Time structure maps
Time horizons are picked from image volumes obtained from either 3-D post- or 3-D prestack time migration. Aside from improved imaging of conflicting dips with different stacking velocities, the latter offers the advantage of providing a 3-D rms velocity field that is associated with events in their migrated positions (3-D prestack time migration). Figure 9.4-1 shows a 3-D view of the image volume derived from 3-D prestack time migration of a marine 3-D data set.
The interpreter identifies the time horizons that are associated with depositional sequence boundaries and geologically and lithologically significant layer boundaries within some of the depositional units. Then, reflection times are picked by combining seed detection and line-based interpretation strategies (interpretation of 3-D seismic data). A simplified form of an interpretation session without explicit fault identification is given below.
- Seed points are placed on the event that is being picked at locations with good signal-to-noise ratio. These are then used to drive a seed detection algorithm to pick patches of the time surface around each seed point. Figure 9.4-2 shows picks from six time horizons from the shallowest (H1) to the deepest (H6). Depending on the signal-to-noise ratio and complexity of geometry of the time horizon, the extent of the surface patches varies from horizon to horizon. Some horizons are almost entirely picked by seed detection (H2), some are covered by limited amounts of seed-detected surface patches (H4), and some are not eligible for seed detection (H6). Note that seed detection has failed especially in intensively faulted areas.
- To ensure structural control and adequate coverage of the time horizons, additional picking along inlines and crosslines is required. Figure 9.4-2 shows the horizon strands derived from line-based picking.
- The surface patches derived from seed detection and horizon strands derived from line-based picking are then combined to form the complete set of control points for each horizon as shown in Figure 9.4-2. At this stage, a comprehensive editing and repicking are required to ensure consistency in picking. The edited control points are then input to a surface fitting algorithm (Section J.5) to create grid points that define the surface by a map function tn(x, y) at every inline and crossline intersection, where the function value tn represents the reflection time at the (x, y) location on the nth surface. Figure 9.4-3 shows the gridded surfaces derived from the control points shown in Figure 9.4-2 for the six horizons. The red corresponds to the highs and the blue corresponds to the lows on each horizon. Each horizon has been color-coded independently.
Note the fault signatures especially evident on horizons H1, H2, and H3. Actually, the sharp boundary between the green-blues and the yellow-reds observed on all the maps is clear evidence of a major fault through the middle of the survey area parallel to the longer dimension. Adjoining this major fault are the oblique faults that can be detected on H1, H2, and H3. Since we use rays to perform time-to-depth conversion, it is important to ensure that ray tracing is made stable by applying a carefully measured amount of smoothing to the gridded surfaces (Figure 9.4-4). This smoothing also is needed to edit outliers among the control points that have inevitably corrupted the grid points. Finally, the gridded surfaces are usually displayed in the form of contour maps as shown in Figure 9.4-5.
Figure 9.4-1 An image volume of data derived from 3-D prestack time migration. The color-coded top surface is the water bottom with the acquisition footprint exhibited by the striations along the inline direction.
- Model building
- Time-to-depth conversion
- Interval velocity maps
- Depth structure maps
- Calibration to well tops
- Layer-by-layer inversion
- Structure-independent inversion