computational challenges for finding big oil by seismic inversion

Computational Challenges Computational Challenges for Finding Big Oil by for Finding Big Oil by

Seismic InversionSeismic Inversion

JackJackBuckskinBuckskin

KaskidaKaskidaTiberTiber

35,055 Feet

Motivation for Better Seismic Imaging StrategyMotivation for Better Seismic Imaging Strategy

¼ billion $$$ well¼ billion $$$ well

Motivation for Better Seismic Imaging StrategyMotivation for Better Seismic Imaging StrategyOil Well BlowoutsOil Well Blowouts

OverpressureZone

Motivation for Better Seismic Imaging StrategyMotivation for Better Seismic Imaging StrategyOil Well BlowoutsOil Well Blowouts

= Low Seismic Velocity Zone

Motivation for Better Seismic Imaging StrategyMotivation for Better Seismic Imaging StrategyMud VolcanoesMud Volcanoes

6.3 km2

13 people killed 30,000 people displacedMay 29, 2006

• Computational Challenge Seismic InversionComputational Challenge Seismic Inversion

OutlineOutline

• Full waveform InversionFull waveform Inversion

• Multisource InversionMultisource Inversion

Given: Given: dd = L= LmmSeismic Inverse ProblemSeismic Inverse Problem

Find: Find: m(x,y,z)m(x,y,z)

Soln: min || LSoln: min || Lmm--dd || ||22

mm = [L L] L = [L L] L ddTT TT-1-1

L L ddTT

migrationmigration

waveformwaveforminversioninversion

Given: Given: dd = L= LmmComputational ChallengesComputational Challenges

Find:Find:mm = [L L] L = [L L] L ddTT TT-1-1

20x20x10 km3

dx=1 m

# time steps ~ 10# time steps ~ 1044

# shots > 10# shots > 1044

m > 10m > 10 unknown velocity valuesunknown velocity values

10101515Total =Total =

d > 10d > 1077

1313wordswords


OutlineOutline



Multisource Migration:Multisource Migration: mmmigmig=L=LTTdd

Forward Model:Forward Model:

m =[Lm =[LTTL]L]-1-1LLTTddMultisrc-Least FWI:Multisrc-Least FWI:

Multisource Encoded FWIMultisource Encoded FWI

m’ = m - Lm’ = m - LTT[Lm - d][Lm - d]

f ~ [Lf ~ [LTTL]L]-1-1

ff Steepest DescentSteepest Descent

PreconditionedPreconditioned

Nd +Nd =[Nd +Nd =[NL +NL ]mL +NL ]m11 222211 2211 11 22

multisource preconditionermultisource preconditioner

Multiscale Waveform TomographyMultiscale Waveform TomographyMultiscale Waveform TomographyMultiscale Waveform Tomography

1. Collect data d(x,t)1. Collect data d(x,t)

2. Generate synthetic data d(x,t) by FD method2. Generate synthetic data d(x,t) by FD methodsynsyn..

3. Adjust v(x,z) until ||d(x,t)-d(x,t) || minimized by CG.3. Adjust v(x,z) until ||d(x,t)-d(x,t) || minimized by CG.synsyn.. 22

4. To prevent getting stuck in local minima:4. To prevent getting stuck in local minima: a). Invert early arrivals initiallya). Invert early arrivals initially

mute

7

b). Use multiscale: low freq. high freq.b). Use multiscale: low freq. high freq.

0 km0 km 20 km20 km

0 km0 km

6 km6 km 3 km/s3 km/s

6 km/s6 km/s

Boonyasiriwat et al., 2009, TLEBoonyasiriwat et al., 2009, TLE

3 km/s3 km/s

6 km/s6 km/s

Initial modelInitial model

5 Hz5 Hz

10 Hz10 Hz

20 Hz20 Hz

Waveform TomogramsWaveform Tomograms

3 km/s3 km/s

6 km/s6 km/s

3 km/s3 km/s

6 km/s6 km/s

3 km/s3 km/s

6 km/s6 km/s

0 km0 km

6 km6 km

0 km0 km

6 km6 km

0 km0 km

6 km6 km

0 km0 km

0 km0 km 20 km20 km

6 km6 km

Low-pass FilteringLow-pass Filtering

18

Offset (km)

Tim

e (s)

(a) Original CSG

0 2 4

0

0.5

1

1.5

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2.5

3

3.5

4

Offset (km)

Tim

e (s)

(b) 5-Hz CSG

0 2 4

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Offset (km)Tim

e (s)

(c) 10-Hz CSG

0 2 4

0

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(b) 0-15 Hz CSG (c) 0-25 Hz CSG

Dynamic Early-Arrival Muting WindowDynamic Early-Arrival Muting Window

19

Offset (km)

Tim

e (s)

(a) Original CSG

0 2 4

0

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1

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Tim

e (s)

(b) 5-Hz CSG

0 2 4

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Offset (km)

Tim

e (s)

(c) 10-Hz CSG

0 2 4

0

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1

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0-15 Hz CSG

Offset (km)

Tim

e (s)

(a) Original CSG

0 2 4

0

0.5

1

1.5

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Tim

e (s)

(b) 5-Hz CSG

0 2 4

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Offset (km)

Tim

e (s)

(c) 10-Hz CSG

0 2 4

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0-25 Hz CSG

Window = 1 s Window = 1 s

19

Offset (km)

Tim

e (s)

(a) Original CSG

0 2 4

0

0.5

1

1.5

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Tim

e (s)

(b) 5-Hz CSG

0 2 4

0

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1

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Offset (km)

Tim

e (s)

(c) 10-Hz CSG

0 2 4

0

0.5

1

1.5

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4

0-15 Hz CSG

Offset (km)

Tim

e (s)

(a) Original CSG

0 2 4

0

0.5

1

1.5

2

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3

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Offset (km)

Tim

e (s)

(b) 5-Hz CSG

0 2 4

0

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1

1.5

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Offset (km)

Tim

e (s)

(c) 10-Hz CSG

0 2 4

0

0.5

1

1.5

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0-25 Hz CSG

Window = 2 s Window = 2 s

Dynamic Early-Arrival Muting WindowDynamic Early-Arrival Muting Window

2000 20202.52.5

00

Dep

th (

km)

Dep

th (

km)

X (km)X (km)

Traveltime TomogramTraveltime Tomogram

15001500

30003000

Vel

ocity

(m

/s)

Vel

ocity

(m

/s)

Waveform TomogramWaveform Tomogram

2.52.5

00

Dep

th (

km)

Dep

th (

km)

ResultsResults

2100 2020

2.52.5

00

Dep

th (

km)

Dep

th (

km)

X (km)X (km)

Waveform TomogramWaveform Tomogram

15001500

30003000

Vel

ocity

(m

/s)

Vel

ocity

(m

/s)

2.52.5

00

Dep

th (

km)

Dep

th (

km)

Vertical Derivative of Waveform TomogramVertical Derivative of Waveform Tomogram

Kirchhoff Migration ImagesKirchhoff Migration Images

22


OutlineOutline



1980

Multisource SeismicMultisource SeismicImagingImaging

vs

copper

VLIW

Superscalar

RISC

1970 1990 2010

1

100

100000

10

1000

10000

Aluminum

Year

202020001980

CPU Speed vs Year

FWI Problem & Possible Soln.FWI Problem & Possible Soln.

• Problem:Problem: FWI computationally costly FWI computationally costly

• Solution:Solution: Multisource Encoded FWI Multisource Encoded FWI

Preconditioning speeds up by factor 2-3Preconditioning speeds up by factor 2-3

Iterative encoding reduces crosstalkIterative encoding reduces crosstalk

Multisource Migration:Multisource Migration: mmmigmig=L=LTTdd

Forward Model:Forward Model:

Multisource Phase Encoded ImagingMultisource Phase Encoded Imaging

d +d +dd =[ =[L +L +LL ]m ]m11 222211

LL{dd{

=[=[L +L +LL ]( ](dd + + dd ) ) 11 222211

TT TT

= = L d +L d +L dL d + + 11 222211

TT TT

LL dd + +L L dd22 112211

Crosstalk noiseCrosstalk noiseStandard migrationStandard migration

TT TT

m = m +(k+1) (k)

Multi-Source Waveform Inversion StrategyMulti-Source Waveform Inversion Strategy(Ge Zhan) (Ge Zhan)

Generate multisource field data with known time shift

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

Initial velocity model

144 shot gathers144 shot gathers

3D SEG Overthrust Model(1089 CSGs)

15 km

3.5 km

15 km

3.5 km

Dynamic QMC TomogramDynamic QMC Tomogram (99 CSGs/supergather)(99 CSGs/supergather)

Static QMC TomogramStatic QMC Tomogram(99 CSGs/supergather)(99 CSGs/supergather)

15 km

Dynamic Polarity TomogramDynamic Polarity Tomogram(1089 CSGs/supergather)(1089 CSGs/supergather)

Numerical ResultsNumerical Results

Multisource FWI SummaryMultisource FWI Summary(We need faster migration algorithms & better velocity models)(We need faster migration algorithms & better velocity models)

IO 1 vs 1/20

Cost 1 vs 1/20 or better

Resolution dx 1 vs 1

Sig/MultsSig ?

Stnd. FWI Multsrc. FWIStnd. FWI Multsrc. FWI

Multisource FWI SummaryMultisource FWI Summary(We need faster migration algorithms & better velocity models)(We need faster migration algorithms & better velocity models)

Future: Multisource MVA, Interpolation, Future: Multisource MVA, Interpolation, Field Data, Migration Filtering, LSM Field Data, Migration Filtering, LSM

Research GoalsResearch GoalsG.T. Schuster (Columbia Univ.,G.T. Schuster (Columbia Univ., 1984)1984)

Seismic Interferometry: VSP, SSP, OBSSeismic Interferometry: VSP, SSP, OBS

Multisource+Preconditioned RTM+MVA+Inversion+Modeling: Multisource+Preconditioned RTM+MVA+Inversion+Modeling:

TTI 3D RTM, GPU: TTI 3D RTM, GPU: Stoffa+CSIM, UUtah K. Johnson SCI, PSU, KAUSTStoffa+CSIM, UUtah K. Johnson SCI, PSU, KAUST

ShaheenShaheen

CorneaCornea

Multisource S/N RatioMultisource S/N Ratio

# geophones/CSG# geophones/CSG

# CSGs# CSGs

L [d + d +.. ]1 221

d +d T d , d 2211

L [d + d + … ]1 2

T , …. +….

Multisrc. Migration vs Standard Migration

# iterations# iterations

Iterative Multisrc. Migration vs Standard Migration

vs

vs

MSMSS-1

M~~

# geophones/CSG# geophones/CSG # CSGs# CSGs

MSMI

Crosstalk TermCrosstalk Term

Time Statics

Time+Amplitude Statics

QM Statics

LL dd + +L L dd22 112211

TT TT

SummarySummary

Time Statics

Time+Amplitude Statics

QM Statics

1. Multisource crosstalk term analyzed analytically1. Multisource crosstalk term analyzed analytically

2. Crosstalk decreases with increasing 2. Crosstalk decreases with increasing , randomness, , randomness, dimension, iteration #, and decreasing depthdimension, iteration #, and decreasing depth

3. Crosstalk decrease can now be tuned3. Crosstalk decrease can now be tuned

4. Some detailed analysis and testing needed to refine 4. Some detailed analysis and testing needed to refine predictions.predictions.

LL dd + +L L dd22 112211

TT TT

• Fast Multisource Least Squares Fast Multisource Least Squares Kirchhoff Mig.Kirchhoff Mig.

• Multisource Waveform Inversion (Ge Zhan)Multisource Waveform Inversion (Ge Zhan)

Multisource TechnologyMultisource Technology

0Z

k(m

)3

0 X (km) 16

The Marmousi2 Model

The area in the white box is used for S/N calculation.

0 X (km) 16

0Z

k(m

)3

0Z

(k

m)

3

0 X (km) 16

Conventional Source: KM vs LSM (50 iterations)

LSM (100x)

KM (1x)

0 X (km) 16

0Z

k(m

)3

0Z

(k

m)

3

0 X (km) 16

200-source Supergather: KM vs LSM (300 its.)

LSM (33x)

KM (1/200x)

S/N

0

1 I300

S/N =7

The S/N of MLSM image grows as the square root of the number of iterations.

MI

• Fast Multisource Least Squares Migration ( Dai)Fast Multisource Least Squares Migration ( Dai)

• Multisource Waveform Inversion (Boonyasiriwat)Multisource Waveform Inversion (Boonyasiriwat)

Multisource TechnologyMultisource Technology

Comparing CIGsComparing CIGs

23


24

CIG from Traveltime Tomogram CIG from Waveform Tomogram


25


26



27


28


17

Data Pre-ProcessingData Pre-Processing

3D-to-2D conversion3D-to-2D conversion

Attenuation compensationAttenuation compensation

Random noise removalRandom noise removal

17

Source Wavelet EstimationSource Wavelet Estimation

Pick the water-bottomPick the water-bottom

Stack along the water-bottom to obtain an estimate ofStack along the water-bottom to obtain an estimate ofsource waveletsource wavelet

Generate a stacked sectionGenerate a stacked section

In some cases, source wavelet inversion can be used.In some cases, source wavelet inversion can be used.

17

Gradient Computation and InversionGradient Computation and Inversion

Multiscale inversion: low to high frequencyMultiscale inversion: low to high frequency

Dynamic early-arrival muting windowDynamic early-arrival muting window

Normalize both observed and calculated data within the sameNormalize both observed and calculated data within the sameshotshot

Quadratic line search method (Nocedal and Wright, 2006)Quadratic line search method (Nocedal and Wright, 2006)A cubic line search can also be used.A cubic line search can also be used.

computational challenges for finding big oil by seismic inversion

Documents

d d t d

multisource mva

multisource phase

ttfast multisource

seismic inversionmotivation

multisource crosstalk

migration filtering

multisource sn ratio