Data processing: measuring – calibrating – or testing – Measurement system in a specific environment – Earth science
Patent
1998-01-29
1999-11-16
McElheny, Jr., Donald E.
Data processing: measuring, calibrating, or testing
Measurement system in a specific environment
Earth science
G06F 1900
Patent
active
059873876
DESCRIPTION:
BRIEF SUMMARY
FIELD OF THE INVENTION
This invention relates to the field of geophysical prospecting. Specifically, the invention involves a method of processing seismic data using parallel processors to obtain a partially migrated image of prestack seismic data.
BACKGROUND OF THE INVENTION
The search for subsurface hydrocarbon deposits typically involves a sequence of data acquisition, analysis, and interpretation procedures. The data acquisition phase involves use of an energy source to generate signals that propagate into the earth and reflect from various subsurface geologic structures. The reflected signals are recorded by a multitude of receivers on or near the surface of the earth, or in an overlying body of water. The received signals, which are often referred to as seismic traces, consist of amplitudes of acoustic energy which vary as a function of time, receiver position, and source position and, most importantly, vary as a function of the physical properties of the structures from which the signals reflect. The data analyst uses these traces along with a geophysical model to develop an image of the subsurface geologic structures.
The analysis phase involves procedures that vary depending on the nature of the geological structure being investigated, and on the characteristics of the dataset itself. In general, however, the purpose of a typical seismic data processing effort is to produce an image of the geologic structure from the recorded data. That image is developed using theoretical and empirical models of the manner in which the signals are transmitted into the earth, attenuated by the subsurface strata, and reflected from the geologic structures. The quality of the final product of the data processing sequence is heavily dependent on the accuracy of these analysis 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. In that assessment, the interpretation of the image involves a variety of different efforts. For example, the interpreter often studies the imaged results to obtain an understanding of the regional subsurface geology. This may involve marking main structural features, such as faults, synclines and anticlines. Thereafter, a preliminary contouring of horizons may be performed. A subsequent step of continuously tracking horizons across the various vertical sections, with correlations of the interpreted faults, may also occur. As is clearly understood in the art, the quality and accuracy of the results of the data analysis step of the seismic sequence have a significant impact on the accuracy and usefulness of the results of this interpretation phase.
In principle, the seismic image can be developed using a three-dimensional geophysical model of seismic wave propagation, thereby facilitating accurate depth and azimuthal scaling of all reflections in the data. Accurately specified reflections greatly simplify data interpretation, since the interpretational focus can be on the nature of the geologic structure involved and not on the accuracy of the image. Unfortunately, three dimensional geophysical models frequently require intolerably long computation times, and seismic analysts are forced to simplify the data processing effort as much as possible to reduce the burdens of both analysis time and cost.
In addition to the 3-D computation challenge, the analyst faces a processing volume challenge. For example, a typical data acquisition exercise may involve hundreds to hundreds of thousands of source locations, with each source location having hundreds of receiver locations. Because each source-receiver pair may make a valuable contribution to the desired output image, the data handling load (i.e., the input/output data transfer demand) can be a burden in itself, independent of the computation burden.
FIG. 1 depicts a pers
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Dickens Thomas A.
Schneider, Jr. William A.
Willen Dennis E.
Exxon Production Research Company
Koch S. P.
McElheny Jr. Donald E.
Reid F. E.
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