Flexible 3-D seismic survey design Gabriel Alvarez Stanford University Victor Pereyra, Laura Car

1. Flexible 3-D seismic survey design Gabriel Alvarez Stanford University Victor Pereyra, Laura Carcione Weidlinger Associates Inc.

2. Goal Locally optimum to illumination. Require many fewer shots than a standard design. Does not compromise the logistics. Show with a simple 3-D example how to optimize the design of a seismic survey such that it is: Alvarez, Pereyra, Carcione

3. Characteristics Flexible: allow survey parameters to change in a systematic way. Exhaustive: exploits all subsurface information as well as logistic and economic constraints. Dips, depths, velocities, presence of fractures, etc Available recording equipment, maps of surface obstacles, etc. Illumination-based: uses target illumination as the primary design consideration. Alvarez, Pereyra, Carcione

4. Design Example Single depth-variable target: 300-3000 m Land prospect. Sources are expensive. Alvarez, Pereyra, Carcione

5. View from the inline direction Subsurface model Alvarez, Pereyra, Carcione

6. View from the cross-line direction Subsurface model Alvarez, Pereyra, Carcione

7. Inline direction Cross-line direction Target reflector Alvarez, Pereyra, Carcione

8. The standard approach Alvarez, Pereyra, Carcione

9. Target parameters • Minimum depth:300 m • Maximum depth:3000 m • Maximum dip: 60 degrees • Minimum velocity: 2000 m/s • Maximum frequency:60 Hz • Minimum trace density:240000 tr/km2 Alvarez, Pereyra, Carcione

10. Survey recording patch 500 m 400 m X X X X X X X X X X X X 20 m X X X X X X X X X X X X 20 m 8 receiver lines 20 shot salvo 24 fold Alvarez, Pereyra, Carcione

11. Other parameters Max offset inline=2990 m Max offset xline=1590m Shot density: 100 shots/km2 Fold: 24 (6x4) Aspect ratio: ~2 Number of receiver lines: 8 Number of channels/line: 300 Alvarez, Pereyra, Carcione

12. Problem: Maximum-minimum offset MMO=640 m Some bins have minimum offset larger than the target depth. Alvarez, Pereyra, Carcione

13. Alternatives to solve the problem 500 m 400 m 250 m 200 m Receiver and shot density are doubled. The fold is doubled: 12 x 4 The aspect ratio is doubled: 4 The salvo is halved: 10 • Halve the receiver- and the source-line intervals. MMO=320 m. Good. But … Alvarez, Pereyra, Carcione

14. Alternatives to solve the problem X X X X X X X X X X X X 20 m 40 m X X X X X X X X X X X X 20 m 20 m The fold is doubled: 12 x 4 The aspect ratio is doubled: 4 The salvo is now one-fourth: 5 2. Halve the receiver and source-line interval and use a rectangular bin Good. Now the source density doesn’t change. But … Alvarez, Pereyra, Carcione

15. Why the need to compromise? Because we are using the same parameters for the entire survey area. We can use different parameters in different parts of the survey: the target is shallow only in a small region. Alvarez, Pereyra, Carcione

16. The proposed approach: subsurface-based design Alvarez, Pereyra, Carcione

17. The method in a nutshell ray tracing Use a subsurface model to trace rays to the surface at uniform opening and azimuth angle. Record the emergence position of the rays at the surface. Compute locally optimum spatially-varying geometry. Alvarez, Pereyra, Carcione

18. Spatially-varying geometry 1. Maintain a standard geometry but allow changes in the parameters (line intervals). 2. Maintain a standard receiver template but allow sources in “arbitrary” positions. 3. Allow sources and receivers to be in “arbitrary” positions. Alvarez, Pereyra, Carcione

19. Model space dimensionality Fixed orthogonal geometry: Only six parameters describe each geometry. Receiver interval Source interval Receiver line interval Source line interval Number of receivers/line Number of receiver lines/patch. Each parameter has a limited number of acceptable values (integer optmization). Alvarez, Pereyra, Carcione

20. Assign source-receiver positions For each geometry: based on ray emergence position being closer to a source or receiver line. Alvarez, Pereyra, Carcione

21. Preprocessing For each trial geometry: • Compute total distance that the rays were moved. • Compute shot and receiver density, fold, aspect • ratio, offsets, etc. Alvarez, Pereyra, Carcione

22. f: fitness value i: index of trial geometry λ: to balance objectives vs. constraints δ: relative weight of each objective ε: relative weight of each constraint O: objectives (illumination and cost) C: constraints (fold, aspect ratio, MMO, etc) Fitnessfunction Alvarez, Pereyra, Carcione

23. Objectives and constraints Objectives (to minimize): • total distance to adjust the ray emergence positions • total number of sources • receiver- and source-line cut Constraints: • Equipment availability • Minimum fold (trace density) • Maximum-minimum offset (MMO) • Aspect ratio Alvarez, Pereyra, Carcione

24. Splitting the survey area 10 km 10 km Shallow zone: depths <400 m (<5 km2) Mid zone: depths (400,700) m (<10 km2) Deep zone: depths >700 m (>85 km2) Alvarez, Pereyra, Carcione

25. Shallow zone Weights-objectives Weights-constraints Alvarez, Pereyra, Carcione

26. Results for shallow zone Max offset inline=1590 m 320 m Max offset xline=1070m 180 m X X X X X X 20 m 9 shots X X X X X X 20 m Shot density: 156 shots/km2 Fold: 30 (5x6) Aspect ratio: ~1.5 Number of receiver lines: 12 Number of channels/line: 160 Alvarez, Pereyra, Carcione

27. Mid zone Weights-objectives Weights-constraints Alvarez, Pereyra, Carcione

28. Results for mid zone Max offset inline=2640 m 440 m Max offset xline=1790m 360 m X X X X X X 20 m 18 shots X X X X X X 20 m Shot density: 114 shots/km2 Fold: 30 (6x5) Aspect ratio: ~1.5 Number of receiver lines: 10 Number of channels/line: 260 Alvarez, Pereyra, Carcione

29. Deep zone Weights-objectives Weights-constraints Alvarez, Pereyra, Carcione

30. Results for deep zone Max offset inline=3590 m 720 m Max offset xline=3590m 720 m X X X X X X 20 m 36 shots X X X X X X 20 m Shot density: 70 shots/km2 Fold: 25 (5x5) Aspect ratio: ~1 Number of receiver lines: 10 Number of channels/line: 360 Alvarez, Pereyra, Carcione

31. Summary of optimum geometry dr: receiver interval ds: source interval drl: receiver-line distance dsl: source-line distance nrl: number of receiver-lines Alvarez, Pereyra, Carcione

32. Stats of optimum geometries Alvarez, Pereyra, Carcione

33. A look at the logistics Logistics are not compromised because: • for each source (salvo) the receiver template • is standard orthogonal, • the receiver-line interval in zone 2 is half that • in zone 3 and in zone 1 is half that in zone 2, • the sources are along continuous lines as usual. Alvarez, Pereyra, Carcione

34. The bottom line • The geometry is locally optimum from the • illumination point of view. • The average source density is about half • than with the standard approach. • Logistics are not compromised. Alvarez, Pereyra, Carcione

35. Additional remarks 1. We emphasized reflector depth, but we can also use reflector dip, curvature, etc. 2. Different geometries may be combined to form the final geometry. 3. Can estimate the local acquisition effort. This will help in dealing with surface obstacles. 4. Surface maps should be used at the design stage to further constrain the position of sources and receivers. Alvarez, Pereyra, Carcione

36. Conclusions The standard seismic survey design is too rigid because of the assumption that the subsurface is featureless. Relaxing this assumption allows the design to be flexible, illumination based, locally optimum in terms of the required acquisition effort. Alvarez, Pereyra, Carcione

37. Thank you for your attention. I will be happy to entertain your questions. Alvarez, Pereyra, Carcione