Data processing: measuring – calibrating – or testing – Measurement system in a specific environment – Earth science
Patent
1998-03-03
2000-08-29
McElheny, Jr., Donald E.
Data processing: measuring, calibrating, or testing
Measurement system in a specific environment
Earth science
G06F 1900
Patent
active
06112155&
DESCRIPTION:
BRIEF SUMMARY
FIELD OF THE INVENTION
This invention relates to the field of seismic data processing. Specifically, but without limitation, this invention relates to the attenuation of water-layer-related multiples in three-dimensional seismic data processing.
BACKGROUND OF THE INVENTION
The search for subsurface hydrocarbon deposits typically involves a sequence of seismic data acquisition, analysis, and interpretation. The data acquisition phase involves use of an energy source to generate signals which propagate into the earth and reflect from various subsurface geologic structures. The reflected signals, referred to as traces, are recorded by a multitude of receivers on or near the surface of the earth, or in an overlying body of water. These signals are relied upon during the analysis phase to develop an image of the subsurface geologic structures.
The analysis phase involves procedures which vary depending on the nature of the geological structure being investigated, and on the characteristics of the dataset itself. However, because the seismic traces will generally include both a signal component and a noise component, one routine aspect of this phase involves procedures directed at eliminating to the maximum extent possible the noise component in the traces. As is well understood to those skilled in the art, the quality of the output of the data processing phase is a function of the success of the noise elimination procedures.
The final phase is the interpretation of the analytic results. Specifically, the interpreter's task is to assess the extent to which subsurface hydrocarbon deposits are present, thereby aiding such decisions as whether additional exploratory drilling is warranted or what an optimum hydrocarbon recovery scenario may be. Again, as is clearly understood in the art, the quality and accuracy of the results of the noise elimination procedures have a significant impact on the accuracy and usefulness of the results of the interpretation phase. It is clear, therefore, that noise elimination is important in the seismic data processing industry.
Two types of noise are commonly present in seismic data: 1) random ambient noise, and 2) coherent linear noise. Coherent linear noise will often be a function of the location from which the data derives. For example, in offshore seismic data acquisition, the noise component of the received seismic traces will, among other sources, often include unwanted energy deriving from signals which are trapped in the water layer and reflect between the seafloor and the water surface. These signals are often referred to as water-layer-related multiples. Multiple attenuation is an important step in marine seismic data processing.
A variety of techniques have been implemented to attenuate water-layer multiples. One well known method of suppressing multiples focuses on the dip difference between the primary signal and the multiples in the common midpoint-stacked dataset. Dip differences in the time-space domain can be separated in the frequency-wavenumber domain based on frequency content. Once the recorded data are transformed to the frequency-wavenumber domain, a dip filter is applied to the data, and the data are inverse transformed back to the time-space domain. However, several limitations exist on this method. First, the Fourier transform can produce unwanted aliasing noise. Second, the dip filter must be appropriately chosen--an overly narrow filter bandwidth will not sufficiently filter out the multiples, whereas an overly wide filter will suppress desired signal frequencies. Finally, smearing of the desired signal frequencies can result from the transform/inverse transform procedures.
A second method of multiple suppression is referred to as predictive deconvolution. Predictive deconvolution relies on the time series periodicity of the multiples for discrimination of primary signal content from the multiples. Generally, the method involves use of an autocorrelation of the trace data to determine the periodicity of the multiples. Use of that periodicity in the de
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Exxon Production Research Company
McElheny Jr. Donald E.
Reid Frank E.
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