Any 3D seismic survey can have an acquisition footprint. Acquisition footprint is an expression of the surface geometry (most common on land data) that leaves an imprint on the stack of 3D seismic data. Often we recognize it as amplitude and phase variations on time slices, which of course display the amplitudes within our data set at a specified two way time (Cordsen, 2004). On the other hand, acquisition footprint is often used to describe amplitude stripes that appear in time slices or horizon slices produced from 3D seismic data volumes. Although acquisition design of a 3D survey has a major influence on the nature and severity of a footprint, improper data processing techniques such as the use of incorrect normal moveout (NMO) velocities can also create footprint (Cordsen, et al., 2000). More seriously, on horizon slices, footprint can interfere with and confuse stratigraphic patterns.
Many different contributions to the generation of acquisition footprint are possible. These can be divided into two main categories: (1) geometry effects: line spacing, fold variations, wide versus narrow patch geometry, source generated noise and variations of offset and azimuth distribution. (2) non-geometry effects: topography, culture, weathers, surface conditions and processing artifacts. In this article we study the effects of these parameters for 3D seismic survey in AHWAZ oil field and calculate acquisition footprint noise in this field. Most of the time the acquisition footprint is based on the source and receiver line spacing and orientations. The larger the line spacing, the more sever the footprint. In land situations where access is very open and, therefore, the lines are very regularly spaced, we may be able to recognize the footprint very clearly. Because the geometry is regular, the footprint also will have the same periodicity. Fold variation themselves are the simplest form of an acquisition footprint. Fold changes with offset (or rather mute distance from the source point); each offset range, therefore, has differing fold contributions (Cordsen, 1995). Because each individual bin of a 3D survey has changing offset distributions, the CMP stack of all traces in a bin will display bin-to-bin amplitude variations. This variation in itself can produce an acquisition footprint.
Generally it has been thought that acquisition footprint is far worse in the shallow part of the seismic and therefore, of course, the geological section, mainly because the fold is lower, and amplitude variations necessarily are far more dramatic. Offset limited fold variations alone may produce a recognizable footprint. The higher the fold, the better the signal to noise ratio; therefore, less footprint is evident.
Wide recording patch geometries are far more accepted these days than narrow patch geometries (Cordsen, et al., 2000). The reasons are numerous and ranges from reduction in acquisition footprint (particularly that due to back-scattered shot noise) to improved statics solutions and the availability of large channel capacities on seismic recording crews (also leading to higher fold). In addition to the impact of the fold variations, acquisition footprint is made worse by source generated noise trains that penetrate our data sets. The lower the signal to noise ratio is, the worse the footprint will be.
Unfortunately, the noise typically has a low frequency content that is much less affected by attenuation. Therefore the noise becomes more prominent relative to the signal content deeper in the section. Our experiences have shown that acquisition footprint problems can be just as prevalent in the deep section as they are in the shallower section. If surface access is poor because of topography variations, tree cover, towns, etc., we irregularize the geometry by moving source points to locations of easier access, and therefore mask the acquisition footprint. It is still present, however. The footprint is just so much harder to identify. Weather and surface conditions may also impact the recorded amplitudes.
One can model an acquisition footprint by creating a stack response on either synthetic or real data. We stack the data in a 3-D cube and display the resulting seismic data over a small time window. The best input is a single NMO and static corrected, offset sorted 2D (or 3D) CMP gather. These traces will be applied to each CMP Bin in the recording geometry. In summary, we should attempt to minimize footprints by employing proper seismic acquisition and processing techniques, but if a footprint persists in the stacked data, there are ways to filter the data and mitigate its effect on geological interpretation. In this article we optimized acquisition parameters in order to minimize acquisition footprint noise for 3D seismic survey in AHWAZ oil field and finally with 3D modeling by OMNI software we saw the intensity of this noise in our seismic sections.