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7th Middle East Geosciences Conference and Exhibition
Manama, Bahrain
March 27-29, 2006
Data
: Experience from North Africa
Data
1 Anadarko Petroleum Corporation, 1201 Lake Robbins Drive, The Woodlands, TX 77380, phone: +1 (832) 636
1550, fax: +1 (832) 636 8075, [email protected]
2 Applied Physics, Delft University of Technology, Lorentzweg 1, Delft, 2600 GA, Netherlands
The appearance of surface-related and internal multiples is a major problem in land seismic
data
. Over the last decade, the
data
-driven surface-related and internal multiple prediction and subtraction methods, that have been mainly developed for
the marine case, have been cross-fertilized towards the land
data
problem.
However, whereas the marine case most of the time provides high quality data
with deterministic surface multiples, the land
data
is characterized by poor quality reflection events, disturbance by surface waves and near surface propagation and
irregular trace spacing. Therefore, proper preprocessing to enhance and regularize the
seismic
reflections, which will act as
the multiple prediction operator, and removal of noise are key elements for a successful wave equation based multiple
suppression.
In this paper a methodology and sequence of data
processes is being discussed that improves the signal-to-noise ratio
(SNR) of land
data
recordings prior to any multiple estimation. The method is applied on pre-stack
data
in the CMP gather
domain under the assumption of locally laterally invariance of the earth. The improved SNR and regular offset sampling is
obtained by forming CMP super gathers from each group of CMP gathers which allows the possibility of trace mixing,
regularization and the signal enhancement in the NMO corrected domain.
Some examples will illustrate the successful application of the method of noise suppression and multiple suppression. After
conditioning and attenuating the pre-stack gathers from surface and internal multiples, the velocity picking procedure can be
performed with more accuracy which is very crucial for structural interpretation
.