Seismic Attributes

1. “ROLE OF SEISMIC ATTRIBUTES IN
PETROLEUM EXPLORATION”
Naga Lakshmi V
Sr. Geophysicist (S)
Cauvery Basin, ONGC
30 May 2022
WORKSHOP ON RECENT TRENDS IN
PETROLEUM EXPLORATION

2. • Basic principles of Seismic
• What is a Seismic Response ?
• What is a Seismic Attribute ?
• Classification of Seismic Attributes
• Applications of Attributes & Limitations/Pitfalls
Presentation Outline

3. Basic Principles of Seismic
P-waves incident on an interface at other than normal incidence angle can produce reflected and
transmitted P & S-waves. S-waves travel through the Earth at about half the speed of P-waves and
respond differently to fluid-filled rocks, and so can provide different additional information about
lithology and fluid content of hydrocarbon-bearing reservoirs
Snell’s Law: When a wave crosses a boundary between two isotropic media, the wave changes direction such that
V1/V2 = Sin i/Sin r
where i is the angle of the incident wave, Vi is the velocity of the incident medium, r is the angle of refraction,
and V2 is the velocity of the second medium.

4. WHAT CAUSES A SEISMIC RESPONSE?
Changes in Bulk-Rock Velocity and density
• Lithology (e.g., sandstone, shale, limestone, salt
• Porosity (e.g., intrinsic, compaction, diagenesis
• Mineralogy (e.g., calcite vs dolomite, carbonaceous shales
• Fluid type and saturation (water, oil, gas)
Seismic data acquisition with propagation paths of different seismic waves/rays illustrated on a two-layer model (left) and
an example source gather showing arrival times of different waves to the receivers located along the profile (right). "R"
represents the seismic receivers, while different colored lines show different seismic events; green-direct wave, light blue-
surface wave, dark blue refracted wave and red-paths of reflected waves. Note that the surface wave occurs in the zone
between two light blue lines.
Acoustic Impedance = density x Velocity
Pimp = v

5. SEISMIC REFLECTION
2 x depth
velocity
Reflection
time
Seismic trace
Midpoint
Acquisition geometry
t =
t = 0
Source
X
Receiver
Velocity
Reflector
Depth
Midpoint

6. What is a Seismic Response ?
What is recorded ?
The energy & two-way travel times of the waves returning
to the surface after being reflected and refracted from the
interface of one or more layers of the earth.
How much of the energy is reflected - depends on the
difference in the impedance which is the product of
velocity of the propagating wave and density (ρ) of the
rock layers.
Pimp = v
The seismic response is simplified using the following
expression h(t) = f ∗ g(t) + e
Where, h(t) is the seismic trace, f is the source wavelet,
g(t) is a reflectivity function and * is the convolution.

7. SEISMIC OUTPUT : GATHERS AND STACK DATA
Seismic gathers are the raw data from which seismic stacks are created. A gather is a family of traces (e.g.
shot-point gather is the family of all traces corresponding to the same source firing). Sorting of traces by
collecting traces that have the same midpoint (CMP) is called a common midpoint gather (CMP-gather).
Full stack seismic data is generated by summing all the offset traces together.

8. SEISMIC OUTPUT
Seismic Processing
Stacking represents summation of NMO-
corrected traces in a CMP family. The
collection of stacked traces forms a
seismic section which gives an image
(slice) of the subsurface

9. “Surface seismic is the most important tool for delineating a
reservoir. In the past, most of the effort was focused on structural
Imaging. However, due to advances in computation hardware and
new theoretical approaches to sedimentation processes and wave
phenomenon, seismic attribute studies has become a major focus
for locating hydrocarbon indicators . AVO, Amplitude analysis,
frequency decomposition wave attenuation and elastic inversion
are few techniques which transform wave information in to
Hydrocarbon Indicators.”

10. WHAT IS A SEISMIC ATTRIBUTE?
 Seismic attribute is a measurable property of seismic data, such as amplitude, dip,
frequency, phase and polarity.
 It is computed by mathematical manipulation of the original seismic data to highlight
specific geological, physical, or reservoir property features.
 They evaluate the shape or other characteristic of one or more seismic trace(s) and their
correlation over specific time intervals. Seismic attributes computed from reflection data
are based on various physical phenomena.
 During seismic wave propagation through earth layers, their wave characteristics, such as
amplitude, frequency, phase, and velocity, change significantly.
 These changes in the seismic waves provide the signatures of the physical properties of
the medium—the rocks in the subsurface through which they propagate.

11.  The amplitude content of seismic data is the principal factor for the
determination of physical parameters, such as the acoustic
impedance, reflection coefficients, velocities, absorption etc.
 The phase component is the principal factor in determining the
shapes of the reflectors, their geometrical configurations etc.
 Frequency is sensitive to changes in the bedding sequence and is
thus useful in telling where stratigraphic changes occur.
Hydrocarbon accumulations often have low frequencies
immediately beneath them, this is referred to as low frequency
shadow.
 These are mathematical descriptions of the shape or other
characteristic of a seismic trace over specific time intervals.

12. • The first use of seismic was to identify potential hydrocarbon
traps by looking purely at the structures imaged.
• It was not until the late 1960s that geophysicists began
commonly using a relationship between seismic amplitude
brightening and structure for clastic reservoirs (Chopra and
Marfurt, 2007; Hilterman, 2001).
• This marked the start of direct hydrocarbon indicator (DHI)
identification and analysis.
• Bright spot technology included more than amplitudes, but
also included flat spots, frequency loss, polarity reversals, and
dim spots – features identified by the interpreter that will
form the motivation for later seismic attribute developments.
OVERVIEW OF SEISMIC ATTRIBUTES

13. CLASSIFICATION OF SEISMIC ATTRIBUTES
Attributes can be measured at one instant in
time or over a time window, and may be
measured on a single trace, or on a set of
traces or on a surface interpreted from seismic
data.
All the horizon and formation attributes
available are not independent of each other
but simply different ways of presenting and
studying a limited amount of basic
information.
Commonly used for two purposes,
some qualitative information of the geometry
and the physical parameters of the subsurface.
by Brown (2004)

14. These attributes allow us to
qualitatively predict discontinuities
and geologic facies
CLASSIFICATION OF SEISMIC ATTRIBUTES
Seismic Attributes
Envelope, RMS amplitude,
spectral magnitude, acoustic
impedance, elastic impedance
and AVO
Sensitive to changes in
seismic impedance
Peak-to-trough thickness,
peak frequency and
bandwidth
Sensitive to layer
thicknesses
Coherence, edge detectors,
amplitude gradients, dip-
azimuth, curvature
Sensitive to seismic textures
and morphology
These attributes can be quantitatively
correlated to well control using multivariate
analysis, geostatistics, or neural networks
Lithology attributes Geometrical attributes

15. Amplitude is the most basic attributes of a seismic trace. Initially the interpreter’s interests to amplitude is
just restricted to “it presence” not its magnitude as in that time seismic is commonly used only for
structural analysis. Currently the seismic data processing is generally directed to obtain “preserve true
amplitude” so stratigraphic analysis can be done. Seismic amplitude is commonly used also for DHI, facies
facies and reservoir properties mapping. The lateral changes of amplitude can be used to distinguish one
facies with another. For instances, the concordant beds tend to have higher amplitudes, hummocky lower
and chaotic the lowest one. The sand-rich environment usually also have a higher amplitude compared
with the shale-prone ones. This differences in sand-shale ratio can be easily analyzed in the amplitude
map.
Types of primary amplitude attributes frequently used is as follows :
1. RMS amplitude 2. Average absolute amplitude
3. Maximum peak amplitude 4. Average peak amplitude
5. Maximum trough amplitude 6. Average trough amplitude
7. Maximum absolute amplitude 8. Total absolute amplitude
9. Total amplitude 10. Average energy
11. Total energy 12. Mean amplitude
13. Variance in amplitude 14. Skew in amplitude
15. Kurtosis in amplitude 16. Reflection Strength/Instantaneous Amplitude
AMPLITUDE ATTRIBUTES

16. DIRECT HYDROCARBON INDICATORS (DHI)
DHI identification uses the knowledge that the introduction of oil
or gas into a rock results in a decrease in density and P-wave
velocity. The introduction of hydrocarbons into a water-filled
reservoir produces different seismic effects depending on the
acoustic impedance contrast between the reservoir and
surrounding rocks.

17. PITFALLS OF DHI
Not all bright spots are due to hydrocarbon filled reservoirs.
If the reservoir rock is harder than the surrounding rock, as is the
case with most limestones, then a bright spot cannot be used to
indicate the presence of hydrocarbons.
The amplitude can also be either a stratigraphic effect (e.g. pinch
outs), lithological effect (e.g. coal beds, volcanic intrusions) or due to
tuning.
The most important factor affecting the correct interpretation of a
DHI is the phase of the data. If the phase is 180 different to what the
interpreter thinks it is, then a hard seismic event, such as a volcanic
intrusion, limestone or conglomerate could be targeted for drilling
rather than a bright gas bearing sand.
Tuning is a common phenomenon associated
with thin beds in seismic data. It refers to the
brightening or dampening of seismic amplitude
because of constructive and destructive
interference from overlapping seismic reflectors.
At a spacing of less than one-quarter of
the wavelength, reflections undergo constructive
interference and produce a single event of
high amplitude. The tuning thickness is
the bed thickness at which two events become
indistinguishable in time, and knowing this
thickness is important to seismic interpreters
who wish to study thin reservoirs.

18. Instantaneous Amplitude, Instantaneous Phase and
Instantaneous Frequency are the fundamental
complex trace attributes.
The complex terms refers to the computation which
assume that conventional seismic trace is a real part of
a complex mathematical function (the imaginary part
is the product of Hilbert transform of the real part).
Reflection strength, also known as trace envelope or
instantaneous amplitude, is the most popular trace
attribute.
It is used to identify bright spots, dim spots, and
amplitude anomalies in general. Bright spots are
important as they can indicate gas, especially in
relatively young clastic sediments.
The advantage of using reflection strength instead of
the original seismic trace values is that it is
independent of the phase or polarity of the seismic
data, both of which affect the apparent brightness of a
reflection.
COMPLEX TRACE ATTRIBUTES
The value of reflection strength always positive with
magnitude close to the value of real data. High
reflection strength often associate with a sharp
lithology change, likes in the case of unconformity or a
sharp change of depositional environment.

19. REAL (A) AND QUADRATURE (B) OF AN ACTUAL SEISMIC
TRACE. THE DASH-LINE IS THE AMPLITUDE ENVELOPE.
FIGURES C AND D ARE CONSECUTIVELY THE
INSTANTANEOUS PHASE AND FREQUENCY WHERE THE
DASH LINE IS THE WEIGHTED-AVERAGE FREQUENCY.
(a)
(b)
(c)
(d)
COMPARISON BETWEEN THE REAL TRACES AND
REFLECTION STRENGTH TRACE (LANDMARK, 1999).
x(t) = a (t) cos (t) – Real trace
y(t) = a (t) sin (t) – Imaginary trace of Hilbert
Transform
Reflection Strength, a(t) =√(𝑥 𝑡 2
+𝑦 𝑡 2
)

20. Instantaneous phase display is very effective for
detecting
(1) The fault discontinuity,
(2) wedging-out,
(3) unconformity
(4) Reflectors with different dips which will interference
each other.
The sedimentary of prograding layers and areas of
onlap or offlap will be very highlighted thus makes this
display is also very effective for sequence boundary
pickings.
By using instantaneous phase display, the detail
stratigraphic interpretation of system tract is much
easier compared if using the reflectivity.
INSTANTANEOUS PHASE
The convention followed by instantaneous phase is that peaks on a seismic trace have 0 degrees
phase, troughs have 180 degrees, down going zero crossing have 90 degrees, and upgoing zero
crossings have -90 degrees
Instantaneous phase Volume

21. INSTANTANEOUS FREQUENCY
Instantaneous Frequency represents the rate of
change of instantaneous phase as a function of time.
It is a measure of the slope of the phase trace, and is
obtained by taking the derivative of the phase.
Values may range from -Nyquist frequency to
+Nyquist frequency. However, most instantaneous
frequencies are positive.
provides information about
(1) the frequency signature of events,
(2) the effects of absorption and fracturing,
(3) depositional thickness.
Instantaneous frequency also provides a means of
detecting and calibrating thin-bed tuning effects.
Instantaneous Frequency Volume

22. AMPLITUDE ATTRIBUTES - RMS Amplitude
RMS amplitude within -30 to -60 ms showing the Channel
The root mean square amplitude (RMS) is a commonly used technique to display amplitude values in a
specified window of stack data. With RMS amplitude, hydrocarbon indicators can be mapped directly by
measure reflectivity in a zone of interest.
24.46
RMS
)
...
0
(5
8
1
RMS
a
N
1
RMS
2
2
N
1
i
2
i




 


23. AMPLITUDE ATTRIBUTES

24. SWEETNESS ATTRIBUTE
Sweetness s(t) is defined as the trace envelope a(t)
divided by the square root of the average frequency
fa(t)
s(t) =
𝑎(𝑡)
𝑓𝑎(𝑡)
 Sweetness is an empirical attribute designed to
identify “sweet spots,” places that are oil and gas
prone.
 In young clastic sedimentary basins, sweet spots
imaged on seismic data tend to have strong
amplitudes and low frequencies.
 High sweetness values are those that most likely
indicate oil and gas.
 Sweetness tends to be driven more by amplitude
than by frequency and often closely resembles
reflection strength.
 According to Hart (2008), sweetness is
particularly useful for channel detection.
•Arbitrary seismic profile in sweetness attribute version. With the
arrows, the zones under analyses were marked (C-A, C-B, MB-A
and MB-B). Inserted well logs are gas saturation.
(Ref: Verification of bright spots in the presence of thin beds by AVO
and spectral analysis in Miocene sediments of Carpathian Foredeep,
July 2019, Acta Geophysica 67(3))

25. SPECTRAL DECOMPOSITION
Use of small or short windows for transforming and displaying frequency spectra (Sheriff, 2005 Encyclopedic
Dictionary of Applied Geophysics). In other words, the conversion of seismic data into discrete frequencies or
frequency bands.
 Layer thickness determinations
 Stratigraphic variations
 DHI characteristics (e.g. shadow zones)
In combination with variance (a.k.a. semblance,
coherence) channels can be more easily identified and
analyzed.
Coherence illuminates the channel edges while spectral
decomposition represents the channel thickness.
Additionally, this method can provide a better idea of
channel body continuity, fill variability, and possible
reservoir quality

26. Channel
and Point
bars
Spectral decomposition at 24 Hz within -30 to -60 ms
ZOOMED
26

27. Horizon probe used manipulated transparency (left) versus CWT Spectral decomposition (middle) strata slices for C3 (upper),
C5 (middle), and C7 (lower). The CWT spectral decomposition used a Morlet wavelet in narrow frequency bands around 38 Hz
as blue, 22 Hz as green, and 18 Hz as red co-rendered with coherence attribute, with our fluvial interpretation stages (right).
(Essam Saeid et al, 2018)
Horizon slice RGB Blend of 18, 21, 25 Hz Co-rendered with coherency

28. AMPLITUDE VERSUS OFFSET/ANGLE (AVO/AVA)
 Amplitude Variation with Offset (AVO) or
Amplitude Variation with Angle (AVA) become
popular in petroleum exploration industry
since introduced by Ostrander (1984).
 The gas-sand model used by Ostrander shows
the increasing of reflection amplitude with the
increasing of offset or angle and the term of
AVO/AVA.
 The presence of hydrocarbons alters the rock
properties within a field. This can cause
amplitude anomalies, where the amplitude is
either enhanced or decreased, depending on
the reservoir lithology.
 The Change in the ratio of the compressional
wave (P-Impedance) and the shear wave (S-
Impedance), translates to a change in
reflectivity with offset.
The reflection coefficient (Rp) of a normally incident P-wave
on a boundary is given by zero offset Zoeppritz equation:
where ρ1 and ρ2 are the densities of the upper and lower
layers, V1 and V2 are their respective P-wave velocities, and
ρ1V1 and ρ2V2 are the P- impedances of the upper and lower
layers respectively.
As the angle increases, the relationship with the shear wave
velocity becomes more important due to the

29. The Zoeppritz equations
A0
A2
B2
A1
B1
θ1
1
2
θ2
ILLUSTRATION OF HOW THE P-WAVE STRIKE
THE BOUNDARY AND SPLIT INTO 4 WAVES. A1,
A2, 1 AND 2 ARE AMPLITUDES AND ANGLES
OF REFLECTED AND TRANSMITTED P WAVE. B1,
B2, 1, AND 2 ARE AMPLITUDES AND ANGLES
OF REFLECTED AND TRANSMITTED S WAVE.
2
cos
2
sin
cos
sin
2
sin
-
2
cos
2
sin
2
cos
2
cos
-
2
sin
2
cos
2
sin
sin
-
cos
sin
cos
cos
sin
cos
sin
1
1
2
2
2
2
1
1
2
2
r
1
1
t
2
1
1
1
2
2
t
2
2
1
1
1
2
2
2
1
1
t
t
t
r
r
r
r
r
t
t
r
r
r
r
r
t
r
D
C
B
A
B
B















































































Where A = Rp reflection, B = Rs reflection, C=Tp transmission, D =
Ts transmission, α=Vp, β=Vs, ρ=density.
The 4x4 series of linear equations shown above is a good way of
deriving the exact amplitudes of a reflected P-wave as a function
of angle. But it does not give an intuitive understanding of how
these amplitudes relate to the various physical parameter. Several
approximations were made for Zoeppritz equation to get better
understanding on the relationships of the observed amplitude
with the physical properties.
Following equation is the Zoeppritz equations which related to rays model
shown in Figure 1.

30. Class 1 AVO anomalies are the hardest to identify as the
reflectivity naturally decreases with offset due to attenuation of the
seismic signal.
Class 2 near zero impedance contrast gas-sandstones
Class 3 is the simplest to identify as the amplitude increases with offset,
which acts against the natural attenuation.
Class 4 AVO anomalies are similar to Class 3, whereby the reservoir has a
lower acoustic impedance (AI) than the seal. However, the change in
gradient slope is due to different Vs properties (Castagna et al., 1998). The
Class 4 response is generally limited to coals or shallow unconsolidated
sands.

31. Approximations for Zoeppritz equations
The Two-Term Aki-Richards Equation
Intercept / gradient analysisis done with
the two-termAki-Richards equation.
A – Intercept
B - Gradient
Courtesy: CGG

32. AVO ATTRIBUTES
The most popular AVO attributes are:
(1)AVO Product : A*B
(2)Scaled Poisson’s Ratio Change : A+B
(3)Shear Reflectivity : A-B
(4)Fluid factor
This is an example of a Class 3 anomaly.
Forming the product of A and B, we get:
Top of sand : (-A)*(-B) = +AB
Base of sand : (+A)*(+B) = +AB
The AVO sum (A+B) shows a negative response at the top of the
reservoir (decrease in σ) and a positive response at the base
(increase in σ):
This gives a positive
“bright” response at both
top and base.
Top of gas sand
Base of gas sand
Simultaneous P-Impedance Inversion
Russell and Hampson, 2006
Courtesy: CGG

33. AVO ATTRIBUTES – SIMULTANEOUS INVERSION
 The  term, or incompressibility, is sensitive to pore
fluid, whereas the  term, or rigidity, is sensitive to
the rock matrix.
 It is most beneficial to cross-plot  -  to minimize
the effects of density.
 / is the most sensitive to variations in rock
properties going from shale into gas sand.
LMR Approach

34. GEOMETRIC ATTRIBUTES
A measure of the trace-to-trace similarity of the seismic waveform within a small analysis window
 Coherency, similarity, continuity, semblance and covariance are similar and relate to a measure of similarity
between a number of adjacent seismic traces (multi-trace analysis).
 Images discontinuities in your seismic data, instead of reflections.
 They convert data into a volume of discontinuity that reveals faults, fractures, and stratigraphic variations
 Alternative measure of waveform similarity
1. Cross correlation, 2. Semblance, variance, 3. Eigen structure, 4. Energy Ratio, 5. Gradient Structural Tensors (GST)
Volume based attribute Grid based attribute

35. COHERENCE ATTRIBUTE
Seismic (1184 ms) Coherence (1184 ms)
The standout of channels on the time slice from coherence
volume demonstrates the clarity and detail that coherence
brings out.
low coherence event ~ low values typically
displayed as dark colors
Coherence measures the similarity of the waveform between neighbouringtraces.
high coherence event ~ high values
typically displayed as light colors

36. GEOMETRIC ATTRIBUTES
Neg Pos
Seismic section
After structure-oriented filtering
Difference section
a)
b)
c)
(Data courtesy: Arcis Seismic Solutions, TGS)
Preconditioning of seismic data
Notice the inclined wavetrains of noise are seen
in the difference section, and the display after
structure oriented filtering looks clean with the
reflections much more coherence.

37. COHERENCE ATTRIBUTE
Neg Pos
Low High
Comparison of time slices (at 1333ms) from (above) seismic and (below) coherence volumes.
Notice the subtle channel signature is not clearly evident on the seismic data.
Stratal slices
Neg Pos Low High

38. COHERENCE ATTRIBUTE
Chopra & Marfurt, 2007
Time slice from the seismic volume;
Time slice from seismic coherence volume
Coherence slice overlaid on seismic slice

39. • Seismic section from the
Oligocene-Miocene succession
of the northern North Sea
• Note the development of a
series of low-displacement
normal faults
• A timeslice (yellow line) is
extracted from both the
reflectivity volume and
corresponding coherency
volume (see next slide)
1 km
SW NE
Image courtesy of Lidia Lonergan
1.25 k m
1.25 k m
Images courtesy of Lidia Lonergan
• Left – Time slice through
reflectivity volume. Faults are
poorly-imaged due to
structural ‘interference’ with
shallowly-dipping reflection
events (see previous slide)
• Right – Timeslice at the same
structural level through a
coherency volume. The faults
are very clearly-imaged

40. Coherence horizon slice
(better for stratigraphic analysis)
Coherence time slice
(better for fault and salt analysis)
Coherence: time slices vs horizon slices

41. DIP & AZIMUTH
redrawn from Mondt (1993)
• Dip - calculates the dip at each point on an
horizon.
• It represents the magnitude of the maximum slope
of the seismic reflections at a point, which is the
slope measured in the direction of the reflection
azimuth.
• Dip highlights structural trends, reflection bumps
and sags, and faults. On vertical editors, faults are
sometimes easier to follow with dip than with the
discontinuity attributes. Dip is a good background
attribute for volume blending.
• Azimuth - calculates the azimuth at each point in
degrees (˚) away from north and displays variations
in the direction of dip.
• Azimuth is the down-dip direction of the seismic
reflection in degrees from the y axis.
• Volume azimuth (f) is the down-dip direction of the
seismic reflection in degrees from the y axis.
Azimuth has the range of -180 to +180 degrees. of
synclinal and anticlinal reflections.
• When displayed with a circular monochrome color-
bar, it resembles illuminated apparent topography,
and is a good attribute for volume blending.
Azimuth highlights broad structural patterns and
reveals the flexure points

42. DIP & AZIMUTH
Seismic Volume Azimuth Volume Dip Volume

43. DIP & AZIMUTH
• A - Seismic section from the North Sea Basin illustrating the presence of
low-displacement normal faults in the Tertiary succession. Note the
mapped reflection event marked X
• B - Dip map of reflection event X illustrates the polygonal pattern of the
fault array
• C - Azimuth map of reflection event X illustrating the polygonal pattern
of the fault array in addition to the variable dip direction of individual
faults
from Jackson (2007)

44. CURVATURE
modified from Roberts (2001)
Curvature refers to the degree of bending of a
surface
(Roberts, 2001)
• A planar horizontal surface has no dip
and no curvature, whereas a titled planar
surface has dip but no curvature
• Curvature deemphasises the effects of
regional horizon dip; this differs from the
dip attribute, where high regional dip can
mask subtle, local dip changes
• Curvature has been used to investigate
the geometry and distribution of a range
of structural and stratigraphic features
(e.g. fault and fracture patterns,
carbonate build-ups)
(Above) Time-structure map (a) and curvature map (b)
illustrating the geometry of an incised deep-water
channel and associated faults (from Hart and Sagan,
2007)
Sign convention for 3D curvature
attributes:
Anticlinal: k > 0
Planar: k = 0
Synclinal: k < 0

45. A MULTIPLICITY OF CURVATURE ATTRIBUTES!
1.Mean Curvature
2.Gaussian Curvature
3.Rotation
4.Maximum curvature - Established use in fracture
prediction
5.Minimum curvature
6.Most positive curvature - Most useful for structural
interpretation
7.Most negative Curvature
8.Dip curvature
9.Strike curvature
10.Shape index
11.Curvedness
12.Shape index modulated by curvedness
See Roberts (2001) for definitions!
Mathematical Basis
Advances in seismic attributes for reservoir characterization
Coherence, curvature
Most-positive curvature
Most-negetive curvature

46. CURVATURE
Seismic
Most-positive
curvature (long-
wavelength)
Coherence
Most-positive curvature
(short-wavelength)
Most-negative
curvature (long-
wavelength)
Most-negative
curvature (short-
wavelength)

47. 1. Curvature attributes are a useful set of attributes that provide images of structure and
stratigraphy that complement those seen by the well-accepted coherence algorithms.
2. Additional noise can be suppressed by iteratively running spatial filtering on horizon
surfaces.
3. For the datasets under study, most-positive and most negative curvature offers better
interpretation of subtle fault detail than other attributes.
4. Volume curvature attributes provide valuable information on fracture orientation and
density in zones where seismic horizons are not trackable.
5. The orientations of the fault/fracture lineaments interpreted on curvature displays can
be combined in the form of rosediagrams, which in turn can be compared with similar
diagrams obtained from image logs to gain confidence in calibration.

48. PITFALLS
Coherence (or variance) cubes delineate the edges of mega blocks and faulted strata, curvature delineates
folds and flexures, while spectral components delineate lateral changes in thickness and lithology.
Seismic attributes are at their best in extracting subtle and easy to overlook features on high-quality seismic
data. However, seismic attributes can also exacerbate otherwise subtle effects such as acquisition footprint
and velocity pull-up/push-down, as well as small processing and velocity errors in seismic imaging.
In terms of attributes, structural pitfalls fall into two general categories: false structures due to seismic noise
and processing errors including velocity pull-up/push-down due to lateral variations in the overburden and
errors made in attribute computation by not accounting for structural dip.
Inadequate well control :
• Wells don't represent all variability within reservoir
• Use seismic modeling to infill gaps
Redundant attributes
• Different attributes highly correlated to one another
• Remove redundant attributes ; keep one that correlates best with rock property

49.  Seismic attributes describe shape or other characteristics of a seismic trace over specific intervals or at
specific times
 Seismic attributes are important because they enable interpreters to extract more information from seismic
data
 Seismic attributes can be derived from a single - trace or by comparison of multiple traces
 Volume-based attributes are derived directly from the seismic reflectivity volume. Largely free from
interpreter bias because they are derived directly from the original seismic volume rather than from
mapped horizons (cf. grid-based attributes)
 Volume-based attributes can be affected by noise in the initial input volume; caution should be
exercised when interpreting geological features...
 Grid-based attributes - generated directly from a mapped seismic horizon
 Calculated using an interpreted seismic horizon, thus the quality and interpretability of the resultant maps
are directly linked to the quality of the initial interpretation; this may be limited by the quality of the original
seismic dataset
 A key part of the seismic interpreter’s toolkit applicable in a wide variety of structural and stratigraphic
settings and across a range of scales
 Seismic attributes are used for qualitative analysis ( e.g. , data quality , seismic facies mapping ) and
quantitative analysis ( e.g. , net sand , porosity prediction)
SUMMARY

50. Thank you

51. Inversion transforms seismic reflection data into
rock and fluid properties.
The objective of seismic inversion is to convert
reflectivity data (interface properties) to layer
properties.
To determine elastic parameters, the reflectivity
from AVO effects must be inverted.
The most basic inversion calculates acoustic
impedance (density X velocity) of layers from
which predictions about lithology and porosity
can be made.
The more advanced inversion methods attempt
to discriminate specifically between lithology,
porosity, and fluid effects.
Simultaneous P-Impedance Inversion
Russell and Hampson, 2006
Recursive Trace Integration
Colored Inversion
Sparse Spike
Model-Based Inversion
Prestack Inversion (AVO Inversion)
Elastic Impedance
Extended Elastic Impedance
Simultaneous Inversion
Stochastic Inversion
Geostatistical
Bayesian

52.  Seismic attributes should be unique. You only need one attribute to measure a given seismic property.
Discard duplicate attributes. Where multiple attributes measure the same property, choose the one that works
best. If you can’t tell which one works best then it doesn’t matter which one you choose.
 Seismic attributes should have clear and useful meanings. If you don’t know what an attribute means, don’t
use it. If you know what it means but it isn’t useful, discard it. Prefer attributes with geological or geophysical
meaning; avoid attributes with purely mathematical meaning.
 Seismic attributes represent subsets of the information in the seismic data. Quantities that are not subsets of
the data are not attributes and should not be used as attributes.
 Attributes that differ only in resolution are the same attribute; treat them that way.
 Seismic attributes should not vary greatly in response to small changes in the data. Avoid overly sensitive
attributes.
 Not all seismic attributes are created equal. Avoid poorly designed attributes.
Source: “Too Many Seismic Attributes ?” by Arthur Barnes. Mar 2006, Vol 31 No.3 Publication of CSEG Recorder
Too Many Seismic Attributes ?

53. DIP & AZIMUTH
from Hoetz & Watters
(1992)
• An example from the Annerveen gas field in the
Netherlands Salt Basin
• Taken from one of the original papers which
presented examples of the application of grid-
based attribute analysis to structural interpretation
• The aim was to delineate the geometry of normal
faults developed within Carboniferous and
Rotliegend (Lower Permian), sub-salt strata; note
the mapped reflection event (base Zechstein Group
- BZG) from which the seismic attributes were
derived (see next slide)

54. GRAPHIC REPRESENTATION OF SEISMIC DATA

55. SEISMIC OUTPUT : GATHERS AND STACK DATA
Seismic gathers are the raw data from which seismic stacks are created. A gather is a family of traces (e.g.
shot-point gather is the family of all traces corresponding to the same source firing). Sorting of traces by
collecting traces that have the same midpoint (CMP) is called a common midpoint gather (CMP-gather).
Full stack seismic data is generated by summing all the offset traces together.

56. GEOLOGICAL SIGNIFICANCE OF SEISMIC ATTRIBUTES
Amplitude Lithological Contrasts
Bedding continuity
Bed spacing
Gross Porosity
Fluid content
Instantaneous Frequency Bed thickness
Lithological contrasts
Fluid content
Reflection Strength Lithological Contrasts
Bedding continuity
Bed spacing
Gross Porosity
Instantaneous Phase Bedding continuity
Polarity Polarity of seismic
Lithological contrasts

57. • Combination of vertical and timeslice displays from a standard reflectivity volume and a coherence cube illustrating the
application of coherency to imaging normal faults
• Note the clear expression of the normal faults on the vertical reflectivity slice and the coherency timeslice. The left-hand fault is
more poorly-imaged on the reflectivity timeslice and the vertical coherency slice. Better imaging on timeslices rather than vertical
slices is typical for coherency-type volumes
from Jackson & Kane (2012)

58. from Bahorich & Farmer (1995)
• Above-left – Timeslice through reflectivity volume, Gulf of Mexico, offshore USA.
Although poorly-defined lineations can be observed in various locations, it is
difficult to clearly visualise any structural or stratigraphic features at this structural
level
• Above-right – Timeslice through corresponding coherency volume. Note the
improved imaging of both faults and channel-shaped bodies (red arrows)

59. Figure 2. Principle of RMS, average absolute, average peak and interpolated maximum peak amplitude
computation
24.46
RMS
)
...
0
(5
8
1
RMS
a
N
1
RMS
2
2
N
1
i
2
i




 

38
.
20
N
a
.
N
1
i
i




Abs
Av
Interpolated
maximum peak
=39
average peak
amplitude = ?
27
5
25
38
37
30
5







60. Interpolated
maximum
trough = [-19] =
19
Average trough
amplitude =
14
2
10
-
18
-

38
Max. Absolute
amplitude = 

N
1
i
i
a
Total absolute
amplitude
5+0+18+10+30
+37+38+25
= 163
Figure 3. Principle of max. absolute, total absolute, average trough and interpolated maximum trough
amplitude computation

61. 


N
1
i
i
a
Total
amplitude
5+0-18-10 +
30+37+38+25
= 107
38
.
598
N
a
Energy
Average
N
1
i
2
i

 

4787
a
Energy
Total
N
1
i
2
i

 

0
a
of
no
a
a
i
N
1
i
i




Mean amplitude =15.29
Figure 4. Principle of total amplitude, mean amplitude, average energy and total energy computation

62. 2
N
1
i
i )
a
-
(a
N
1
V 


Variance of
amplitude
Average
Amplitude
= 13.38
= 423.15
3961.06
-
15.29)
-
(a
8
1
)
a
-
(a
N
1
Skew
3
N
1
i
i
N
1
i
3
i






 N
1
i
4
a
i
a
N
1
K
Kurtosis



 









= 280868
Figure 5. Principle of average, variance, skew and kurtosis amplitude

66. Factors that influences velocity of wave are porosity,
density, temperature, grain size, gas saturation, frequency,
external pressure, pore pressure and stress. The curve
between P wave velocity and various parameters are
shown in the figure below. The most influential factor on
the P wave velocity changes include porosity, gas
saturation, external pressure and pore pressure. The
relationship between gas saturation and P wave velocity
drops drastically resulting in the formation of anomalies
such as DHI, AVO, and others.