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GCMapping
Geologic Features Using Seismic
Curvature*
By
Satinder Chopra1 and Kurt J. Marfurt2
Search and Discovery Article #40272 (2008)
Posted February 7, 2008
*Adapted from the Geophysical Corner column, prepared by the authors, in AAPG Explorer, November, 2007, Part 1, entitled “Using Curvature to Map Faults, Fractures”, and December, 2007, Part 2, entitled “Curvature Can Be a Map to Clarity”. Editor of Geophysical Corner is Bob A. Hardage ([email protected]). Managing Editor of AAPG Explorer is Vern Stefanic; Larry Nation is Communications Director.
1Arcis Corporation, Calgary, Canada ([email protected])
2University of Oklahoma ([email protected])
General Statement
Curvature is a measure of the deviation of a surface from a plane. The more a surface is structurally flexed, folded or faulted, the larger its curvature. Curvature can indicate domes and sags associated with salt and shale diapirism, differential compaction, and diagenetic dissolution and collapse, as well as predict paleostress and areas favorable for natural fractures.
Curvature is usually
computed from picked horizon surfaces interpreted on 3-D seismic
data volumes.
An interpreter defines surface patches of a given size, which appropriate
software algorithms then fit with a mathematical quadratic surface. Curvature
measures computed from the coefficients of this quadratic surface include:
1) Curvedness.
2) Azimuth of minimum curvature.
3) Shape index.
4) Minimum, maximum, most-positive, most-negative.
5) Dip.
6) Strike curvatures.
We find the most-positive and most-negative curvatures to be the easiest measure to visually correlate to features of geologic interest.
uFigure CaptionsuMethoduFaults and Fractures
uFigure CaptionsuMethoduFaults and Fractures
uFigure CaptionsuMethoduFaults and Fractures
uFigure CaptionsuMethoduFaults and Fractures
uFigure CaptionsuMethoduFaults and Fractures
uFigure CaptionsuMethoduFaults and Fractures
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Method
Figure 1a shows a
time-structure map at about 1850 ms, interpreted from a 3-D
Whether due
to limitations in the survey design, coherent noise, or systematic
errors in data processing, an acquisition footprint is related to
the source and receiver geometry and has little correlation to the
subsurface geology. Horizons picked on noisy
Even when
spatial filtering is used to minimize effects of an acquisition
footprint, horizon-based curvature estimates may still suffer from
footprint artifacts. In contrast, curvature
Faults and FracturesAs examples, Figures 1d and 1e show the most-positive and most-negative volumetric curvature attributes extracted along the horizon surface in Figure 1a. Notice that these displays are free of the N-S and E-W artifacts seen in Figures 1b and 1c, and show arcuate folds indicated by yellow arrows. The advantages of volumetric attributes are two-fold:
1) As shown
in Figure 1, the images have a higher
signal-to-noise ratio. Volumetric estimates of curvature are
computed not from one picked data sample, but rather from a vertical
window of 2) Not every geologic feature that we wish to interpret falls along a horizon that can be interpreted. Often the target of interest falls above or below a strong, easily picked horizon.
Curvature images having different
spatial wavelengths provide different perspectives of the same
geology. Tight (short-wavelength) curvature delineates small
details, such as intense, highly localized fracture systems. Broad
(long-wavelength) curvature enhances smooth, subtle flexures that
are difficult to see in conventional
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