogony Field DataMahogony Field Data
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Graben Velocity ModelGraben Velocity Model
0
Dep
th (
m)
3000
50000 X (m)
V1=2000 m/sV2=2700 m/s
V3=3800 m/s
V4=4000 m/s
V5=4500 m/s
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FDFD Synthetic DataSynthetic Data
1.4
0
Tim
e (s
)
0 Offset (m)5000
0 Offset (m)5000
Horizontal ComponentHorizontal Component Vertical ComponentVertical Component
P-PP-P P-SP-S
P-SP-S P-PP-P
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LSMF SeparationLSMF Separation
1.4
0
Tim
e (s
)
0
Offset (m)
5000
0
Offset (m)
5000
Horizontal ComponentHorizontal Component Vertical ComponentVertical Component
P-SP-S P-PP-P
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F-K Filtering Separation F-K Filtering Separation
1.4
0
Tim
e (s
)T
ime
(s)
0
Offset (m)
5000
0
Offset (m)
5000
Horizontal ComponentHorizontal Component Vertical ComponentVertical Component
P-PP-P P-SP-S
P-SP-S P-PP-P
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Test Results Indicate:Test Results Indicate:
LSMF works well for separating LSMF works well for separating
P-P and P-SVP-P and P-SV
LSMF is superior to F-K filteringLSMF is superior to F-K filtering
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ExamplesExamples
• Graben Model Graben Model • Mahogony Field DataMahogony Field Data
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Acquisition SurveyAcquisition Survey
9 km
OBC
Shot Line
29 km
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Main Processing FlowMain Processing Flow
Geometry assignment, datuming and so onGeometry assignment, datuming and so on
Trace edit, noise elimination, dual-sensor summationTrace edit, noise elimination, dual-sensor summation
Amplitude RecoveryAmplitude Recovery
Static correction, (F-K filtering), multiple suppressionStatic correction, (F-K filtering), multiple suppression
LSMF, velocity analysis LSMF, velocity analysis
Migration Migration
Output Output
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0
Tim
e (s
)
725
4
Offset(m)
Raw CSGRaw CSG-750
Hydrophone componentHydrophone component
725Offset(m)-750
Vertical componentVertical component
Continuous events
Continuous events
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0
Tim
eT
ime
(s)
725
4
Offset(m)Offset(m)
Raw CSGRaw CSG-750
Radial componentRadial component
725Offset(m)Offset(m)
-750
Transverse componentTransverse component
Wormy events
Wormy events
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0
Tim
e (s
)T
ime
(s)
3750
4
X (m)X (m)
RawRaw CRGCRG0
Hydrophone componentHydrophone component
3750X (m)X (m)0
Vertical componentVertical component
Continuous events Continuous
events
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0
Tim
e (s
)
3750
4
X (m)
Raw CRGRaw CRG0
Radial component
3750X (m)
0
Transverse component
Continuous events Continuous
events
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Rough Estimate of Static ShiftRough Estimate of Static Shift
Station NumberStation Number
Sta
tic
shif
t (m
s)S
tati
c sh
ift
(ms)
-40 100
12
Receiver static
Shot static
Source Receiver
p s
Source Receiver
p s
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TheThe Shear static shifts existShear static shifts exist
These shifts mainly come from These shifts mainly come from receivers and one-way Shear path receivers and one-way Shear path from deeper reflector from deeper reflector
P-S waves originate from P-S waves originate from reflectorsreflectors
Data Analysis Indicates:Data Analysis Indicates:
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CRG1 Data before Using LSMFCRG1 Data before Using LSMF
CRG1 (Vertical component) CRG1 (Vertical component)
0
4
Guided wave and P-S
Tim
e (
s)T
ime
(s)
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CRG1 Data after Using F-K FilteringCRG1 Data after Using F-K Filtering
CRG1 (Vertical component) CRG1 (Vertical component)
0
4
Unwanted waves remain
Tim
e (
s)T
ime
(s)
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CRG1 Data after Using LSMFCRG1 Data after Using LSMF
CRG1 (Vertical component) CRG1 (Vertical component)
0
4
Less Noise remains
Tim
e (
s)T
ime
(s)
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Prestack Migration Image Prestack Migration Image With F-K SeparationWith F-K Separation
Tim
e (
s)T
ime
(s)
0
3.5
Midpoint (Km)Midpoint (Km) 4.60
c
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Prestack Migration Image Prestack Migration Image With LSMF SeparationWith LSMF SeparationT
ime
(s)
Tim
e (
s)0
3.5
Midpoint (Km)Midpoint (Km) 4.60
c
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A Zoom View of Box AA Zoom View of Box A T
ime
(s)
Tim
e (
s)
2.02.0
3.23.2
Midpoint (Km)Midpoint (Km)0.60.6 1.41.4
Midpoint (Km)Midpoint (Km)0.60.6 1.41.4
FK+Mig.FK+Mig. LSMF+Mig.LSMF+Mig.
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A Zoom View of Box CA Zoom View of Box CT
ime
(s)
Tim
e (
s)
0.20.2
0.80.8
Midpoint (Km)Midpoint (Km)3.43.4 4.64.6
Midpoint (Km)Midpoint (Km)3.43.4 4.64.6
FK+Mig.FK+Mig. LSMF+Mig.LSMF+Mig.
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OutlineOutline• Motivation and ObjectiveMotivation and Objective
• LSMF MethodLSMF Method
• ExamplesExamples Graben ModelGraben Model Mahogany Field Data Mahogany Field Data
• SummarySummary
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SummarySummary
• P-SV waves in Mahogony data P-SV waves in Mahogony data
originate from the deep reflectors originate from the deep reflectors
• LSMF gives better separation resultsLSMF gives better separation results
and and improves the migration image improves the migration image
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SummarySummary
• LSMF can eliminate unwanted noise, LSMF can eliminate unwanted noise,
such as guided wavessuch as guided waves
• LSMF has negative impact on the LSMF has negative impact on the
fidelity of data to some extentfidelity of data to some extent
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SummarySummary
• Multiple EliminationMultiple Elimination
• Prestack Depth Migration Prestack Depth Migration
• Converted Wave ImagingConverted Wave Imaging
Future ResearchFuture Research::
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AcknowledgementAcknowledgement
We are grateful to the 1999 sponsors We are grateful to the 1999 sponsors of the UTAM consortium for financial of the UTAM consortium for financial supportsupport