Imaging Every Bounce in a Multiple G. Schuster, J. Yu, R. He U of Utah

OUTLINE Why Migrate Multiples? Migrating every Bounce Numerical Results Summary.

Why Migrate Multiples? Wider Coverage Better Fold Better Vert. Res.

Motivation 1: Extend Coverage 3D Courtesy of B. Paulsson PGSI

Shot radius 160 level Receiver array Depth in Well: 4,000-12,000 ft 22,000 ft 20,000 ft 22,000 ft The 3D Image Volume from a Massive 3D VSP ® Survey Courtesy of B. Paulsson PGSI

3D View of the image volume around the 3D VSP well

0 8 km 0 (Zhang & McMechan 1997) 24 km Motivation 2: Peek Around Corners with Multiples

OUTLINE Why Migrate Multiples? Migrating every Bounce Numerical Results Summary.

Migrating Every Bounce 1. Predict Multiple Traveltimes from Data Primary Pick t(s,g’) Pick t(s,g’) sg’

Migrating Every Bounce 1. Predict Multiple Traveltimes from Data sg’g sg’gPrimarysg’g t(s,g’) + t(g’,g) t(s,g’,g) = min( ) Asakawa & Matsuoka, 2002

0 m 180 m 0.1 s 0.0 s Time (s) X (m) 3rd-order 2nd-order 1st-order primary Free-Surface Multiples & 2-Layer Model

Migrating Every Bounce 1. Predict Multiple Traveltimes from Data s g 2. Migrate Multiples Sum Data along Predicted T(s,x,g) d(g, t(s,g’) + t(g’,x,g’’) + t(g’’,g) ) g m(x) = Predicted from Data xg’g’’

Migrating Every Bounce 1. Predict Multiple Traveltimes from Data s g 2. Migrate Multiples Sum Data along Predicted T(s,x,g) d(g, t(s,x,g’) + t(g’,g’’) + t(g’’,g) ) g m(x) = x

Modeling Peglegs (Jakubowicz; Reshef, Keydar, Landa; Weglein, Gasparotto, et al). sg X y t(s1y) + t(x2g) - t(xoy) = t(s1o2g) 12 o ?PrimariesPegleg A B Choose x & y so incidence angles agree

Summary s g Migrate Multiples d(g, t(s,x,g’) + t(g’,g’’) + t(g’’,g) ) g m(x) = x model model

OUTLINE Why Migrate Multiples? Migrating every Bounce Numerical Results Summary.

d 0 km X 5 km 0 km Z 2.5 km 0 km Z 2.5 km m0 + m1 + m2 m0 m1m2 2-Layer Model: Migration 1 CSG

0 km X 5 km m0 + m1 m0 X z X z m0 vs m1: 2-Layer Migration Images Even Illumination

OUTLINE Why Migrate Multiples? Migrating every Bounce Numerical Results Summary.

One part of SMAART model Depth (ft) 0 30K Offset (ft)050.55K reflector 0 reflector 1 reflector 2 reflector 3

Time (s) (s) 0 9Geophone(#) Free-Surface Multiple

Time (s) (s) 0 9Geophone(#) Another Multiple

KM before de-multiple Depth (ft) 0 30K Offset (ft) K

KM after de-multiple Depth (ft) 0 30K Offset (ft) K

Multiple migration Depth (ft) 0 30K Offset (ft)050.55K Using multiple 02020

Multiple migration result Depth (ft) 0 30K Offset (ft) K

Multiple migration result Offset (ft) Depth (ft) Depth (ft) ,250 6,750 9,375

OUTLINE Why Migrate Multiples? Migrating every Bounce Numerical Results Summary.

Raw Data of CRG#15Ghosts of CRG# Time (s) 1000 ft 00

Raw Data of CRG#15Primary of CRG# Time (s) 1000 ft 00

Primary Image1st Ghost Image 0 1 Depth (kft) 445 ft 00

Primary Image1st Order Ghost Image 0 1 Depth (kft) 445 ft 00

Well & Primary ImageWell & 1st Ghost Image 0 1 Depth (kft) Offset=105ft

Well & Primary ImageWell & 1st Ghost Image 0 1 Depth (kft) Offset=200 ft

Summary Use data to design migration kernel Benefits: Better resol. & fold kernel Use Delft method predict multiples Test on field data

Primary Migration x Sum Data along Predicted T(g) Predicted by Ray Tracing

Multiple Migration x Sum Data along Predicted T(g) Predicted from Data Predicted from Ray Tracing

Prediction+Subtraction u Predict or pick the traveltime of a multiple. u u NMO the multiple within a time window. u u If a significant overlaying primary is suppressed at the same time, use the same strategy to predict it and fill the gap. u u Predict the multiple by a multichannel two-way prediction filter. u u Subtract the predicted multiple.

OUTLINE Why Migrate Multiples? Prediction of Multiple T(g) Joint Migration Pattern Recog. Joint Migration LSM.

What Good are Natural T(s,g’,g)? Primary g’g s’ t(s,g’,g) = min(t(s,g’) + t(g’,g)) t(s,g’,g) = min(t(s,g’) + t(g’,g)) g’g’g’g’

Answer 1: Natural Decon of Multiples Primary g’g s’ Actual Multiple Predicted Multiple Adaptive Subtraction Deblurring: d = G d 10

Answer 2: Semi-Natural Migration of Multiples g’ s Actual Multiple Predicted Multiple x d(g, t(s,g’) + t(g’,x) + t(x,g) ) g m(x) = g

OUTLINE Why Migrate Multiples? Prediction of Multiple T(g) Joint Migration Pattern Recog. Joint Migration LSM.

0 km 5 km 0 km 7 km Salt Model

Joint Migration : LS Multiple Migration PROBLEM : Multiples get Coherently Migrated Primary Multiple SOLUTION: Least Squares Joint Migration MultiplePrimary L0 m0 + L1 m1 = d (L0 + L1) m = d

0 km 5 km 0 km 7 km 0 km 7 km L0 0 0L0L1L2 ++ Standard Migration L1 L2 Correlation Wt L0 L0L1L L1 L2

s 0 km 5 km 0 km 7 km 0 km 7 km Migration with Correlation Weights Correlation Weights

0 km 5 km 0 km 7 km 0 km 7 km Ground Truth Migration

OUTLINE Why Migrate Multiples? Prediction of Multiple T(g) Joint Migration Pattern Recog. Joint Migration LSM.

Multiple Migration x Sum Data along Predicted T(g) Predicted from Data Predicted from Ray Tracing

Middle Bounce Migration x Predicted from Ray Tracing d(g, t(s,g’) + t(g’,x,g’’) + t(g’’,g) ) g m(x) = Predicted from Data

Rigorous Theory? D(g) = R f + R f + … dataprimary1st-order 1 2

Frechet Derivative D(g) = R f + R f + … r rr r R 121 f R 121 rR121 fR121 + product rule

d 0 km X 5 km 0 km Z 2.5 km 0 km Z 2.5 km m0 + m1 + m2 m0 m1m2

Multiple Time (s) 0 9 Geophone(#)1 540

Multiple Time (s) 0 9 Geophone(#)1 540

0 km 2 km 0 km 2 km 2 km 0 km 2 km 2 km 0 km 2 km 2 km Graben Model Standard Migration Least Squares Migration Least Squares MigrationGhosts