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Modeling case study of a subsalt exploration concept
John Sinton*, Jim Blackerby, and Steve Whitney, ConocoPhillips, and S...
Modeling Case Study of a Subsalt Exploration Concept

The critical acquisition geometry features for each set of        ...
Modeling Case Study of a Subsalt Exploration Concept

reflection decreases and the structural deviation increases.       ...
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Modeling Case Study Of A Subsalt Exploration Concept


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Wide Azimuth subsalt imaging

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Modeling Case Study Of A Subsalt Exploration Concept

  1. 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,, looking down toward the middle of the model. 2007, Beaudoin,, 2007; Corcoran,, 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. 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. 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