1. Oil & Natural Gas Corporation Ltd.
Seismic Data Interpretation

2. What is seismic interpretation?
 Interpretation is telling the geologic story
contained in seismic data.
 It is correlating the features we see in seismic
data with elements of geology as we know
them.
 Depositional environments and depositional history
 Structure
 Anticline, syncline
 Faults
 Stratigraphy
 unconformity
 Pinch-out
 Channels, reefs, salt domes

3. Objective
 Prospect generation and identification of suitable locations
for drilling by interpreting subsurface geo-data
 Petroleum system
 Reservoir rock
 Porous and permeable sandstone, limestone
 Any other rock type forming trap, e.g., fractured shales
 Source rock
 Shale/carbonates
 Cap rock
 Shale/carbonates
 Reservoir characterization
 Estimation of reservoir parameters
 Area, thickness, porosity, saturation etc.
 The primary goal of seismic interpretation is to make
maps that provide geologic information (reservoir
depth structure, thickness, porosity, etc.).

4. Petroleum System
oil
Cap rock

5. Traps

6. Interpretation
functionalities
 Project and data management
 Data conditioning (interpretive processing)
 Scaling, filtering, wavelet processing etc.
 Integration of various types of data (seismic, well etc.)
 Calibration (well to seismic tie)
 Synthetic seismogram generation and correlation with seismic
 Visualization
 Horizon and fault Correlation
 Generation of time maps
 Depth conversion and generation of depth maps
 Special studies
 Seismic Attributes
 Direct hydrocarbon indicators (DHI) and AVO
 Seismic Inversion
 Time lapse reservoir monitoring
 Integration of structure, attributes, impedance, geologic model etc.
 Prospect generation and location identification
 Reports/proposals

7. Pre-requisite of
interpretation
 Knowledge of geology and geophysical processes
 Type of data and type of information which can be extracted
 Objective of interpretation and amenability of seismic data
 Elements of seismic trace data
 Modes of display
 Amplitude, time and frequency
 Role of colours and colour bar
 Contrasting and gradational colour scheme
 Polarity and phase conventions
 Resolution and detectability
 Seismic to well tie
 Character based matching between synthetic and seismic
 Depth to time conversion (T-D curves)
 Check shots, VSP, Synthetics

8. A seismic section showing colour convention and other display elements
Elements of display
Positive amplitude Blue
Negative amplitude Red

9. Elements of display
- max + max
Variable area wiggle display
+/- zero crossing
-/+ zero crossing

10. 3-D Cube of
seismic data
Elements of display
Time slice
In-linecross-line

11. Successive time
slices depict the
anticlinal closures
Time 2430
Time 2390
Elements of display
Vertical section
and time slices

12. Top and bottom of a gas reservoir
(low impedance zone) in (a)
American polarity and (b) European
polarity
Polarity Convention
American polarity is
described as: An increase in
impedance yields positive
amplitude normally displayed in
blue. A decrease in impedance
yields negative amplitude
normally displayed in red.
European (or Australian)
polarity is described as the
reverse, namely: An increase in
impedance yields negative
amplitude normally displayed in
red. A decrease in impedance
yields positive amplitude
normally displayed in blue.
American
European

13. Phase
The task of tying seismic data and well data
together and hence identifying seismic horizons is
often oversimplified. The task requires knowledge
of velocity, phase, polarity and tuning effects.
Zero phase data makes all aspects of interpretation
easier and everyone knows that zero phase is
desirable. However, zero phase is difficult to
accomplish and often it is not achieved in
processing. Hence interpreters always need to
check the phase and polarity of their data.

14. Resolution
The ability to separate two features that are close
together
The minimum separation of two bodies before their
individual identities are lost on resultant map or
cross-section
The resolving power of seismic data is always
measured in terms of seismic wavelength (λ=V/F)
Limit of separability = λ/4
The predominant frequency decreases with depth because
the higher frequencies in the seismic signal are more
quickly attenuated. Wavelength increases with depth.
Resolution decreases with depth
For thinner intervals amplitude is progressively attenuated
until Limit of visibility=λ/25 is reached when reflection
signal becomes obscured by the background noise

15. Limit of Separability
Age of rocks Very
young
young medium old Very old
Depth Very
shallow
shallow medium deep Very deep
Velocity (m/s) 1600 2000 3500 5000 6000
Predominant
frequency
70 50 35 25 20
Wavelength 23 40 100 200 300
Separability 6 10 25 50 75

16. Limit of visibility
 Factors affecting the
visibility
 Impedance contrast of
the geologic layer of
interest relative to the
embedding material
 Random and systematic
noise in the data
 Phase of the data or
shape of seismic wavelet
 It may be less than 1 m
to more than 40 m
Limit of visibility
S/N Example Limit
Poor Water sand
poor data
λ/8
Moderate Wayer or oil
sand fairly
good data
λ/12
High Gas sand
good data
λ/20
Outstanding Gas sand
excellent
data
λ/30

17. STRUCTURAL
INTERPRETATION
Calibration (Well-to-seismic-tie)
Correlation (horizons and faults)
Map generation (gridding and contouring)

18. For geologic information from seismic data, nearby wells are
correlated to seismic reflectors. Synthetic seismograms
(synthetics) provide this link by converting rock properties from
well logs to a synthetic trace.
Synthetics make “rocks look like wiggles,” using the convolution
model (T = RC * W), which states that traces (T) are the result of
convolving (*) the reflection coefficient series (RC) with the
wavelet (W). When seismic data are acquired, a source wavelet is
sent into the earth, reflected back (convolved) to the surface at
geologic boundaries (RC), and recorded as a trace (T).
RC is calculated from velocity and density logs
Well to seismic tie Synthetic Seismograms

19. RC for normal incidence is
RC = (r1v1- r2v2) /(r1v1+ r2v2)
where v1, v2 are P-wave velocities (sonic
log) and r1, r2 are densities (density log) in
the layer above (1) and below (2) the
reflecting boundary. The normal incidence
assumption is generally valid, except where
velocity and density contrasts are very large
(gas sands, coal, hard streaks, etc.). When
these exceptions are critical to the
interpretation, RC needs to be calculated as
a function of angle (AVA) from more
complex equations and a third parameter, S-
wave velocity (shear log).
Well to seismic tie Synthetic Seismograms
(1) r1,v1
(2) r2,v2

20. Synthetic to seismic matching before starting the interpretation. Understand
geologic elements (model) and their geophysical responses (Trace).
DT RHOB IMP GR LLD RC
Black
traces
Seismic
Red
traces
synthetic

21. Seismic signatures of pay sands in B12-11. Sandstone pays are marked
by troughs. Pay1 and Pay3 high negative amplitudes
Overlay of synthetic
and logs on seismic
sections. Here synthetic
is shown by yellow trace
Well to seismic tie Synthetic Seismograms

22. Horizon Correlation
 Identification of sequence boundaries
 Reflection configuration and termination pattern, Onlap, Top lap and
Down Lap
 Tracking
 Auto and manual mode
 Auto dip and correlation
 3D tracking
Fault correlation
 Picking on vertical and horizontal sections
 Fault plane correlation
 Computing throw of faults

23. Map generation
 Base map generation
 Depicting seismic
 Well location
 Cultural data
 Block boundaries
 Scale, coordinates and legends
 Griding
 Various method with faults and without faults
 Incorporating heaves and throw
 Contouring
 Over lay of attributes
 Map analysis

24. Time map generated from horizon and fault correlation

25. Seismic attributes

26. Seismic attributes
 Attributes are derivatives of basic seismic
measurements/Information
 Seismic attributes extract information from seismic data that is
otherwise hidden in the data
 These information can be used for predicting, characterizing,
and monitoring hydrocarbon reservoirs
 Basic information
 Time
 Amplitude
 Frequency
 Attenuation
 Phase
 Most attributes are derived from normal stacked and migrated
data volume
 Can be derived from Pre-stack data (AVO)

27. Seismic attributes
Attribute Information
Time-derived Structural information
Amplitude-derived Stratigraphic and
reservoir
Frequency-derived Stratigraphic and
reservoir
Attenuation Permeability

28. Direct hydrocarbon indicator (DHI)

29. DHI
 Bright spot
 Water sand has lower
impedance than embedding
medium and impedance of
gas sand is further reduced.
 Top and base reflections
show natural pairing
 If sand is thick enough, flat
spot or fluid contact
reflection should be visible
between gas sand and water
sand
 Flat spot will have opposite
polarity than bright spot at
top
 More common in shallower
sandstone reservoirs ( Mio-
Plio)
Peak on
synthetic
seismogram

30. DHI
 Dim spot
 Water sand has higher
impedance than
embedding medium and
water is replaced by
water impedance is
reduced
 Contrast is reduced at
upper and lower
boundaries and reservoir
is seen as dim spot
 Flat spot can be expected
at the point where the
dimming occurs.
 More common in deeper
sandstone reservoirs
where shale impedance
is lower than sandstone
impedance

31. DHI
 Polarity reversal
 Water sand is of higher
impedance than enclosing
medium and gas sand
impedance is lower than
enclosing medium
 The polarity of reflections
from water-sand-shale
interface and gas-sand-
shale interface have
opposite sign and thus
polarity reversal
 Flat spot from GWC may
show bright amplitude .
 More common in medium
depth range sandstone
reservoirs

32. Validation of DHI
 AVO
 In many practical cases gas sands show an
increase of amplitude with offset
 Many difficulties of theoretical and practical nature
 Data is pre-stack hence lower S/N

33. Pitfalls in DHI
Exploration prospects based on a sound geologic model
and supported by seismic amplitude anomalies are highly
prospective and are usually assigned a high probability of
success. However, a fraction of such prospects, perhaps
10-30%, result in dry holes. Postdrill appraisal can usually
assign these results to one or more of the following
factors:
• Unusually strong lithologic variations
• Fizz water and low gas saturation
• Superposition of seismic reflections
• Contamination of the seismic signal by multiples or other
undesired energy

34. In seismic gather reflection coefficient at an incidence angle θ
is given by: R(θ ) = Ro + BSin2θ
Where: Ro: RC at zero offset and R (θ) : RC at angle θ
B is a gradient term which produces the AVO effect. It is
dependent on changes in density, ρ, P-wave velocity, VP, and S-
wave velocity, Vs.
Principle of AVO Analysis

35. Principle of AVO Analysis
The AVO response is dependent on the properties of P-wave
velocity (VP), S-wave velocity (VS), and density (ρ) in a
porous reservoir rock. This involves the matrix material, the
porosity, and the fluids filling the pores.
Poisson’s Ratio
K = the bulk modulus, or the reciprocal of
compressibility.
μ = The shear modulus, modulus of rigidity

36. Increase of
amplitude with
offset
Principle of AVO Analysis
Stack
Gather

37. Principle of AVO Analysis
S-wave
P-wave
Water Saturation
Velocity(m/s)
With increase
in gas
saturation, P-
wave velocity
drops
dramatically,
but S-wave
velocity only
increases
slightly.

38. •Rutherford and Williams (1989) derived the following
classification scheme for AVO anomalies, with further
modifications by Ross and Kinman (1995) and Castagna (1997):
• Class 1: High impedance gas sand with decreasing AVO
• Class 2: Near-zero impedance contrast
• Class 2p: Same as 2, with polarity change
• Class 3: Low impedance gas sand with increasing AVO
• Class 4: Low impedance sand with decreasing AVO
Rutherford/Williams Classification

39. The generic AVO curves at the top of the gas sand

40. Inversion

41. Inversion
 Inversion is the process of extracting, from the
seismic data, the underlying geology which gave
rise to that seismic.
 Traditionally, inversion has been applied to post-
stack seismic data, with the aim of extracting
acoustic impedance volumes.
 Recently, inversion has been extended to pre-stack
seismic data, with the aim of extracting both
acoustic and shear impedance volumes. This
allows the calculation of pore fluids.
 Another recent development is to use inversion
results to directly predict lithologic parameters such
as porosity and water saturation.

42. Inversion Non-Uniqueness
 All inversion algorithms suffer from “non-
uniqueness”.
 There is more than one possible geological model
consistent with the seismic data.
 The only way to decidebetween the possibilities is
to use other information, not present in the seismic
data.
 This other information is usually provided in two
ways:
 The initial guess model
 constraints on how far the final result may deviate from the
initial guess
 The final result always depends on the “other
information” as well as the seismic data

43. Acoustic Impedance
The definition of the zero-offset reflection coefficient, shown in the figure above. R0
, the reflection coefficient, is the amplitude of the seismic peak shown and
represents relative impedance contrast.
1122
1122
0
VV
VV
R





Seismic
raypath
Interface at
depth = d
1 V1
2 V2
t
Reflection at time
t = 2d/V1
Geology Seismic
Surface
Seismic
Wavelet
Shale
Gas Sand

44. Impedance Reflectivity
Wavelet
Seismic trace
The common forward model for all inversions
Inversion

45. ImpedanceReflectivity
Inverse
Wavelet
Seismic
Inversion tries to reverse the forward model
Inverse Model

46. QC plot showing accuracy of inversion process. Error between
synthetic generated from real and inverted impedance is
insignificant

47. Differentiating different lithology types through acoustic
impedance inversion

48. Impedance profile distinguishing between different rock types
Limestone
Sandstone
Basement

49. Thanks