MVA - Migration Velocity Analysis note

Update on Tomography MVA Development
Shengwen Jin
© 2013 HALLIBURTON. ALL RIGHTS RESERVED.
Disclaimer
The following is intended to outline Landmark’s general product
direction. It is intended for informational purposes only and may
not be incorporated into any contract. It is not a commitment to
deliver any code, material or functionality and should not be
relied upon in making any purchasing decisions. The
development, release and timing of any features or functionality
described for Landmark’s products remains at the sole discretion
of Landmark.
© 2013 HALLIBURTON. ALL RIGHTS RESERVED.
2
Tomography MVA Development
 Objective
– Fill a gap in migration velocity updating for depth imaging processing
– Build a robust and user-friendly workflow for tomography MVA
 Timeline
– Kicked off the project in Q1 2013
– Will release the first version in January 2014 (Isotropic ray-based tomo)
• KDMIG offset image gathers
• Grid-based or layer-based
 Main features:
–
–
–
–
Tight integration with DecisionSpace via SesiSpaceLink
Structure tensor adoption, Hybrid model constrains
Anisotropy support
Wave equation tomography using RTM angle image gathers
© 2013 HALLIBURTON. ALL RIGHTS RESERVED.
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Basic Workflow for Ray-Based Tomography MVA
Shot gathers
Major components:
(0) Velocity model
(1)
KDMIG
 KDMIG
 Offset image gathers
(1) Stack image
volume
(6) Update velocity
w/ constraints
More iterations?
(1) CIG
image gathers
Picked horizons
(from DS)
(2) Compute
semblance
(4)
Ray-tracing
(3) Pick
depth residuals
(5)
Tomographic
inversion
 Compute semblance
 Grid-based volumetric
 Pick residuals
 Grid-based volumetric
 Parallel, JS table
 Tomographic inversion
 G-N solver
 Update velocity
Tight integration
w/ DecisionSpace
© 2013 HALLIBURTON. ALL RIGHTS RESERVED.
Final velocity
 Constraints
Obtain/Create An Initial Velocity




Existing velocity from a prior processing
Create a V(z) model in sediment
RMS converted via Dix formula
Manipulated from a prior velocity
model
– Remove/smooth through salt, salt
flooding
– Use well data to estimate anisotropic
parameters
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Step 1: Kirchhoff PSDM (KDMIG)
 Produce 3D depth image volume and CIG gathers
 QC image
– Overlay the stack, initial velocity (and horizons if available)
 QC CIGs
– Evaluate the gathers for noise, multiples especially in target area
– Determine and apply an additional mute that will optimize the stack and
semblance.
© 2013 HALLIBURTON. ALL RIGHTS RESERVED.
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QC CIGs
Stack image: Inline 7225
CIG & Semblance w/o mute
© 2013 HALLIBURTON. ALL RIGHTS RESERVED.
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CIG & Semblance w/ mute
QC CIGs
Stack image: Inline 4093
CIG & Semblance w/o mute
© 2013 HALLIBURTON. ALL RIGHTS RESERVED.
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CIG & Semblance w/ mute
Step 2: Compute Semblance
 Grid- (full volume) or horizon-based semblance
© 2013 HALLIBURTON. ALL RIGHTS RESERVED.
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Compute Semblance
 Grid-based semblance
© 2013 HALLIBURTON. ALL RIGHTS RESERVED.
10
Step 3: Auto-Pick Residuals on Semblance
 Pick grid-based volumetric residuals
 Re-engineer the 3D Autopicker tool
– Parallelization
– JS table output
– Auto-pick residuals along horizons
 QC displays
– Stack overlain w/ active horizons in both 2D
and 3D View
– Interactive moveout correction on gathers
(gather flattening)
– On-the-fly 1D residual velocity associated
with the picking
© 2013 HALLIBURTON. ALL RIGHTS RESERVED.
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QC Plot of Picks and Semblance
© 2013 HALLIBURTON. ALL RIGHTS RESERVED.
12
Step 4: TomoMVA Raytracing
 Ray-tracing is conducted separately.
 Velocity smoothing is necessary.
 Local dip and azimuth at the image point
obtained from horizons or Structural
Tensors.
 QC rays
 Parameterization
–
–
–
–
Stencil size
Offset range, interval and tolerance
Azimuth range, interval and tolerance
Grouped horizons
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Smoothing of Velocity Model
No smoothing on velocity
100m x 100m smoothing
© 2013 HALLIBURTON. ALL RIGHTS RESERVED.
14
Ray-tracing QC w/ GNU plot
Constant offset: 4500m
© 2013 HALLIBURTON. ALL RIGHTS RESERVED.
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Ray-tracing Tool
© 2013 HALLIBURTON. ALL RIGHTS RESERVED.
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Step 5: Tomography Inversion
Tomographic Inversion Equation
ù
v ( rI ) é L(h)
- L ( h = 0)ú × Ds = Dz ( h)
ê
2 cosa ë cosg
û
Raypath lengths
Depth residuals
Updated slowness perturbation
Offset
2
1
Depth
Relative depth residual
KDMIG offset image gather
© 2013 HALLIBURTON. ALL RIGHTS RESERVED.
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Solver: Conjugate Gradient Algorithm
 Gauss-Newton (GN) method
 LSQR method
– Bill Harlan (2004) presented a generalized
inversion algorithm using G-N method.
– The standard approach to nonlinear
inversion in geophysics
– An iterative method for solving nonlinear
inversion problem
– Minimization of the non-quadratic objective
function using an iterated linearized inversion
scheme
– The memory needed scales linearly with the
number of data and model parameters with
transformations technique (Harlan, 2004)
© 2013 HALLIBURTON. ALL RIGHTS RESERVED.
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– The commonly used approach to linear
inversion in geophysics
– An iterative method for solving linear
inversion problem
– Minimization of the quadratic objective
function involving a large sparse and
ill-conditioned matrix
– Loading entire sparse matrix into
memory dominates the memory
requirements
Step 6: TomoMVA Post-Inversion Updating
 Update velocity after
tomographic inversion
– Smoothing updated
perturbation of velocity before
adding to initial velocity (V = V0
+ ΔV)
– Constrain with horizons, hybrid
models, and well data, etc.
© 2013 HALLIBURTON. ALL RIGHTS RESERVED.
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Preliminary Testing
3000 m/s
4000 m/s
5000 m/s
Initial Velocity
(10% higher)
1st Iteration
2nd Iteration
© 2013 HALLIBURTON. ALL RIGHTS RESERVED.
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True Velocity
Preliminary Testing
Initial Velocity
(10% higher)
1st Iteration
2nd Iteration
© 2013 HALLIBURTON. ALL RIGHTS RESERVED.
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True Velocity
Packaging
 Major components for LDI TomoMVA
–
–
–
–
–
TomoMVA Semblance Computing
TomoMVA Residual Autopicker
TomoMVA Raytracing
TomoMVA Inversion
TomoMVA Post-Inversion Updating
 Look for partners to test on real data
© 2013 HALLIBURTON. ALL RIGHTS RESERVED.
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Wave Equation Tomography MVA
Shot gathers
Shot gathers
Velocity model
More iterations?
RTM
migration
Velocity model
KDMIG
Stacked image
volume
Angle image
gathers
Stacked image
volume
Offset image
gathers
Pick horizons
as constraints
Generate
semblance
Pick horizons
as constraints
Generate
semblance
Compute
Sensitivity Kernel
Measure
depth residuals
Ray-tracing
Measure
depth residuals
More iterations?
Tomography
inversion
Tomography
inversion
Final updated
velocity model
Final updated
velocity model
© 2013 HALLIBURTON. ALL RIGHTS RESERVED.
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Sensitivity Kernel Based Wave Path vs. Ray Path
•
•
•
•
Sensitivity kernel is a kind of phase spectrum. The range of non-zero values in the kernel reveals
the region where velocity perturbation may exist.
The value of kernel quantitatively reveals the velocity perturbation for a subsurface image point.
The polarity of kernel reveals the wave propagation faster or slower with the migration velocity as
compared with the waves in the medium with true velocity.
Fresnel zone
© 2013 HALLIBURTON. ALL RIGHTS RESERVED.
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Update Sediment Velocity (Sigsbee2a Dataset)
Initial velocity
1500
Step 1 - Update sediment velocity
4500
© 2013 HALLIBURTON. ALL RIGHTS RESERVED.
25
Salt Flood
Step 2 - Update top of salt and Flood salt
1500
Step 3 – Update base of salt
4500
© 2013 HALLIBURTON. ALL RIGHTS RESERVED.
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Improve Subsalt Velocity
Step 4 – Improve subsalt velocity
1500
True velocity
4500
© 2013 HALLIBURTON. ALL RIGHTS RESERVED.
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Angle Image Gathers
A
A: Initial
Updated
True
B: Initial
Updated
True
C: Initial
Updated
True
D: Initial
Updated
True
B
C
D
© 2013 HALLIBURTON. ALL RIGHTS RESERVED.
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TomoMVA on SEAM Model
Initial velocity
1st iteration
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2nd iteration
TomoMVA on SEAM Model
Initial velocity
Updated velocity
© 2013 HALLIBURTON. ALL RIGHTS RESERVED.
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True velocity
TomoMVA on SEAM Model
A
0
C
Opening angle (degree)
40
Initial
B
Updated
True
Initial
Updated
True
Initial
Updated
7
A
B
© 2013 HALLIBURTON. ALL RIGHTS RESERVED.
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C
True
Questions?
Shot gathers
(0) Velocity model
(6) Update velocity
w/ constraints
More iterations?
Tight integration
w/ DecisionSpace
(1)
KDMIG
(1) Stack image
volume
(1) CIG
image gathers
Picked horizons
(from DS)
(2) Compute
semblance
(4)
Ray-tracing
(3) Pick
depth residuals
(5)
Tomographic
inversion
Final velocity
© 2013 HALLIBURTON. ALL RIGHTS RESERVED.
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