7
Quantitative Interpretation
Quantitative Interpretation
Objectives
-
To use seismic attributes to derive rock properties with care
-
Familiar with many different ways of doing quantitative
interpretation
-
Understand data precondition for quantitative interpretation
Why do we need quantitative
interpretation?
-
It is tempting to instantly relate mapped seismic attribute
patterns to geology without considering the physics
(qualitative Interpretation)
-
It is very easy to derive spurious correlation of attributes and
well data. Later there is a need to find physical explanation
for seismic attributes
-
To understand key factors that effect seismic amplitude as it
has the potential to directly impact the risk on exploration
The Essentials of Amplitude Interpretation
Wavelet Shape
(phase and polarity)
Type of Seismic
(full, near, far,
intercept,
gradient)
Interpretation
Gas Sand
Carbonate
High porosity brine sand
Igneous
over pressure
tuning
imaging artifact, ??
Reflection Model
+
Shale
Amp.
0
Offset
Sand
-
Basic Rock Properties
Two basic rock properties that control the elastic response
of isotropic rocks to sound waves
-
Acoustic Impedance (AI) = Vp* Density
-
Ratio of compressional to shear waves velocity (Vp/Vs)
Compression and Shear Wave Velocity
-
Vp and Vs relate to rock properties because they can be thought
of as the degree to which rock can be changed in its volume
versus the degree to which it can be changed in its shape
Relationship Velocity to Rock Properties
Stress (S) =
Force (kg-m/sec2)/ Area (pr2)
Strain
=
Amount of deformation an object experiences
compared to its original size and shape
(dimensionless)
S
Relationship Velocity to Rock Properties
POISSON RATIO (s) and Vp/Vs Relationship
First Order Rock Property Relationship Of
Different Elastic Parameters
1.
Porosity decrease with
depth due to compaction
2.
Bulk density is linearly
relate to porosity
3.
AI is negatively correlated
to porosity
4.
For a given lithology there
is strong relationship
between density and
velocity
5.
For a given porosity
density and AI can be
lowered (red arrow) by less
dense fluid (HC) and more
pore space
6.
Vp/Vs decreases with
depth
7.
HC presence lowers
Poisson Ratio and AI
First Order Rock Property Relationship with Pressure
1.
Vp (and Vs) increase with
increasing effective
pressure (Pe)
2.
Over pressure sand tend to
have higher Poisson
Rations than
overpressured gas sands
3.
Experimental data show
that brine sands generally
show increasing poisson
ratio with decreasing
effective pressure. But it is
also possible that there is a
slight decrease with
decreasing effective
pressure
Normal Incidence Reflectivity
NOTE:
-
The primary factor in seismic reflectivity is the RC which depends
on the contrast of AI at interface
-
When the seismic is normally incident to the interface (incident
angle is 0) the amount of energy reflected is proportional to the RC
Non-normal Incidence Reflectivity
NOTE:
-
When the seismic is incident at non-normal angle to the interface
(incident angle is < 0) the amount of energy reflected not only depends
on AI contrast, but also Poisson Ration contrast (Zoeppritz 1919)
Angle of Incident vs transmitted waves
Simplification of Zoeppritz Equation
Amplitude Changes With Angle
Linearization of Zeoppritz Equation
NOTE:
-
To simplify the equation so that variation in amplitude is linear in terms of Sin2q
-
To represent AVO response in terms of normal incidence amplitude (Ro) and the
Gradient (G) (Shuey 1985)
-
However, Linearization becomes invalid at large angle
Processing For Relative Amplitude
Objective of AVO Processing:
-
To remove noise and propagation effects unrelated to primary reflectivity of rock
units whilst preserving (as much as possible) the relative amplitude across the prestack gather.
Seismic Attributes
• Attributes is the quantity of envelop amplitude, instantaneous
phase, instantaneous frequency and other measurements
derived from seismic data
• Attributes can be extracted from any type of data (amplitude,
velocity, complex trace, etc.).
• Interpreters use attributes in reservoir delineation and
reservoir characterization.
• Interpreters should understand the physical processes
associated with attribute responses.
• If you do not understand why attributes are related to
reservoir characteristics, use these attributes with caution.
Attributes Product of Seismic Inversion
Elastic Attribute Parameters
Vp:
Vs:
r:
Ip:
Is:
l:
m:
Q:
s:
R0:
G:
Compressional velocity
Shear velocity
Density
P impedance = acoustic impedance
S impedance
Lambda, compressibility
Mu, rigidity
Incidence angle
Sigma, Poisson’s ratio
Zero offset reflectivity, intercept of elastic impedance curve
Gradient of elastic impedance curve
Elastic Attributes
Vp/Vs Ratio
Poisson Ratio
Acoustic Impedance
Shear Impedance
Rho Mu
Rho Lambda
Lambda Mu Ratio
Seismic Attributes Group
• amplitude statistics
• complex trace statistics
• spectral statistics
• sequence statistics
Statistic calculation of any input data
Seismic Attributes Group
• amplitude statistics
• complex trace statistics
• spectral statistics
• sequence statistics
Complex trace calculation of input
amplitude traces
Seismic Attributes Group
• amplitude statistics
• complex trace statistics
• spectral statistics
• sequence statistics
Calculating the characteristic of power
spectra of the data in frequency domain
Seismic Attributes Group
• amplitude statistics
• complex trace statistics
• spectral statistics
• sequence statistics
These attributes focus upon
energy build-up in a
sequence, polarity
comparisons, and amplitude
threshold analysis.
RMS Amplitude
• RMS amplitude is calculated as the square root of the average of the squares of
the amplitudes found in the analysis window
• Because amplitudes are squared before averaging, the RMS computation is very
sensitive to extreme amplitude values.
Average Absolute Amplitude
• For each trace, the absolute values of the amplitudes in the analysis window are
added; then the total is divided by the number of samples in the window to yield the
mean.
• Average absolute amplitude is not nearly as sensitive to extreme amplitudes as is
RMS amplitude, which involves squaring of the amplitude values.
Maximum Peak Amplitude
For each trace, PAL does a parabolic fit through the maximum positive amplitude in
the analysis window and the two samples on either side of it.
The maximum value along that curve is interpolated and output to the attribute
horizon file.
Average Peak Amplitude
To calculate the average peak amplitude for each trace, all the positive values
within the analysis window are added; then the total is divided by the number of
positive samples within the window.
Maximum Trough Amplitude
• For each trace, PAL does a parabolic fit through the maximum negative amplitude in
the analysis window and the two samples on either side of it.
• The maximum negative value along that curve is interpolated and the absolute value
is output to the attribute horizon file
Maximum Absolute Amplitude
• To calculate the Maximum Absolute Amplitude for each trace, PAL calculates the peak
and trough values within the analysis window and determines the largest peak or
trough.
• AL then does a parabolic fit through the values in this peak or trough and the two
samples on either side of it. The maximum value is interpolated and output for that
trace.
Total Absolute Amplitude
For each trace, the sum of all absolute trace amplitudes within the specified
window are output to the attribute horizon file.
Total Amplitude
For each trace, Total Amplitude computes the total amplitude (integration of
amplitude) for samples within the horizons.
Average Energy
For each trace, the squared values of the amplitudes in the analysis window are added.
The total is then divided by the number of samples in the window to yield the mean
Total Energy
For each trace, the squared values of the amplitudes in the analysis window are added.
Maximum Trough Amplitude
For each trace, PAL does a parabolic fit through the maximum negative amplitude in
the analysis window and the two samples on either side of it. The maximum negative
value along that curve is interpolated and the absolute value is output to the attribute
horizon file.
Mean Amplitude
For each trace, this process adds the values of the amplitudes in the analysis
window and then divides the sum by the number of non-zero sample values.
Attribute Sensitivity
Summary Of Attribute Applications
Quantitative Analysis Methods
Linear Regression
Principal Component
Neural Network
Region Growing
Classification
Discriminate Analysis
Cluster Analysis
Thresholding
Linear Regression Data Analysis
Linear Regression Data Analysis
Linear Regression Data Analysis
Linear Regression Data Analysis
Using Rank Correlation
Well data and Seismic Attribute Correlation
Seismic Section
Generated
Impedance
Section
45
RMS Amplitude extraction from
Seismic Amplitude Cube
RMS Amplitude extraction from
Impedance Cube
46
Linear Regression Data Analysis
Linear Regression Data Analysis
Seismic Derived Net Pay Map
Waveform Classification
Supervised Waveform Classification
• Use Similarity Values
to compare the target
wavelets to the reference
wavelet.
• The darker areas in this
display identify wavelets
that more closely match
the reference wavelet
than those wavelets
mapped in white.
Un-supervised Waveform Classification
Quantitative Analysis Result Using Discriminant Analysis
Seismic Attributes
Seismic Attributes
Seismic Attributes
Seismic Attributes
Seismic Attributes
Seismic Attributes
Seismic Attributes
Seismic Attributes
Seismic Attributes
Seismic Attributes
Convolution Model & Polarity Convention