Chao Ma and Arthur B. Weglein M-OSRP, University of Houston October 19, 2015

1. A new Inverse Scattering Series (ISS) internal-multiple-attenuation algorithm that predicts the accurate time and approximate amplitude of the first-order internal multiples and addresses spurious events: Analysis and Tests in 2D Chao Ma and Arthur B. Weglein M-OSRP, University of Houston October 19, 2015 1

2. Motivation Current ISS internal-multiple-attenuation algorithm • Unique strength • This algorithm uses primaries in the input data to predict all first-order internal multiples generated at any interface with accurate time and approximate amplitudeat once without any subsurface information 2

3. Motivation Current ISS internal-multiple-attenuation algorithm • Unique strength • This algorithm uses primaries in the input data to predict all first-order internal multiples generated at any interface with accurate time and approximate amplitudeat once without any subsurface information • Limitation • Spurious events generation (non-physical events generation) due to internal multiples in the input data 3

4. Motivation Current ISS internal-multiple-attenuation algorithm • Unique strength • This algorithm uses primaries in the input data to predict all first-order internal multiples generated at any interface with accurate time and approximate amplitudeat once without any subsurface information • Limitation • Spurious events generation (non-physical events generation) due to internal multiples in the input data Always Sometimes 4

5. A comment on spurious events generation The current ISS internal multiple-attenuation algorithm is derived from one term of a comprehensive framework (i.e., Inverse Scattering Series) Any limitations of one term (e.g., spurious events generation) are anticipated and will be removed by other terms in the series.

6. Key Points that will be covered • When spurious events occur • A simple example of a spurious event generation • Circumstances spurious events are significant • Toolbox issue • Resolution of spurious events • adding one option to the toolbox 6

7. Current ISS internal-multiple-attenuation algorithm • This method is a multi-D algorithm • For simplisity, we will illustrate the algorithm in the 1D normal incident case.

8. Current ISS internal-multiple-attenuation algorithm A first-order internal multiple

9. Current ISS internal-multiple-attenuation algorithm A first-order internal multiple

10. Current ISS internal-multiple-attenuation algorithm A first-order internal multiple

11. Current ISS internal-multiple-attenuation algorithm A first-order internal multiple

12. Current ISS internal-multiple-attenuation algorithm A first-order internal multiple

13. Current ISS internal-multiple-attenuation algorithm The ISS internal-multiple-attenuation algorithm works by seeking three events within the data that can be combined in the manner (Longer-shorter-longer relationship in the vertical travel time domain) below to predict the internal multiples. 13

14. Current ISS internal-multiple-attenuation algorithm 1D normal incident case: input data 14

15. Current ISS internal-multiple-attenuation algorithm 1D normal incident case: ISS internal multiple attenation algorithm Transform input data 15

16. Current ISS internal-multiple-attenuation algorithm 1D normal incident case: ISS internal multiple attenation algorithm Transform Transform output data input data 16

17. Data with the first-order internal multiples attenuated Current ISS internal-multiple-attenuation algorithm 1D normal incident case: ISS internal multiple attenation algorithm Transform Transform output data input data 17

18. Current ISS internal-multiple-attenuation algorithm 1st-order internal multiple events Primary events

19. Current ISS internal-multiple-attenuation algorithm 1st-order internal multiple events Primary events Input data higher-order internal multiple events spurious events * Internal multiple events * when there are three or more reflectors.

20. Generation of higher-order internal multiples Zhang and Shaw, 2010 20

21. Generation of spurious events 21 (Weglein et al, 2011; Ma et. al, 2013; Liang et. al, 2013)

22. Circumstances when spurious events occur and do not occur • Do not occur • Only two reflectors • Occur • At lease three reflectors and satisfying "Lower-Higher-Lower" relationship in the vertical travel time domain 22

23. Circumstances when spurious events occur and do not occur • Do not occur • Only two reflectors • Occur • At lease three reflectors and satisfying "Lower-Higher-Lower" relationship in the vertical travel time domain • Only a few major reflectors (e.g., Water Bottom (WB) and Top Salt (TS)) • Ten, hundreds of reflectors (e.g., Middle East, North Sea, Offshore Brazil) 23

24. Removal of spurious events • Other termsanticipate that problem and they exist to precisely remove the problem • A specific, deterministic prediction mechanism within a comprehensive ISS framework • Adding those higher-order terms to the current ISS internal multiples attenuation algorithm provides the new algorithm • After adding those new terms, the new algorithm boils down to same form as the current algorithm but using a different input 24

25. Current ISS internal-multiple-attenuation algorithm 1st-order internal multiple events Primary events Input data higher-order internal multiple events spurious events Internal multiple events

26. Removal of spurious events 1st-order internal multiple events Primary events Input data higher-order internal multiple events spurious events Internal multiple events New input with internal multiples reduced

27. Examples 27

28. 2D model Synthetic velocity and density model used to generate the test data in this section (courtesy of WesternGeco). Note that horizontal and vertical coordinates are not drawn to the same scale. The average dip of the walls of the trench featuring in the center of the model is approximately 20 degree. (Figure adapted from Terenghi and Weglein, 2012) 28

29. 2D model (251 shots) Synthetic velocity and density model used to generate the test data in this section (courtesy of WesternGeco). Note that horizontal and vertical coordinates are not drawn to the same scale. The average dip of the walls of the trench featuring in the center of the model is approximately 20 degree. (Figure adapted from Terenghi and Weglein, 2012) 29

30. 2D model (each shot contains 251 receivers) Synthetic velocity and density model used to generate the test data in this section (courtesy of WesternGeco). Note that horizontal and vertical coordinates are not drawn to the same scale. The average dip of the walls of the trench featuring in the center of the model is approximately 20 degree. (Figure adapted from Terenghi and Weglein, 2012) 30

31. Five traces for comparison Synthetic velocity and density model used to generate the test data in this section (courtesy of WesternGeco). Note that horizontal and vertical coordinates are not drawn to the same scale. The average dip of the walls of the trench featuring in the center of the model is approximately 20 degree. (Figure adapted from Terenghi and Weglein, 2012) 31

32. Trace plots Red: true multiples; Blue: prediction without addressing spurious events

33. Trace plots Red: true multiples; Blue: prediction with addressing spurious events

34. Key Points that have be covered • The reason spurious events are produced • internal multiples in the input are combined (three or more reflectors) • The circumstances under which the spurious events • Not likely to happen: • only a few major reflectors (e.g., WB and TS) • Likely to happen: • tens, hundreds of reflectors (e.g., North Sea, Middle East, Offshore Brazil, etc.) • The removal of spurious events will improve the prediction capability • An elimination algorithm will further improve the prediction (next presentation) 34

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36. Thank you 36