Wide Azimuth subsalt imaging

1. Modeling case study of a subsalt exploration concept
John Sinton*, Jim Blackerby, and Steve Whitney, ConocoPhillips, and Steve Sloan, University of Kansas
the background into which the salt was inserted between
Summary
the surfaces in Figure 1. A constant salt velocity of 14, 800
A modeling study of a subsalt exploration problem was ft/s was used.
used to help understand imaging issues and to help plan
solutions to those issues. By comparing images from Approximate image area
various acquisition geometries ranging from wide (WATS)
to narrow (NATS) one can say WATS and certain XWATS
geometries should resolve most of the subsalt imaging
problems for the area of interest. Despite these
improvements subsalt illumination remains an issue for all
types of acquisition geometries as demonstrated by
interpreting images and creating amplitude maps. WATS
geometries seem to be more robust when faced with
velocity model inaccuracies.
Introduction
Over the last several years Wide Azimuth Towed Streamer
(WATS) acquisition has been shown to provide significant Figure 1: Final top and bottom of salt used in the model. The
improvement in imaging and multiple attenuation for view is above the top of salt from the lower left-hand corner,
complex geology (Regone, 2006; Sava, 2006; Barley, et.al., looking down toward the middle of the model.
2007, Beaudoin, et.al., 2007; Corcoran, et.al, 2007;
Howard, 2007; Michell, 2007). Several service providers The model was used to compute seismic shots using a
are now offering multi-client Exploration WATS 3D constant-density acoustic two-way wave-equation
surveys. We wanted to better understand potential imaging algorithm. Approximately 7,000 shots were computed each
improvement possible through the use of WATS using a 50 m by 50 m gridded receiver array centered on
acquisition in the Gulf of Mexico. Specific questions to be the source covering a square 18.6 km on a side. Thus, each
addressed are: shot was recorded by approximately 140,000 receivers.
• Will the imaging improvements seen elsewhere The shot line spacing was 150 m which was chosen so that
translate to the specific area of interest? subsets of the full data could closely approximate proposed
• Which of the proposed acquisition geometries is more multi-client surveys. Subsets of the full shots were created
likely to produce more improvement? for a typical Narrow Azimuth Towed Streamer (NATS)
• geometry and several XWATS geometries (Figure 2).
Which imaging technology is best with the exploration
WATS (XWATS) data?
Wide
• Narrow
How do errors in the model affect the image quality of
XWATS data?
MAZ/RAZ
NATS WATS
• Are subsalt reflection amplitudes a useful
interpretation tool?
Method
A modeling approach similar to that described by Regone
(2006) was used in this study. The model for a specific
exploration project was constructed using well data,
seismic depth-imaging velocities, and salt surfaces
provided by the interpreters of the area. As a first step
Cable Boat
interpreted surfaces represented the shapes of the water
bottom, top of salt, bottom of salt and major stratigraphic Shot Boat Requires multiple passes
boundaries were imported into the interpretation system.
Each surface required editing to create closed volumes. Figure 2: Examples of various marine towed streamer acquisition
geometries.
Figure 1 shows the final top and bottom salt surfaces.
Depth migration derived sediment velocities were used as

2. Modeling Case Study of a Subsalt Exploration Concept
The critical acquisition geometry features for each set of Quantitative information was extracted from each image by
shots are shown in Table 1. Typical XWATS and NATS picking a deep subsalt reflection. The impedance contrast
surveys use a sail line spacing of 450 m. All datasets used causing that reflection covered the entire model except
a shot spacing of 100 m and receiver spacing within each where there was salt. That reflection coefficient was
cable was 50 m. The number of cables and the placement constant everywhere so one should expect extracted
of the source were varied between the datasets. The “Ideal reflection strength to also be constant under idealized
XWATS” type used a source centered receiver array that aperture and imaging algorithm conditions. Maps of
was 18.6 km by 3.5 km. The XWATS-1 type used a 16.6 reflection strength and structural deviation from the actual
km by 2 km array with the source centered on one side. surface were produced (Figure 3).
The XWATS-2 type used a 9.3 km by 3.5 km array with
the source centered on the front of the cable. The NATS Results
type used a 9.3 km by 1 km array with the source centered
on the front of the cable. Figure 3 compares results for each data type. The results
are arranged in a qualitative sense from best (top) to worst
Type Sail Cable
(bottom) quality. The top row of the figure shows from left
Line
to right the depth slice extracted from the model, with salt
Spacing
colored white; the reflectivity of the subsalt reflector; and
Spacing Number Source
the depth to the subsalt reflector. Variations in its
WATS 150 m 50 m 373 Center
reflectivity are less than 1%. The depth slices show
Ideal XWATS 450 m 50 m 70 Center
degradation from WATS to NATS as a loss of both salt
XWATS 1 450 m 100 m 20 Side boundary reflection and sediment reflections. NATS can
XWATS 2 450 m 100 m 35 Front not image parts of the salt and sediment reflections. It was
NATS 450 m 100 m 10 Front possible to pick nearly all of the subsalt reflection in
WATS image as shown by the second row under
Table 1: This table shows the critical acquisition parameters for
reflectivity. Likewise, the structural deviation from the
each dataset type. See Figure 3 for a graphical representation of
each type. actual reflecting surface (right column) for the WATS type
is limited to less than 50 m (green to red color) for most of
Each set of shots (full and subsets) was imaged with a one- the surface. That deviation is only two depth samples in
way finite-difference shot migration and a single arrival the image and the model. Most of the deviation is caused
Kirchhoff migration. No preprocessing of any kind was by interference with salt related multiples. As one shifts to
applied to the data prior to imaging. the XWATS or NATS types the amount of pickable
Velocity Reflectivity Depth
Model Geometry ACTUAL
L
H
WATS
Best
Ideal XWATS
NA NA
XWATS 1
XWATS 2
Worst NATS
Figure 3: Comparison of shot migration imaging results for various acquisition geometries. Columns are (left to right) depth slice,
acquisition geometry (source is black dot to cables in red), reflection strength (amplitude increases from red to black) and structural deviation
from the actual surface (red to green is approximately less than 100 m).

3. Modeling Case Study of a Subsalt Exploration Concept
reflection decreases and the structural deviation increases. geology and the type of information available. Although
Table 2 shows the percentage of reliably interpretable not discussed in the paper it is possible to use the results of
reflection for each data type. All WATS-like data types are the modeling study in a value-of-information exercise to
considerably better than the NATS type. further quantify a decision making process.
Specific to the subject area it was shown that WATS
Type % Accurately Imaged
produces the highest quality image with certain types of
WATS 83%
XWATS a close second. The NATS geometry cannot
Ideal XWATS 81%
image significant portions of the subsalt geology. Shot
XWATS 1 79%
migration is preferred over Kirchhoff imaging if one can
XWATS 2 67%
accept loss of steep dip information and afford the extra
NATS 55%
cost to generate migrated gathers. Reflection amplitudes
Table 2: Shown is the percentage of the subsalt reflection that was under salt in the image volumes were strongly influenced
accurately pickable. by the overlying salt geometry. One should be very careful
not to over interpret subsalt reflection amplitudes. Large
The velocity model used to image data was perturbed as scale velocity errors do not significantly degrade WATS
shown in Figure 4 and new images were computed for the image quality, although subsalt reflections are misplaced
WATS and NATS data types. The viewer is looking which could alter an interpretation. Images with NATS
upward toward the bottom of salt (gray surface). The red data are much more sensitive to any type of errors in the
surface is a large scale deepening of the bottom of salt by salt geometry.
as much as 1,500 m. The purple feature is not discussed
Exact Perturbed
here. Figure 5 compares sections extracted from four
images: WATS with exact and perturbed models; NATS
with exact and perturbed models. The perturbation in
W
bottom of salt is highlighted in red. It is clear that the
A
WATS data type can image the subsalt reflection well with
T
the perturbed model, although the reflection is misplaced.
S
The NATS data type can not image the entire subsalt
reflection with the perturbed model.
N
A
T
S
No reflection
Figure 5: WATS and NATS images with the exact and perturbed
models..
Acknowledgements
Figure 4: Modified model.
The authors thank ConocoPhillips Global New Venture
Exploration office for their support and management of
Conclusions ConocoPhillips for permission to publish this work.
This study confirms as other authors have found that
modeling can be used to provide quantitative information to
influence the exploration decision process. Modeling is,
relative to field acquisition, a cost effective method to
estimate critical acquisition parameters and imaging
decisions. One should expect to spend weeks to months on
a modeling study depending on the complexity of the