AVO attribute analysis for the identification of gas bearing sands

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AVO ATTRIBUTE ANALYSIS FOR THE IDENTIFICATION OF
GAS BEARING SANDS
BY
RASHI
ROLL NO. GP-05
A DISSERTATION SUBMITTED TO DEPARTMENT OF
GEOPHYSICS, KURUKSHETRA UNIVERSITY IN PARTIAL
FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF
MASTER OF TECHNOLOGY
IN
APPLIED GEOPHYSICS
MAY, 2018
SUPERVISED BY: PROF. DINESH KUMAR
AND SHRI ABHIJIT SONOWAL

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DECLARATION
I, Rashi, Roll. No. GP-05 a bonafide student of Department of Geophysics, Kurukshetra
University hereby declare that the Dissertation entitled “AVO attribute analysis for the
identification of gas bearing sands” submitted by me in partial fulfillment of the requirements
for the award of the Degree of Master of Technology in Applied Geophysics is my original
work.
Place: Kurukshetra
Date: Signature of the Candidate

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To my family and friends

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Table of contents
Acknowledgement……………………………………………………………………………...... vii
List of figures……………………………………………………………………………………... viii
List of tables………………………………………………………………………………………. ix
1. Introduction………………………………………………………………..…… 01
1.1 Seismic methods and oil exploration ……………………………………………... …. 01
1.2 AVO (Amplitude variation with offset)………..……………………………………… 10
1.3 Software used………………………………………….………………………………. 14
1.4 Objective of the dissertation………...…………………………………………………. 15
1.5 Outline……………………………………………………………………………… … 15
2. Dataset used & Study area…………………….………………………………..16
2.1 Dataset used………………………………………………………………………… … 16
2.2 Study area…………………………………………………..……….………………. …18
2.3 Geological setup of the study area ……………………………………………………. 20
3. Methodology Adopted…………………………………………………….…….21
3.1 Intercept Gradient analysis………………………………………………………. …22
3.2 Workflow…………………………………………………………..…… ….24
4. Data analysis and Results ….……………………………………….…………. 26
4.1 Well log data analysis………………………………………………………..…… …26
4.2 Well to Seismic tie…………………………………………………………………. … 30
4.3 Gradient analysis……………………………………………………………………. .. 31
4.4 Creation of volume from angle gathers…………………………………………….. …33
4.5 AVO analysis at volume scale………………………………………………………… 37
5. Discussion and Conclusion…………………………………………………….. 39
6. References ……………………………………………………………………… 40

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ACKNOWLEDGEMENT
I would like to express the deepest appreciation to my dissertation guide Shri Abhijit Sonowal,
Dy. Chief Geophysicist, Oil India Limited for his constant support, guidance during the
dissertation work. I am thankful to Shri G.V.J. Rao, CGM-Geophysics (i/c) for his permission to
use resources available in the Department. I am grateful to Prof. B.S. Chaudhary, Chairman,
Department of Geophysics, Kurukshetra University, for diligently helping me out in all
endeavours.
I am highly indebted to Prof. Dinesh Kumar, my Dissertation supervisor who has the attitude and
substance of a genius; you continually and convincingly conveyed a spirit of adventure in regard
to research and excitement in regard to teaching.
I am and always be thankful to my parents for the kind of environment they gave me while
bringing me up. You have always been my friends rather than parents. I am proud to be your
daughter! Your support has been invaluable. I am thankful to Shri D.S. Manral, DGM-
Geophysics, Oil India Limited for his support & guidance throughout this study. And then there
are my seniors Shri Kartik Sharma and Shri Amrinder Sharma who have always helped me in
various ups and downs during this work. I am thankful to other members of Seismic Imaging &
Modeling Centre for their support. Thank you for being there for me!
Last but not the least, I bow to Maa Sarasvati for what I am today is because of her blessings.
Rashi

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List of figures
Chapter 1
Figure 1.1 Bats using sound waves to locate their prey................................................................... 3
Figure 1.2 Seismic land survey……………………………………………..…………….............. 4
Figure 1.3 Seismic offshore survey .....…..………………………………………………………. 5
Figure 1.4 Picture (example) of the raw data that is required to be processed for the
subsurface image……………………………………………………………………………….. 5
Figure 1.5 Seismic data translated in to a 3-D picture (example data)………………………… 6
Figure 1.6 Incident, reflected and refracted waves at the interface of two media (viz. 1 and 2)… 9
Figure 1.7 Model CMP gathers contrasting the expected AVO response of a typical wet sand and
gas sand. The increasing amplitudes at far offsets for the gas are anomalous with respect to most
other reflections………………………………………………………………………………..… 11
Figure 1.8 Recording the amplitudes corresponding to a CMP for different angle of incidence. 12
Figure 1.9 Reflection Coefficient variation with the angle of incidence…………...................... 14
Chapter 2
Figure 2.1 Base map of the study area………………………………………………….……….. 17
Figure 2.2 Tectonic map of North-Eastern India……………………………………...………… 18
Figure 2.3 Geology of Upper Assam…………………………………………………….……… 19
Chapter 3
Figure 3.1 Curves showing reservoir top (red) and base (green)………………………….…….. 22
Figure 3.2 Top and base of the reservoir in the gather…………………………………….……. 22
Figure 3.3 Intercept and gradient product plot (data example)…………………………….…… 23
Figure 3.4 Workflow of the methodology adopted…………………………………………...…. 24

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Chapter 4
Figure 4.1 Zone of investigation along marked on the recorded logs and other basic derived
values………………………………………………………………………………….…………28
Figure 4.2 (a) Cross-plot of the Gamma ray (API) and P-wave impedance……………………..29
Figure 4.2 (b) Display curve of depths corresponding to hydrocarbon saturated zone………….29
Figure 4.3 The well log curves on the first 4 tracks and well tie between the synthetic and
seismic wavelet (7th track) and the seismic gather………………………………………………31
Figure 4.4 (a) AVO analysis corresponding to the event at 1903 ms (well top) and 1922ms (well
base)………………………………………………………………………………….…………..32
Figure 4.4 (b) AVO analysis showing top and base of the gas sand on the basis of intercept and
gradient properties with the help of red and green colored blocks………………………………32
Figure 4.5 Seismic trace is preserving the same character as shown by the synthetic trace…….33
Figure 4.6 (a) Offset gather………………………………………………………………………34
Figure 4.6 (b) Angle gather………………………………………………………………………34
Figure 4.7 Angle gather preserving the same character of amplitude (increasing in a negative
sense)……………………………………………………………………………………………..35
Figure 4.8 Attribute analysis for the synthetic as well as seismic data…………………………..36
Figure 4.9 Crossplot of the intercept and gradient values for both seismic and synthetic
trace……………………………………………………………………………………………....36
Figure 4.10 AVO attribute analysis for the entire volume…….…………………………………37
Figure 4.11 CDP stack overlain on the reservoir top (red) and base (blue)……………………...38
Chapter 5
Figure 5.1 Intercept Gradient product analysis, showing the areal extension of the
hydrocarbon...................................................................................................................................39

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List of tables
Table 1 Classification of sands………………………………………………………………......13
Table 2 Basic details of seismic data used……………………………………………………….16

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Chapter 1
Introduction
1.1 SEISMIC METHODS AND OIL EXPLORATION.
1.1.1 A BRIEF HISTORY OF REFRACTION AND REFLECTION METHOD.
The earliest efforts to locate oil-bearing structures by geophysical tools involved gravity
measurements. Shortly before the beginning of the present century, Baron Roland von Eotvos, of
Hungary, completed development of the torsion balance that bears his name. At about the same
time, seismic refraction equipment, very crude by modern standards, was brought from Germany
to look for salt domes in the Gulf Coast. In 1919, Ludger Mintrop (a German researcher) had
applied for a German patent on locating and measuring depths to subsurface features by
refraction profiling (Dobrin, Milton B. and Savit Carl H.)
Both the torsion-balance and refraction campaigns were successful in locating salt domes as
early as 1924. The gravity surveys led to the discovery of the productive Nash domes, and the
seismic shooting was responsible for finding the Orchard dome, both in Texas. These successes
led to more widespread application of the two techniques, and by 1929 virtually all the
piercement-type domes in the Gulf Coast had been discovered.
Early Reflection Work:
The earliest experiments with the seismic reflection method were carried out by J.C. Karcher
from 1919 to 1921(Dobrin, Milton B. and Savit Carl H.). To demonstrate the potential of the
method for oil exploration, he mapped a shallow reflecting bed in central Oklahoma early in
1921. On the 50th anniversary of this event, in April 1971, a monument was dedicated at the site
where these tests had been conducted. It was until 1927; however that reflection method was put
to work for routine exploration. In that year the geophysical research corporation used the
technique to discover the Maud field in Oklahoma. By the early 1930s, reflection became the
most widely used of all geophysical techniques.

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1.1.2 GENERAL THEORY.
Planet Earth! If you look closer you will see whole of the world exist beneath the surface of land
and sea. Layer after layer, rock structure goes deep under the Earth’s crust and trapped within
these structures along with other liquids you will often find deposits of oil and natural gas; the
world’s two most important sources of energy. These famous fuels are in constant demand
because they make the world go round, day in and day out.
So, how do we find something completely hidden beneath the earth’s surface?
It’s a mystery that people in Oil and Gas industry are always trying to solve and for a very good
reason drilling for hydrocarbon is expensive and before they spend money on equipment and
cruise, exploration and production (E&P) company needs a reliable strategy for pinpointing
where to drill!
Geo-scientists have a secret weapon called as seismic exploration and it involves sending the
acoustic energy which takes the form of wavelet in to the ground to get a sound picture beneath
the surface. It’s complicated.
So, let’s start with the analogy of bats (figure 1.1). Bats can’t see very well. So they send out
little waves of sound that bounces off of objects and then go back to their ears. It’s called
SONAR (Sound Navigation And Ranging). It gives them, what you might call a sound picture of
their world. That’s a good example of how nature already uses a form of seismic acoustic
imaging to locate objects. Doctors also use it for ultrasound imaging.

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Figure 1.1 Bats using sound waves to locate their prey. (Source: https://askabiologist.asu.edu/echolocation)
Geoscientists use the man made tools to make the sound wavelets listen to them and then record
them when you want to know if oil and gas deposits are in a particular area. Geophysical
companies bring large trucks that have big vibrators on them. Most of the time, this is what
generates the acoustic energy or vibrations. They use geophones to hear the reflected sound, but
sometimes they set off small buried charges. They set many geophones on the ground in a line
and they are attached to a recorder inside the truck (figure 1.2). The vibrator sends thousands of
wavelets down in to all the different layers of the earth. Some of the wavelets bounce off the
boundaries between the rocks below the surface and are reflected back to the geophones that are
waiting to record them. Each geophone along the cable sends the received wavelets to the
recording truck where they are recorded and stored.

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Figure 1.2 Seismic land survey.( Source: http://www.argas.com/land-data-acquisition/ )
Although the wavelets reach in to the subsurface of the ocean (That’s offshore seismic and it just
require a different device to send out the wavelet and record those that are reflected back out). At
sea, a seismic crew works off a vessel with a specially designed back in. So, it’s easier to lay
floating cables or streamers and all along the length of streamers, hydrophones are attached one
after another. Several of these hydrophone streamers are pulled behind the vessel at once.
Acoustic sources (for example- air gun) are towed behind the vessel in front of the streamers and
release compressed air which creates the wavelets. These wavelets travel through the water and
in to the subsurface below where just like on land they bounce off the rock layers and then return
to the hydrophones to be recorded (figure 1.3).

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Figure 1.3 Seismic offshore survey. (Source:
https://www.marinelog.com/index.php?option=com_k2&view=item&id=7020:boem-paves-the-way-for-us-
east-coast-seismic&Itemid=230 )
Figure 1.4 Picture (example) of the raw data that is required to be processed for the subsurface image.
(Source: https://www.youtube.com/watch?v=hxJa7EvYoFI)
Here’s what seismic looks like after it’s been recorded (figure 1.4). Basically it’s a bunch of
squiggles. There are still a few more steps to go before it begins to look like an actual picture of
the earth’s interior. Right now, the data is still in it’s raw form. To get a picture that actually

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looks like the earth beneath us, the data has to be processed. It takes a large supercomputing PC
cluster to process the seismic data. These computers go through all the different traces made by
the wavelet and filter out most of the things we don’t need, such as vibrations made by a tractor
in a field nearby. Using really amazing computer applications and working on state-of-the-art
workstations geo-scientists can see the seismic data translated in to a 3D picture (figure 1.5).
You might be thinking, I don’t see any oil and gas there?
Figure 1.5 Seismic data translated in to a 3-D picture (example data). (Source:
https://www.youtube.com/watch?v=hxJa7EvYoFI)
But believe or not, geo-scientists can look at this processed data with their trained eyes. And
make informed decisions about, whether or not, oil and gas deposits are in the geologic
structures. Seismic data leads to a high percentage of drilling success with less risk to the
environment. And in a world where the demand for oil and gas is increasing faster than the
supply, good seismic information will lead to more affordable energy.

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1.1.3 BASIC THEORY
Let us briefly study about what is actually happening in the subsurface. The physical properties
of earth materials are not uniform because subsurface variations occur in lithology, porosity,
mineralogy, density, permeability, and pore fluids. To understand wave propagation in these
materials, simplified mathematical models are usually constructed. On such model assumes only
the propagation of compressional or P-wave types and is usually called the acoustic media
model. However, when a P-wave strikes an interface between two solids, at an angle that is
below the critical angle, it generates reflected and transmitted P- and S-waves. Similarly, an
incident SV-wave also generates reflected and transmitted waves of both types. Such a process is
called mode conversion. Models that consider such effects are called elastic-media models and
fully consider the propagation of S-waves and mode-converted waves, in addition to P-waves.
Mathematically, the propagation of such waves can be described by solving the wave equation.
For one-dimensional acoustic-wave propagation (Chopra, S. and Castagna, John P. (2014))
2
2
2
2
2
x
u
V
t
u





(1)
For three-dimensional wave propagation,
2
2
2
2 1
t
u
V
u



(2)
In equations 1 and 2,
2
2
2
2
2
2
2
zyx 







 (3)
and is also called the Laplacian, u is the seismic wave field, V is the wave velocity in the
medium, and t is time. When a plane wave strikes an interface at normal incidence, a part of the
wave is reflected and the rest is transmitted. The ratio of the reflected wave’s amplitude to the
incident wave’s amplitude is called the reflection coefficient, and is determined by the
impedance contrast between the two layers, impedance being the product of velocity and
density of the medium. The amplitude of the reflected wave is given by multiplying the
amplitude of the incident wave by the reflection coefficient. Thus, for a plane wave reflected at

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normal incidence, the reflection coefficient R is given as (Chopra, S. and Castagna, John P.
(2014))
1122
1122


VV
VV
R


 (4)
where V and ρ are the velocity and density, respectively, for the two media (with appropriate
indices) on either side of the interface (figure1.6), and with medium 2 being below and medium
1 above the interface. Because the product of the velocity and the density is the impedance (I) of
the medium, we can write
12
12
II
II
R


 (5)
The greater the difference is between the impedances of the media on either side of the
interface, the greater the percentage of energy is that will be reflected. The numerator in
equation 5 determines the sign (sometimes referred to as the polarity) of the reflection. If the
impedance of the lower layer is higher than the impedance of the upper layer, the reflection
coefficient for the interface is positive, and vice versa. Thus, the reflection coefficient is a
numerical measure of the amplitude and polarity of a wave reflected from an interface, with
respect to those values for the incident wave. Similarly, the amplitude of the transmitted wave is
given by multiplying the amplitude of the incident wave by the transmission coefficient.
Because the sum of the amplitudes of the reflected and transmitted waves is equal to the
amplitude of the incident wave (by the law of conservation of energy at an interface, and
because there are no sources at an interface), the transmission coefficient can be calculated by
subtracting the reflection coefficient R from 1 (Chopra, S. and Castagna, John P. (2014))
12
12
1
II
I
RT

 (6)
when a plane wave strikes a rock interface at an oblique angle of incidence, as we commonly
observe in reflection seismic recordings, a more complicated situation arises. The discontinuity
in the elastic parameters that the obliquely incident P-waves encounter at the interface results in
compressive and shear stresses. This leads to partitioning of the incident energy at the interface,
so that, in addition to the reflection and refraction of the incident P-wave, there is P- to S-mode

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energy conversion. Thus, below the critical angle, an incoming P-wave gives rise to a reflected
P-wave, a transmitted P-wave, a reflected S-wave, and a transmitted S-wave (Figure 1.6). In
such a case, equation 4 is no longer applicable in a practical sense for angles of incidence
greater than 10°or 15°, and these angles may be smaller for large reflection coefficients. The
angular relationships among the different wave components follow Snell’s law, which is given
as (Chopra, S. and Castagna, John P. (2014))
,
sinsinsinsin
2121 s
t
s
r
p
t
p
r
VVVV

 (7)
Figure 1.6 Incident, reflected and refracted waves at the interface of two media (viz. 1 and 2). (Source: AVO5
)
where VP and VS are the P-wave velocity and S-wave velocity, respectively, for the two media
(as indicated by their indices) on either side of the interface. Angle Өi is the angle that the
incident ray makes with the normal and is called the angle of incidence. Similarly, Өr and Өt are
the angle of reflection and the angle of transmission, respectively, for the P-waves, and φr and φt
are the angle of reflection and the angle of transmission, respectively, for the S-waves. It is
important to remember that the transverse waves generated by the incident P-waves at plane

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interfaces are of the SV type; that is, the vibrations are parallel to the plane of incidence. The
partitioning of incident-wave energy into the different components depends largely on the angle
of incidence as well as on the physical properties of the two media. The physical properties we
refer to here are the P-wave velocity, the S-wave velocity, and the densities of those two media.
A fundamental principle of direct hydrocarbon detection using AVO analysis is the idea that
anomalous contrasts in these parameters — especially in the values for VP/VS or Poisson’s ratio
on either side of an interface — result in anomalous partitioning of energy as a function of angle
of incidence.
Knowing the basics we have seen there are many seismic attributes including p-wave velocity, s-
wave velocity, poisson’s ratio and combination of these. But while doing AVO attribute analysis
we will focus on just two. These are intercept and gradient, and together are called as AVO
attributes.
1.2 AVO (AMPLITUDE VARIATION WITH OFFSET)
1.2.1 HOW AVO CAME IN TO PICTURE (DISCOVERY OF AVO)
Earlier bright-spot analysis and direct hydrocarbon detection were developed in the 1970s, and
during that time they met with considerable success (Chopra, S. and Castagna, John P. (2014).
High-amplitude seismic events were being drilled, and the success rate for exploration wells was
excellent in the Cretaceous sands of the Sacramento Valley in California. However, not all bright
spots were associated with hydrocarbons. During the bright-spot era, the challenge for
geophysicists was to be able to distinguish, on conventional, stacked seismic sections, true gas-
sand signatures from those of non gaseous or abnormally high- or low velocity layers. In the fall
of 1974, Chevron drilled a well on a very high-amplitude event in the Fallon Basin of Nevada. It
turned out to be a high-velocity basalt layer rather than hydrocarbons. Thus, Ostrander suggested
that under suitable geologic conditions, gas sands display a distinct increase in amplitude with an
increase in offset, whereas their amplitude under other conditions decreases or remains flat with
increasing offset. Such an examination of reflection amplitudes from varying source receiver
offsets has been termed ―AVO analysis.‖

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1.2.2 INTRODUCTION TO AVO
Amplitude variation with offset (AVO) is the offset dependent variation of P wave reflection
coefficients to estimate anomalous contrasts in shear wave velocities and densities across an
interface (Chopra, S. and Castagna, John P. (2014)). Although the conventional p-wave
reflection coefficient at normal incidence is, in itself, a hydrocarbon indicator, AVO goes beyond
the P-wave normal incidence by producing a second attribute that is related to the contrast in
Poisson’s ratio.
Most of the time, the gas sands that produce these amplitude anomalies have lower impedance
than the encasing shales and have reflections that increase in magnitude with offset. The theory
behind AVO exploration for gas in clastic rocks is straightforward. Gas within the pore space of
a clastic rock lowers the compressional wave velocity of the rock substantially, but leaves the
shear wave velocity relatively unaffected. The change in the ratio of P-wave velocity to S-wave
velocity causes the partitioning of an incident wave to differ for the case of a gas-sand /shale or
gas-sand/wet sand reflector from that of most other reflectors. For some reservoirs the reflections
associated with gas bearing rocks increase in amplitude with offset relative to other reflections
(Figure-1.7). Such an increase with offset is uncommon in seismic data; most reflections
decrease in amplitude with offset. In this sense, AVO analysis is a search for such an anomalous
seismic response. The input to the AVO analysis is a common midpoint gather which is a set of
traces sampling the same subsurface point at varying offsets. The use of AVO as a direct
hydrocarbon indicator in clastic rocks is based on differences in the response of the P-wave
Figure 1.7 Model CMP gathers contrasting the expected AVO response of a typical wet sand
and gas sand. The increasing amplitudes at far offsets for the gas are anomalous with respect
to most other reflections. After (Mujiburrahmam, 2018).

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velocity (vp) and S-wave velocity (vs) of a reservoir rock to the introduction of gas in the pore
spaces. P-waves are sensitive to the changes in the pore fluids. The introduction of only a small
amount of air or gas into the pore spaces of the rock can reduce the P-wave velocity of the rock
drastically. In contrast, S-waves do not see the pore spaces of the rock and have a velocity that
depends mainly on the rock framework. Therefore, the decrease in the vp/vs ratio of a reservoir
rock upon the introduction of gas in the pore spaces changes the relative amplitude of reflection
from the top and base of the reservoir as a function of angle at which a wave strikes the
boundary. The study of relative amplitudes of the traces within a CMP gather is known as
amplitude variation with offset analysis. Amplitude variation with angle (AVA) denotes the
examination of traces sampling the same midpoint at increasing angles of incidence (figure 1.8).
Figure 1.8 Recording the amplitudes corresponding to a CMP for different angle of incidence. After (CGG)
Seismic reflections from gas sands exhibit a wide range of amplitude-versus-offset (AVO)
characteristics. The two factors that most strongly determine the AVO behavior of a gas-
sand reflection are the normal incidence reflection coefficient Ro and the contrast in
Poisson's ratio at the reflector. Based on their AVO characteristics, gas-sand reflectors can
be grouped into three classes defined in terms of Ro at the top of the gas sand. Class I gas
sands have higher impedance than the encasing shale with relativity large positive values
for Ro. Class 2 gas sands have nearly the same impedance as the encasing shale and are

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characterized by values of Ro near Zero. Class 3 sands have lower impedance than the encasing
shale with negative large magnitude values for Ro. Each of these sand classes has a distinct AVO
characteristic.
Shuey’s approximation to Zoeppritz Equation is given by (Yilmaz, Oz., 2008)
)sin(tan
2
1
sin]
)1(
[)( 222
2000 





 




 pppp RARR (8)
As shown in figure 1.8, the traces in a seismic gather reflect from the subsurface at increasing
angles of incidence . The first order approximation to the reflection coefficients equation as a
function of angle is given by (Yilmaz, Oz., 2008)
(9)
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. The AVO classes are represented in the table 1
below.
Table 1: Classification of sands..
AVO Class Characteristics
Class 1:
High impedance sand with decreasing AVO. The layer has higher
impedance than the surrounding shales.
Class 2:
Near-zero impedance contrast between the sand and surrounding
shales.
Class 2p: Near-zero impedance contrast with polarity reversal.
Class 3:
Low impedance sand with decreasing AVO, compared to surrounding
shales.
Class 4: Low impedance sand with increasing AVO.
 2
0 sin)( BRR 

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1.3 SOFTWARE USED
The software used for the AVO analysis is Hampson Russell Solutions, commonly known as
HRS. It was first launched in 1987. Hampson Russell is reservoir characterization software,
having features for attribute extraction and prediction along horizontal wells, as well as geo-
statistical mapping capabilities. Hampson Russell also provides it’s users workflows with data
conditioning, inversion and map prediction features. Key features include data conditioning
processes, residual Normal Moveout (NMO) correction, and FXY deconvolution for noise
attenuation and spectral balancing. Inversion can now output relative impedances for both pre-
and post-stack data and extract attributes along horizontal well paths. This new information helps
Figure 1.9 Reflection Coefficient variation with the angle of incidence. (After Chopra, S. and
Castagna, John P. (2014))

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interpret data from derived attributes resulting in more accurate reservoir model. The MapPredict
application is fully integrated, easy-to-use, map-based geo-statistical software that integrates
well, seismic and attribute data into accurate, detailed maps. MapPredict encompasses the
functionality of Hampson Russell’s former ISMap application and has evolved even further to
include the ability to handle horizontal wells. MapPredict is especially suited to finding
relationships between multiple seismic attribute slices and properties derived from well
information such as hydrocarbon production. GeoSoftware delivers innovative reservoir
characterization and advanced seismic interpretation and analysis software that offers expanded
capabilities for improved productivity (After CGG).
1.4 OBJECTIVE OF THE DISSERTATION
The objective of this dissertation is to delineate the extension of hydrocarbon saturated zones on
the basis of AVO analysis. The area under study is located in Upper Assam Basin in OIL’s
operational area. The area is covered by 3D seismic data and drilled wells established
hydrocarbon in Miocene age formations. Amplitude versus offset (AVO) technique was
therefore used to model subsurface synthetic response from well logs and applied as a tool to
identify and delineate the extension of hydrocarbon reservoir within the area.
1.5 OUTLINE
As of now we have known about the history of seismic refraction and reflection methods, their
role in oil exploration, seismic attributes and the very background of AVO that we require for
this dissertation work. Then proceeding towards the introduction of the software that has been
used for the ―AVO attribute analysis for the identification of gas bearing sands”, we are now
aware of the objective of this dissertation work too. Summing up all of this in Chapter 1, we will
study about the dataset used and the study area in the Chapter 2. Chapter 3 will contain the
methodology adopted. Then the data analysis and results will be covered in Chapter 4. Chapter 5
will contain the discussion and conclusion. Chapter 6 will contain the references that have helped
writing this text.

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Chapter 2
Dataset used and study area
2.1 DATASET USED
We require two types of dataset for performing an AVO attribute analysis. They are:
1. Seismic data
2. Well log data
The available seismic data within the study area is processed in an amplitude preserved manner
which is a pre-requisite for AVO analysis. Whereas, in case of well logs sonic (p-wave and s-
wave) along with density logs are required.
The base map below (see figure 2.1) shows the seismic coverage and drilled well position in the
study area. Details about the seismic dataset used are given in table 2.
Table 2: Basic details of seismic dataset used
Inline Number range 1050 to 1125
Cross-line Number range 2000 to 2400
Inline Interval 50m
Cross-line Interval 25m
Sampling Interval 2ms
Record Length 06 seconds

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Figure 2.1 Base map of the study area.

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2.2 STUDY AREA
The study area includes OIL’s operational area in the Upper Assam Basin (See figure 2.2). The
Upper Assam Basin is a foreland Basin located at the boundary of two convergent plates viz.
Indian and Eurasian. The formation of the basin comprises of alternate sand and shale bed from
Eocene to Recent Age. Hydrocarbon production in these areas primarily comes from Eocene,
Oligocene-Miocene age formations. In the present study, AVO analysis has been carried out in a
Miocene reservoir to identify hydrocarbon proven sands and investigate its possible areal
extension in and around the well location.
Figure 2.2 Tectonic map of North-Eastern India. (After Ishwar, N.B. and Bhardwaj, A,2013)

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Figure 2.3 Geology of Upper Assam. (Source: Dissertation Report)

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2.3 GEOLOGICAL SETUP OF THE STUDY AREA:
The Upper Assam Basin is one of the petroliferous basins of India and encompasses parts of the
Indo-Burma range and shelf areas to the west. The Indo-Burma range is a geologically complex
tectonic belt which extends in north-south direction along the geographical boundary of India
and Burma (presently Myanmar). It is characterized by association of a number of
thrust/overthrust, ophiolitic rocks, high degree of metamorphism, pelagic sediments etc. On the
other hand, the shelf area is comparatively free from the thrust tectonics and is characterized by
occurrences of normal faults down to basement. Sediments ranging in thickness from 3500 m to
more than 7000 m was deposited over granitic basement. The age of sediments ranges from
Upper Cretaceous through Paleogene to Neogene times. One important structural feature is the
area known as ―Belt of Shuppen‖ which is a series of thrusts and overthrusts trending in the
northeast-southwest direction and flanks the eastern part of the shelf area of the basin. The
thickness of sediments increases towards the eastern thrust belts as well as to the northeast.
Presence of commercial hydrocarbon has been established in clastic sediments of both Paleogene
and Neogene age.
The study area is in eastern part of Upper Assam Basin. Major formations (see figure 2.3) of the
basin are viz. Sylhet group( Eocene), Kopili (Late Eocene –Oligocene), Barail (Oligocene-
Miocene), Tipam (Miocene), Girujan (Miocene), Namsang (Pliocene) and Siwalik/Dhekiajuli
(Recent). The formations are primarily of clastic sediments. The thickness of these formations
varies in N-S direction (i.e. across the basin) whereas the thickness variation is less in NE-SW
direction (i.e. basinal strike direction).

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CHAPTER 3
Methodology Adopted
In this study, AVO (amplitude variation with offset) forward modeling and analysis was done in
a well in the gas charged reservoir zone. Synthetic gathers were generated using Aki-Richard’s
equation and subsequently, AVO attributes, intercepts and gradient were calculated based on
Aki-Richard’s two term equation. The available seismic data was processed in AVO friendly
manner where relative amplitudes were preserved. Well to seismic tie was performed to match
the synthetic event with seismic and a good correlation has been observed.
Intercept and gradient analysis is the common and popular AVO analysis method. The method is
to plot the amplitude of the signal for a reflector (i.e., horizon) against the offset of the trace or
the calculated angle that the corresponding sound wave would make when it met the reflector.
This plot yields the "Intercept", where the trend of the amplitude measurements meets the zero-
offset line (so it would be equivalent to a geophone directly next to the source, and a 90° angle to
the reflector). It also yields the "Gradient", which is the slope of the curve made by the plot
points which in our case has been done by the software and we used the direct values.
Intercept and gradient plots on angle gather for a particular horizon or a reservoir top and base
would be responses as shown in figure 3.1. These type of curves plot would infer reservoir sand
properties with respect to overburden and underlying shale layer.

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Figure 3.1 Curves showing reservoir top (red) and base (green).
3.1 INTERCEPT GRADIENT ANALYSIS:
Figure 3.2 Top and base of the reservoir in the gather. After (Rebecca Goffey,2012)
In this case, both the intercept (A) and the gradient (B) are large numbers or ―bright‖. Also, they
have the same sign. This is an example of a Class 3 anomaly. Forming the product of A and B,
we get:
Top
Base

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Top of sand: (-A)*(-B) = +AB
Base of sand: (+A)*(+B) = +AB
This gives a positive ―bright‖ response at both top and base being consecutive cycle (see figure
3.2). This is Class III type AVO response. When this intercept and gradient product plot in the
color scale is plotted, anomaly along the reservoir top and base would show positive response for
class-III sand represented in Figure 2.4. This type of AVO response is very easy to detect in the
section.
Figure 3.3 Intercept and gradient product plot (data example). After (Mujiburrahmam, 2018)

33. 33
3.2 WORK FLOW:
Fig 3.4 Workflow of the methodology adopted
Figure 3.4 explains part of the workflow adopted in this study in order to perform AVO analysis.
In this study, well log data from sonic log and density log has been utilized in order to obtain
values of density and compressional velocity which finally gives the acoustic impedance.
Thereafter, synthetic trace is generated on the basis of convolution between reflectivity series
and wavelet extracted from the seismic gathers. As the well log data is in the depth domain and
Well Log Data
P-sonic
(Vp)
Density
(ρ)
Compute Acoustic
Impedance
(ρ*v)
Generate
reflectivity
series
Seismic gathers
Extract
wavelet
Convolution
(*)
Generate
synthetic trace
Well tie &
event
correlation

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seismic data is in the time domain, we utilize the time depth relationship (TDR) and events
correlation so as to tie the synthetic trace with the seismic gathers.

35. 35
Chapter 4:
Data analysis and Results
4.1 WELL LOG DATA ANALYSIS:
Recorded logs contain caliper, gamma ray, resistivity, neutron porosity, full wave sonic (P, S),
density and other basic logs (figure 4.1). Log data interpretation and drilled well information
confirm two gas bearing zones at depth 2320-2340m and 2370-2390m (Measured depth)
respectively.
Gamma ray log generally gives higher value for the shale lithology (due to the presence of
radioactive grains in shales) and lesser for the sandstone. So, gamma log give us an indication
about the lithology of the subsurface.
In resistivity logs, generally three logs, namely, MSFL (Micro Spherical Focused Logs), LLS
(Laterolog Short) and LLD (Laterolog Deep) are recorded. MSFL and LLS logs are of lesser
value in AVO analysis as their depth of investigation is restricted to the invaded zone and the
transition zone. LLD records resistivity values of un-invaded zone, hence, is required for AVO
analysis.

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Table 2
Log / Derived property used Property
recorded/Used
Observations
Gamma ray log Gamma ray index
(API units)
High values for shale lithology and
low values for sandstone lithology
LLD (Resistivity log) Resistivity(ohm-m)
High values corresponding to
sandstone region and low values
for the shale medium
Neutron log Porosity(fraction)
Under-estimation of the porosity in
the presence of hydrocarbon
saturated gas-sand
Density log Bulk density(g/cm3
)
Low value in the presence of the
hydrocarbon saturated gas sand
reservoir.
Sonic log Transit time (μs/ft)
Decrease in p wave velocity in the
presence of the hydrocarbon
saturated zone ( due to decrease in
the value of bulk modulus as bulk
modulus is the direct measure of
the resistance a material towards
the application of the stress)
Poisson’s Ratio Vp/Vs
Sharp decrease in the Poisson’s
ratio as vp shows a decrease and vs
increases in the presence of
hydrocarbon saturated zone

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On the basis of observations made using the various logs and derived properties, the zone of
investigations to perform AVO analysis has been chosen to be from 2320-2340m and 2370-
2390m (Measured depth).
As we know there is a probability of finding a reservoir corresponding to low values of p-wave
impedance and gamma ray. Fig. 4.2(a) shows a selected region that looks favorable to the
presence of the hydrocarbon bearing zone.
Figure 4.2(b) is display curve corresponding to this selected region, which will display the depth
values corresponding to hydrocarbon saturated zone. The red color region depicts the different
depths corresponding to the selected area of hydrocarbon saturation.
Figure 4.1 Zone of investigation along marked on the recorded logs and other basic derived values.
Zone of
investigation

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(a) (b)
Zone of
interest
Figure 4.2 (a) Cross-plot of the Gamma ray (API) and P-wave impedance. (b) Display curve of depths corresponding to
hydrocarbon saturated zone

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4.2 WELL TO SEISMIC TIE:
Well-seismic tie allows well data (measured in units of depth) to be correlated with seismic data
(measured in units of time). For any geo-scientific study, which uses seismic & well data, both
dataset needs to be converted in one domain. Here, Well-to-seismic tie aims to convert depth unit
of wells into time units. A Well-seismic tie is a four step process, which includes:
 Select the seismic data in periphery of the well location; extract statistical wavelet from
the target zone using appropriate wavelength.
 Synthetic seismic is created by convolving well derived impedances and statistical
wavelet, and synthetic is correlated with observed seismic near well. First, check
shot/VSP correction are required for sonic calibration, however, in case check shot/VSP
is not available, logs can be shifted by matching the major sequence boundaries in logs &
seismic. After that, minor stretch/squeeze operation on logs is performed (well-log
correlation) for optimum correlation between synthetic and observed seismic.
 Once a reasonable T-D curve is established, deterministic wavelet is extracted by
correlating the Synthetic and Observed Seismic near well.
 On correlating the deterministic wavelet derived synthetic and seismic near well, minor
corrections are done for optimum correlation, and in this process T-D curve is further
refined.
Using seismic data we have extracted a statistical wavelet. The figure 4.3 is showing a good
correlation between synthetic and seismic traces.

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Figure 4.3 The well log curves on the first 4 tracks and well tie between the synthetic and seismic wavelet (7th
track) and the seismic gather.
4.3 GRADIENT ANALYSIS
Also we can see that seismic traces are also giving the same result as the well log data. The
trough in seismic wavelet corresponding to the well top shows a negative intercept and negative
gradient while the peak (crest) corresponding to the well base shows a positive intercept and
positive gradient. These are visible as mirror images of each other (fig 4.4(a)).

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Fig. 4.4(a)AVO analysis corresponding to the event at 1903 ms(well top) and 1922ms(well base) and (b) AVO
analysis showing top and base of the gas sand on the basis of intercept and gradient properties with the help
of red and green colored blocks.
If we see the cross plot of AVO attributes i.e. intercept and gradient, an interesting deviation
from the background trend is observed. Looking at all those values having different colors, the
red color block in the third quadrant corresponds to the well top and green color block in the first
quadrant corresponds to the well base. This is very beautifully shown in the figure 4.4(b). After
the AVO analysis we can see if we take the product of these attributes, the product will always
come out to be a positive value which confirms that the sand present in the reservoir is of class
III. If we see the trend in synthetic traces and the seismic traces, we find them talking to each
other (fig. 4.5)
(a) (b)

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Fig. 4.5 Seismic trace is preserving the same character as shown by the synthetic trace.
4.4 CREATION OF VOLUME FROM ANGLE GATHERS
Offset gathers (see figure 4.6(a)) were converted in to angle gathers (see figure 4.6(b)) for AVO
analysis which is completely dependent on the angle of incidence and it was observed that the
amplitude is increasing in negative sense (i.e. the value was increasing in negative sense with
increase in incidence angle) and gradient behavior is also negative which is shown in figure 4.7.

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Figure 4.6(a) Offset gather
Figure 4.6(b) Angle Gather

44. 44
Figure 4.7 Angle gather preserving the same character of amplitude (increasing in a negative sense).
Now working on the AVO attributes and doing their analysis we proceeded towards the study of
same for two events corresponding to the synthetic and seismic traces for 1903 ms and 1922ms.
In Figure 4.8, panel 1 shows seismic events and panel 2 synthetic gather for the same events.
This makes us confident about our observations using well log information. Analysis of the
attributes on their cross-plot (figure 4.9),we see that red and navy blue colored blocks lie in the
third quadrant and the green and sky blue colored blocks lie in the first quadrant, affirming us
that their product will be always be positive (intercept*gradient) which delineates the class III
type of the gas-sand present in the reservoir from the background.

45. 45
Figure 4.8 Attribute analysis for the synthetic as well as seismic data.
Figure 4.9 Cross-plot of the intercept and gradient values for both seismic and synthetic trace.

46. 46
4.5 AVO ANALYSIS AT VOLUME SCALE
After performing AVO analysis for a single CDP gather, the AVO analysis study was extended
to the complete volume by determination of intercept (A) and gradient (B) for the whole volume.
We selected two regions (one red and another blue) depicting our class-III type of sand in the
reservoir (figure 4.10).
Figure 4.10 AVO attribute analysis for the entire volume.
On analyzing the volumetric version, CDP stack section was overlain on the reservoir top (red)
and base (blue) zones derived from the cross-plot, as shown in the figure 4.11, it was observed
that there was good correlation between the two. Further, it was observed that highlighted

47. 47
portion in figure 4.10 corresponding to the positive value of the Intercept* gradient product
shows the extension of the reservoir (from N to S) (figure4.11). For Class-III gas sand in our
study area Intercept* gradient product is helpful to identify the anomaly from background trend.
Figure 4.11 CDP stack overlain on the reservoir top (red) and base (blue).

48. 48
Chapter 5
Discussion and Conclusion
On the basis of AVO analysis performed on the seismic gathers, it has been inferred that there is
a presence of Class III type gas sand in the reservoir. Cross-plotting of rock properties indicate
that reservoir sandstone is of low impedance with high impedance encased lithology (shale).
Drop in Poisson’s ratio has been observed for gas charged reservoir sand allow utilization of
AVO technique to characterize the reservoir. AVO anomaly in the target reservoir within the
study area has been classified as class III, with real seismic PSTM gathers which showed large
amplitude at far offset for the gas charged sand.
The extent of gas charged sand can be determined using the intercept- gradient attribute as
shown in Figure 5.1. The extension of the hydrocarbon saturated zone was prominent in the In-
line direction where as in the cross-line direction its extent was limited spatially. Thus, within the
study area AVO analysis on seismic data can help us delineate hydrocarbon bearing zones from
non-hydrocarbon bearing zones.
Figure 5.1 Intercept Gradient product analysis, showing the areal extension of the hydrocarbon.

49. 49
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