Data processing: measuring – calibrating – or testing – Measurement system in a specific environment – Earth science
Reexamination Certificate
2001-06-08
2003-04-08
McElheny, Jr., Donald E. (Department: 2862)
Data processing: measuring, calibrating, or testing
Measurement system in a specific environment
Earth science
Reexamination Certificate
active
06546339
ABSTRACT:
FIELD OF THE INVENTION
This invention relates to geophysical prospecting using seismic signals, and in particular to a method of estimating seismic velocity.
BACKGROUND
Effectively searching for oil and gas reservoirs often requires imaging the reservoirs using three-dimensional (3-D) seismic data. Seismic data is recorded at the earth's surface or in wells, and an accurate model of the underlying geologic structure is constructed by processing the data. 3-D seismic imaging is perhaps the most computationally intensive task facing the oil and gas industry today. The size of typical 3-D seismic surveys can be in the range of hundreds of gigabytes to tens of terabytes of data. Processing such large amounts of data often poses serious computational challenges.
Obtaining high-quality earth images necessary for contemporary reservoir development and exploration is particularly difficult in areas with complex geologic structures. In such regions, conventional seismic technology may either incorrectly reconstruct the position of geological features or create no usable image at all. Moreover, as old oil fields are depleted, the search for hydrocarbons has moved to smaller reservoirs and increasingly hostile environments, where drilling is more expensive. Advanced imaging techniques capable of providing improved knowledge of the subsurface detail in areas with complex geologic structures are becoming increasingly important.
Obtaining high-quality images of subsurface structures typically requires having an accurate velocity model of the subsurface region of interest. One commonly-used method of improving the accuracy of velocity models is Migration Velocity Analysis (MVA). A known MVA approach is based on Common Reflection Point (CRP) gathers generated by 3-D prestack Kirchhoff migration. For further information on CRP gathers see Stork, “Reflection Tomography in the Postmigrated Domain,”
Geophysics
57:680-692 (1992), and Deregowski, “Common Offset Migration and Velocity Analysis,”
First Break
8(6):224-234 (1990). The CRP gathers contain redundant structural information which can be used to correct the velocity model. Conventional MVA using CRP gathers can suffer from accuracy and complexity problems, however.
SUMMARY
The present invention provides geophysical velocity analysis methods comprising the steps of: establishing a seismic data set and a velocity model corresponding to a seismic exploration volume; generating a set of angle-domain common image gathers for the volume from the seismic data set and the velocity model; for each of the gathers, generating a plurality of moveout paths z corresponding to a plurality of residual velocity values, wherein each of the moveout paths corresponds to a residual velocity value &Dgr;v according to an angle-domain residual moveout equation
z
=
z
0
1
-
p
2
(
v
^
+
Δ
v
)
2
1
-
p
2
v
^
2
,
wherein p is a ray parameter, z
0
=z(0) is a zero-angle depth, and {circumflex over (v)} is a trial velocity corresponding to said each of the gathers; selecting a best-fit residual velocity value from the plurality of residual velocity values, the best-fit residual velocity value corresponding to a best-fit moveout path; and updating the velocity model using the best-fit residual velocity value.
In one embodiment, the moveout paths are generated by performing residual moveout on the ACIG data according to the ACIG moveout equation, computing a semblance value for each depth and residual velocity value, and selecting the residual velocity values corresponding to maximum-semblance points as best-fit residual velocity values. In another embodiment, each moveout path is generated synthetically, directly from the ACIG moveout equation for a given zero-angle depth. Each synthetically-generated moveout path is then compared to an observed event path in the ACIG corresponding to that zero-angle depth. The residual velocity value corresponding to the moveout path that best fits the observed event path is then selected as a best-fit moveout path.
In yet another embodiment, different ACIGs are generated for the same location using a suite of trial velocities. The trial velocity that leads to horizontal alignment of events in its corresponding ACIG is then selected as a best-fit trial velocity, and is used in updating the velocity model.
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Bevc Dimitri
Liu Wei
Popovici Alexander M.
3D Geo Development, Inc.
McElheny Jr. Donald E.
Popovici Andrei D.
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