Center for Subsurface
Imaging and Fluid Modeling
2010
Shuyu Sun and GT Schuster
8 PhD students, 5 Research Fellows
(Prof Sherif Hanafy, Dr. Chaiwoot B. et al.)
Bill Bosworth: PhD Colgate, Marathon 21 years,
Apache 5 years, senior research advisor Apache
Mike Zinger: BS Iowa State, Amoco 20 years,
10 years Aramco,Team Leader Red Sea Expl.
Shuyu Sun: PhD UT Austin, S. Carolina Univ., reservoir
simulation
Dinesh Kaushik: PhD, Gordon Bell Prize, algorithms
C. Boonyasiriwat: PhD, U of Utah, FWI and simulation
David Keyes: PhD Harvard, Columbia Univ.,Yale
Univ., Gordon Bell Prize, VP SIAM
Ibrahim Hoteit: PhD J. Fourier, Data assimilation
Raed Al Huseini: PhD, Economic Development
Great Appreciation
Mara Rovelli, Sabrina Percher, Marielaure Boulot,
Antonia Forshaw, Mirna Haydar, Mariam Fouad
Center for Subsurface
Imaging and Fluid Modeling
2010
Shuyu Sun and GT Schuster
8 PhD students, 5 Research Fellows
(Prof Sherif Hanafy, Dr. Chaiwoot B. et al.)
Center for Subsurface Imaging and
Fluid Modeling (CSIM) Consortium
• Goal: Develop innovative
•
computational methods for seismic
imaging and subsurface fluid flow modeling. Examples
include 3D waveform inversion, 3D RTM, TI modeling,
reservoir fluid simulator.
Advantages More than $1,500,000/yr in KAUST research
funds, tightly coupled visualization+supercomputer resources
+ reservoir fluid modeling+ seismic imaging
Computers: IBM Blue Gene 225 Tflop, Intel+GPU Clusters
:
•
GPU+IBM experts
• Benefits: Yearly Houston meeting, annual reports, access to
student interns, expert in fluid flow modeling, seismic, and
eventually EM imaging
• Collaborations: UT Austin (Stoffa+TTI), UU (GPU)
Research Goals
G.T. Schuster (Columbia Univ., 1984)
Seismic Interferometry: VSP, SSP, OBS
Multisource+Preconditioned RTM+MVA+Inversion+Modeling:
Seismic Lab: >630 Channel capacity, resisitivity
TTI 3D RTM, GPU: Stoffa+CSIM, UUtah K. Johnson SCI, PSU, KAUST
Cornea
Shaheen
Research Goals
Shuyu Sun (UT Austin, 2005)
Modeling of multiphase flow in porous media
(new approaches for fractures, diffusion, capillarity …)
Advanced finite element methods
(dynamic mesh adaption, multiscale resolution,
element-wise conservation, efficient linear solvers, …)
Computational thermodynamics of reservoir fluid
2010 CSIM Consortium
($25 K/year)
Inaugural Members: Aramco, Exxon, Chevron,
BP, Petrobras, GXT, PEMEX
Annual Meeting: Houston Jan. 2011
Midyear Report: Summer 2010
Software Policy: Same as UTAM for Schuster
Shuyu Sun Policy
http://utam.gg.utah.edu/csim
Multisource Seismic
Imaging
vs
CPU Speed vs Year
100000
10000
1000
copper
Aluminum
100
VLIW
Superscalar
10
RISC
1
1970
1980
1980
1990
2000
Year
2010
2020
Motivation for Better Seismic Imaging Strategy
Jack
Buckskin
¼ billion $$$ well
Kaskida
Tiber
35,055 Feet
FWI Problem & Possible Soln.
• Problem: FWI computationally costly
• Solution: Multisource Encoded FWI
Preconditioning speeds up by factor 2-3
Iterative encoding reduces crosstalk
Multisource Phase Encoded Imaging
{
Forward Model:
L
{
d
d +d =[L +L ]m
1
2
1
2
mmig=LTd
Multisource Migration:
T
T
=[L +L ](d + d )
1
(k+1)
(k)
2
T
1
T
m = m + = L d +L d +
1
1
2
2
Standard migration
2
T
T
L 1d 2+L2d1
Crosstalk noise
Multisource S/N Ratio
d 1 , d 2 , ….
d1 +d 2 +….
LT [d1 + d2 +.. ]
LT [d 1+ d 2 + … ]
# CSGs
# geophones/CSG
Multisrc. Migration vs Standard Migration
# geophones/CSG
# CSGs
MS ~ M vs
~
S-1
MS
Iterative Multisrc. Migration vs Standard Migration
# iterations
MI
vs
MS
Crosstalk Term
T
T
L 1d 2+L2d1
Time Statics
Time+Amplitude Statics
QM Statics
Summary
T
T
L 1d 2+L2d1
Time Statics
1. Multisource crosstalk term analyzed analytically
2. Crosstalk decreases with increasing w, randomness,
Time+Amplitude Statics
dimension, iteration #, and decreasing depth
3. Crosstalk decrease can now be tuned
QMdetailed
Statics analysis and testing needed to refine
4. Some
predictions.
Multisource Technology
• Fast Multisource Least Squares Kirchhoff Mig.
• Multisource Waveform Inversion (Ge Zhan)
3
Z k(m)
0
The Marmousi2 Model
0
X (km)
16
The area in the white box is used for S/N calculation.
Z k(m)
0
Conventional Source: KM vs LSM (50 iterations)
3
KM (1x)
X (km)
16
Z (km)
0
0
3
LSM (100x)
0
X (km)
16
Z k(m)
0
200-source Supergather: KM vs LSM (300 its.)
3
KM (1/200x)
X (km)
16
Z (km)
0
0
3
LSM (33x)
0
X (km)
16
S/N =
MI
0
S/N
7
The S/N of MLSM image grows as the square root of
the number of iterations.
1
I
300
Multisource Technology
• Fast Multisource Least Squares Migration ( Dai)
• Multisource Waveform Inversion (Boonyasiriwat)
Multisource Encoded FWI
Nd +Nd =[NL +NL ]m
Forward Model:
1
Multisource Migration:
Multisrc-Least FWI:
1
2
2
1
1
2
2
mmig=LTd
m =[LTL]-1LTd
multisource preconditioner
Preconditioned
m’ = m -f LT[Lm - d]
f ~ [LTL]-1
Steepest Descent
Multiscale Waveform Tomography
1. Collect data d(x,t)
syn.
2. Generate synthetic data d(x,t) by FD method
3. Adjust v(x,z) until ||d(x,t)-d(x,t)syn.|| 2 minimized by CG.
4. To prevent getting stuck in local minima:
a). Invert early arrivals initially
mute
b). Use multiscale: low freq.
high freq.
7
Boonyasiriwat et al., 2009, TLE
0 km
6 km/s
6 km
3 km/s
0 km
20 km
Waveform Tomograms
0 km
6 km/s
Initial model
6 km
0 km
3 km/s
6 km/s
5 Hz
6 km
3 km/s
0 km
6 km/s
10 Hz
6 km
3 km/s
0 km
6 km/s
20 Hz
6 km
0 km
3 km/s
20 km
Data Pre-Processing
3D-to-2D conversion
Attenuation compensation
Random noise removal
17
Source Wavelet Estimation
Generate a stacked section
Pick the water-bottom
Stack along the water-bottom to obtain an estimate of
source wavelet
In some cases, source wavelet inversion can be used.
17
Gradient Computation and Inversion
Multiscale inversion: low to high frequency
Dynamic early-arrival muting window
Normalize both observed and calculated data within the same
shot
Quadratic line search method (Nocedal and Wright, 2006)
A cubic line search can also be used.
17
Low-pass Filtering
(b)0-15
5-HzHz
CSG
(b)
CSG
(c)0-25
10-Hz
(c)
HzCSG
CSG
0
0
0.5
0.5
0.5
1
1
1
1.5
1.5
1.5
2
2.5
Time (s)
0
Time (s)
Time (s)
(a) Original CSG
2
2.5
2
2.5
3
3
3
3.5
3.5
3.5
4
0
2
4
Offset (km)
4
0
2
4
Offset (km)
4
0
2
4
Offset (km)
18
Dynamic Early-Arrival Muting Window
Window = 1 s
CSG(a) Original CSG(b) 5-Hz CSG
0
0
0
5
0.5
0.5
1
1
1
1
1.5
1.5
1.5
1.5
2
2.5
2
2.5
2
2.5
Time (s)
5
2
0.5
Time (s)
1
(b) 5-Hz CSG (c) 10-Hz CSG (c) 10-Hz CSG
0
0
0-15 Hz CSG
Time (s)
0.5
Time (s)
5
Window = 1 s
2
2.5
3
3
3
3
3
5
3.5
3.5
3.5
3.5
4
40
m)
4
0
2
4
Offset (km)
4
2
40
Offset (km)
4
04
2
Offset (km)
0-25 Hz CSG
4
2
40
Offset (km)
2
4
Offset (km)
19
Dynamic Early-Arrival Muting Window
Window = 2 s
CSG(a) Original CSG(b) 5-Hz CSG
0
0
0
5
0.5
0.5
1
1
1
1
1.5
1.5
1.5
1.5
2
2.5
2
2.5
2
2.5
Time (s)
5
2
0.5
Time (s)
1
(b) 5-Hz CSG (c) 10-Hz CSG (c) 10-Hz CSG
0
0
0-15 Hz CSG
Time (s)
0.5
Time (s)
5
Window = 2 s
2
2.5
3
3
3
3
3
5
3.5
3.5
3.5
3.5
4
40
m)
4
0
2
4
Offset (km)
4
2
40
Offset (km)
4
04
2
Offset (km)
0-25 Hz CSG
4
2
40
Offset (km)
2
4
Offset (km)
19
Traveltime Tomogram
Results
Depth (km)
0
Velocity (m/s)
3000
2.5
Waveform Tomogram
Depth (km)
0
2.5
1500
0
X (km)
20
20
3000
Waveform Tomogram
Depth (km)
Velocity (m/s)
0
1500
2.5
Vertical Derivative of Waveform Tomogram
Depth (km)
0
2.5
0
X (km)
20
21
Kirchhoff Migration Images
22
Kirchhoff Migration Images
22
Comparing CIGs
23
Comparing CIGs
CIG from Traveltime Tomogram
CIG from Waveform Tomogram
24
Comparing CIGs
25
Comparing CIGs
CIG from Traveltime Tomogram
CIG from Waveform Tomogram
26
Comparing CIGs
27
Comparing CIGs
CIG from Traveltime Tomogram
CIG from Waveform Tomogram
28
Multi-Source Waveform Inversion Strategy
(Ge Zhan)
144 shot gathers
Generate multisource field data with
known time shift
Initial velocity model
Generate synthetic multisource data
with known time shift from estimated
velocity model
Multisource deblurring filter
Using multiscale, multisource CG
to update the velocity model with
regularization
3D SEG Overthrust Model
(1089 CSGs)
15 km
3.5 km
15 km
Numerical Results
Dynamic QMC Tomogram
(99 CSGs/supergather)
Static QMC Tomogram
(99 CSGs/supergather)
3.5 km
Dynamic Polarity Tomogram
(1089 CSGs/supergather)
15 km
Multisource FWI Summary
(We need faster migration algorithms & better velocity models)
Stnd. FWI
Multsrc. FWI
IO
1
vs
Cost
1
vs
Sig/MultsSig
Resolution dx
1/20
1/20 or better
?
1
vs
1
Multisource FWI Summary
(We need faster migration algorithms & better velocity models)
Future: Multisource MVA, Interpolation,
Field Data, Migration Filtering, LSM