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IBEKWE, ABRAHAM IKECHUKWU
(PG/M.Sc/09/51925)
REVIEW OF SEISMIC REFLECTION
DEPARTMENT OF PHYSICS AND ASTRONOMY
FACULTY OF PHYSICAL SCIENCES
Ugwoke Oluchi C.
Digitally Signed by: Content manager’s Name
DN : CN = Weabmaster’s name
O= University of Nigeria, Nsukka
OU = Innovation Centre
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REVIEW OF SEISMIC REFLECTION OPERATIONS
BY
IBEKWE, ABRAHAM IKECHUKWU
(PG/M.Sc/09/51925)
A PROJECT
SUBMITTED TO THE DEPARTMENT OF PHYSICS AND ASTRONOMY,
FACULTY OF PHYSICAL SCIENCES,
UNIVERSITY OF NIGERIA, NSUKKA,
IN PARTIAL FULFILMENT FOR THE AWARD OF MASTER OF SCIENCES (M.Sc)
IN SOLID EARTH GEOPHYSICS.
SUPERVISORS
PROF. F. N. OKEKE (FAS)
DR. J. U. CHUKUDEBELU
NOVEMBER, 2013
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CERTIFICATION
This is to certify that this project was submitted by Ibekwe, A. Ikechukwu to and approved by the
Department of Physics and Astronomy, Faculty of Physical Sciences, University of Nigeria,
Nsukka, in partial fulfillment for the award of Master of Sciences (M.Sc) in Solid Earth
Geophysics.
__________________________________________
Head of Department
__________________________________________
Supervisor
__________________________________________
Supervisor
_______________
Date
______________
Date
______________
Date
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ACKNOWLEDGEMENT
“Dreams are not the things you see when you sleep. Dreams are those things that keep you from
sleep!”
The completion of this project is a dream come true and I would like to acknowledge all those who
in one way or another contributed to this great success.
Firstly, I would like to acknowledge the Head, staff and students of the Department of Physics and
Astronomy, UNN, for the knowledge and time we shared. Your inputs actually contributed to my
success. Most especially my supervisors, Prof (Mrs.) F. N. Okeke (the most compatible person I
have ever worked with) and Dr. J. U. Chukudebelu (if I were to have a second dad, it would be him)
for their patience and constructive criticism towards me and my work.
I also acknowledge my family for their unalloyed and unreserved support towards my education. I
want to specifically observe my mum, Mrs. N. N. Ibekwe (the best mum in the world) and Engr.
Nelson Uzonwa and his family for investing in me. Your efforts have finally paid off.
To my course mates, Chimaroke, Chisom and Robert. You guys are the best.
I also want to appreciate Mrs. Ijeomakanu. She surprised me sometime.
To all my friends, Cyril, Iyke Allah, Victor (76), Salata and Chinenye. I love you all!
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DEDICATION
This work is dedicated to all those who believe in me, most especially, my mum and sisters.
And also to my future kids!
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ABSTRACT
In the summer of 1921, a small team of physicists and geologists (William P. Haseman, J. Clarence
Karcher, Irving Perrine, and Daniel W. Ohern) performed a historical experiment near the Vines
Branch area in south-central Oklahoma using a dynamite charge as a seismic source and a special
instrument called a seismograph. The team recorded seismic waves that had traveled through the
subsurface of the earth. Analysis of the recorded data revealed seismic reflections from a boundary
between two underground rock layers.
Further analysis of the data produced an image of the subsurface, called a seismic reflection profile
that agreed with a known geologic feature. That result is widely regarded as the first proof that an
accurate image of the earth's subsurface could be made using reflected seismic waves.
Since that day till date, more technological and developmental advancements have been recorded in
the geophysical sphere the world over. Today, reflection seismology is a thriving business with the
result of the 1921 experiment being nothing compared to present day survey results.
This review attempts to take a critical look at some seismic reflection operations that have been
performed around the world, analyze the approach employed in the acquisition, processing and
interpretation of seismic data and to suggest or recommend suitable and more appropriate approaches
to specific survey objectives.
This review work begins by introducing the basic concepts and principles upon which seismic
reflection work operates, and then will take a look at seismic data acquisition methods and the basic
processes applied in transforming a noise-filled data to an output with clearer features (data
processing).
Ten (10) seismic reflection cases will be reviewed. The review will cover their
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acquisition, processing and interpretation procedures and their results discussed. This project will
conclude by proffering recommendations as to what method or sequence a seismic crew could adopt
to address a desired result.
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TABLE OF CONTENT
TITLE PAGE
i
CERTIFICATION
ii
ACKNOWLEDGEMENT
iii
DEDICATION
iv
ABSTRACT
v
TABLE OF CONTENT
vii
LIST OF FIGURES
xi
LIST OF TABLES
xii
CHAPTER ONE: GENERAL INTRODUCTION
1.1
Introduction
1
1.2
Elasticity Theory
2
1.3
Stress
2
1.4
Strain
4
1.5
Seismic Wave Equation
6
1.6
Seismic Waves
9
1.6.1
Body Waves
10
1.6.2
Surface Waves
11
1.7
Seismic Wave Propagation
11
1.7.1
Huygen’s Principle
12
1.7.2
Fermat’s Principle
12
1.8
Wave Attenuation
12
1.9
Seismic Velocity
13
1.10
Factors that Affect Seismic Velocity
14
1.11
Purpose of Study
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CHAPTER TWO: SEISMIC DATA ACQUISITION AND PROCESSING
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2.1
Introduction
15
2.2
Seismic Data Acquisition
15
2.3
Seismic Data Processing
16
2.4
Pre-Processing
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2.4.1.
Demultiplexing
17
2.4.2
Trace Editing and Muting
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2.4.3
Gain Recovery
2.5.
Main Processing
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2.5.1.
Deconvolution
22
2.5.2.
CMP Sorting
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2.5.3.
Velocity Analysis
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2.5.4.
NMO Correction
29
2.5.5.
Dip move-out (DMO) Correction
30
2.5.6.
Statics Corrections
32
2.5.7
Residual Statics Correction (RSC)
34
2.5.8.
Stacking
35
2.5.9.
Digital Filtering
35
2.5.10.
Migration
36
2.6.
Mathematical Operations in Seismic Data Processing
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2.6.1.
The Fourier Series/Transform
40
2.6.2.
Convolution
44
2.6.3.
Correlation
46
2.6.4.
The z-transform
47
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CHAPTER THREE: LITERATURE REVIEW
3.1.
Introduction
3.2.
Case 1: Seismic Reflection Studies in the Skellefte
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10
Ore District, Northern Sweden
3.3.
3.4.
3.5.
3.6.
3.7.
3.8.
3.9.
3.10.
3.11
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Case 2: Seismic Reflection Tomography: A Case
Study of a Shallow Lake in Lake Balaton
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Case 3: COCORP’S Deep Seismic Reflection Profiling in
the Williston Basin (Comparison of Explosive and
Vibroseis Source Energy Penetration)
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Case 4: Reflection Seismic and Ground-Penetrating
Radar Study of -Previously Mined (Lead/Zinc) Ground,
Joplin, Missouri
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Case 5: Seismic Processing and Velocity Assessments
in the Oil and Gas Resource Potential of the 1002 AREA,
Arctic National Wild Life Refuge (ANWR), Alaska
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Case 6: Seismic Reflection Investigation of C-1 to F-1
Sector of the Superconducting Super Collider (SSC)
Ring West of Waxahachie, Texas
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Case 7: Analysis of Existing Seismic Reflection Data in
South Florida for Aquifer Storage and Recovery (ASR)
Regional Study
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Case 8: 4D Acquisition Using Two Boats in the Enfield
Oil Field Offshore, Western Australia
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Case 9: 3D Interpretation of The Espoir Field Area,
Offshore Ivory Coast
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Case 10: Structural and Stratigraphic Mapping of
Emi field, offshore Niger Delta, Nigeria.
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CHAPTER FOUR: RESULT DISCUSSION
4.1.
Results of Case 1
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4.2.
Results for Case 2
84
4.3.
Results of Case 3
86
4.4.
Results of Case 4
89
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4.5.
Results of Case 5
90
4.6.
Results for Case 6
90
4.7.
Results for Case 7
91
4.8.
Results for Case 8
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4.9.
Results for Case 9
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4.10.
Results for Case 10
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CHAPTER FIVE: DISCUSSIONS AND CONCLUSIONS
5.1.
Discussions and Conclusions
97
5.3.
Recommendation
99
REFERENCES
101
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LIST OF FIGURES
Fig. 1.1: Components of stress acting on faces perpendicular to the x-axis.
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Fig 2.1: Conventional seismic data processing flowchart
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Fig 2.2 A stacking chart showing traces and common gathers
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Fig 2.3: A CMP gather (a) containing a single event with a move-out velocity of
2264m/s; (b) shows NMO correction of (a) using appropriate velocity
(2264m/s); (c) shows an over-corrected event because a very low velocity
was used (2000m/s) and (d) shows under-correction resulting from too
high a velocity (2500m/s).
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Fig 2.4: Illustration of elevation statics
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Fig 2.5: Design and application of zero-phase filter in the frequency domain
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Fig 2.6: Design and application of zero-phase filter in the time domain
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Fig 3.1:Bedrock geological map of the Kristineberg area, with the locations
of the seismic profiles, the CDP lines and the temporary seismic stations
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Fig 3.2: Location map of the broader study area of Lake Balaton
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Fig 3.3. A map showing the survey area of the Williston basin and Trans-Hudson
Orogen of North Dakota
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Fig 3.4: Map of the survey area in Joplin, Missouri
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Fig 3.5: Location of seismic line segment C1 – F1
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Fig 3.6: Location map of the Enfield 4D monitor survey
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Fig 3.7: Location map of 1981-82 Ivory Coast 3-D seismic survey showing
structure of Albian unconformity
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Fig 3.8: Map of Niger Delta showing the location of the study area (Emi field)
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Fig 4.1: A brute stack showing (a) before and (b) after applying refraction and
residual statics, and a time and space-variant band-pass filter
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Fig 4.2: Line drawing of the stacked section along Profile 5 (a) before and
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(b) after migration and depth conversions
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Fig 4.3: Raw zero offset section (512 traces with 25cm trace spacing)
containing three reflectors acquired in Balaton Lake
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Fig 4.4: The same data in figure 4.3 after (a) band-pass filtering,
(b) gain recovery function, (c) deconvolution (optimum Wiener
deconvolution) and (d) phase-shift migration of zero-offset data
87
Fig 4.5: Reconstructed reflectors after inverting reflection times and
appropriate regularization factors have been applied. Dashed lines
represent initial reflectors and thick continuous lines represent the
final inverted geometry
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Fig 4.6: A stacked section of data obtained from lines (a) MT10(vibroseis)
and (b) MT12 (explosive) imaging the same unmigrated sub-surface
features through 30s TWT. (c) is still MT12 imaging the upper mantle
features
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Fig 4.7: In-line 525, crossing A-1X well location, Espoir field, and showing
clear definition of rotated fault blocks beneath Albian unconformity
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Fig 4.8: Comparison of 2-D migrated and 3-D migrated sections across structure
drilled by well A-4X showing improved definition of erosional high on
Albian unconformity and fluid contact (flat spot)
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LIST OF TABLE
Table 2.1: Basic Fourier transform theorems
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Table 3.1 Data acquisition parameters for profiles 1 and 5
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Table 3.2: A summary of the processing operations on profiles 1 and 5
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Table 3.3: Acquisition parameters for the Lake Balaton survey
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Table 3.4: A table showing recording parameters for both dynamite and vibroseis
Data of the 1002 area
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Table 3.5: Acquisition parameters for the ASR studies seismic survey
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Table 3.5. A summary of the processing steps applied to the ASR studies data
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CHAPTER ONE
GENERAL INTRODUCTION
1.1.
Introduction
Seismic reflection method is the most commonly used geophysical technique which applies the
principles of seismology for stratigraphic and structural mapping beneath the ground surface to depth
of several hundred meters.
Seismology is a wide scope in geophysical science that seeks to analyze the nature of the sub-surface.
It studies movements in the sub-surface and its resultant effect on the earth’s surface. Seismological
studies are based on the travel-time and amplitudes of waves through a medium. The time it takes a
wave to travel through a medium suggests the nature and characteristics of that medium.
The origin of seismology is traced back to 132AD in the ancient Chinese kingdom when a Chinese
scientist, Chang Heng invented the first functional seismoscope (Lowrie, 2007). Before this time,
disturbances within the earth such as earthquakes and volcanoes were thought to be the wrath of
angry gods and such theories were generally accepted.
The development of seismology was somewhat slow from 1638 when Galileo described the response
of a beam to loading, to Hooke’s establishment of the law of spring in 1660. It took another 150 years
for Navier to put down the elasticity equations. Time also slipped before Cauchy and Poisson
established the modern elastic theory which formed the basis of the study of the nature of the earth.
Not until 1892 did seismic knowledge receive a rapid boost when John Milne invented a sensitive and
reliable seismograph (Lowrie, 2007).
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Though not comparable to modern equipment, it allowed quantitative and accurate description of
earthquakes which aided the study of the earth’s seismicity and internal structure.
1.2. Elasticity theory
This theory is a very expedient prerequisite for the study of the physical principles upon which
seismic wave propagation characteristics is based. Such characteristics range from the generation of
such seismic waves, their transmission, absorption and attenuation as they travel through the earth.
Others include their reflection, refraction and diffraction characteristics at boundary surfaces. This
theory was postulated by Hooke in which he stated that ‘within the elastic limit of an elastic material,
the stress in that body is proportional to the strain’. This is Hooke’s law and forms the basis of elastic
theory which the earth materials are thought to obey. Hooke’s law is given by;
Two basic concepts are used to describe wave propagation through the earth. They are stress and
strain. Hooke’s law applies for small strains well below the elastic limit. When several stresses are
involved, each produces strain independent of the others. Hence, total strain is the sum of strains
produced by individual stresses i.e. each stress is a linear function of all stresses and vice versa.
1.3. Stress
Stress, p, is defined as force per unit area acting on a surface and is measured in
. If the force F
exceeds a value called the proportionality limit, Hooke’s law no longer holds and the body suffers
permanent deformation and does not return to its original shape and size when the stress is removed.
If force, F is normal to the surface, we have normal stress. If F is tangential to the surface, we have
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shearing stress. If F is neither parallel nor perpendicular to element of surface, it can be resolved into
normal or shearing stresses.
Fig. 1.1 shows a small element of volume inside a stressed body in which stresses acting on the two
faces normal to the x-axis are shown.
, denotes a stress parallel to the y-axis acting on a face
perpendicular to the x-axis.
z
x
Fig.1.1. Components of stress acting on faces perpendicular to the x-axis.
If the stresses are balanced, the medium is said to be in equilibrium, i.e.
acting on a
plane a-b-c-d must be equal and opposite to the corresponding stresses shown on the opposite face
e-f-g-h. For a body in equilibrium, moment = 0.
The force, F is defined by its components in
directions by
of the surface element is characterized by its area A, with
to the x-axis. The component
by
, and also shear stresses
being the small surface with area normal
acting normal to the surface
and
respectively. The size
produces a normal stress, denoted
in the y and z-axis. That is;
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Same applies also for components of
resulting in the (3×3) matrix
The stress matrix shows nine stress components which completely define the state of stress of a body.
If the forces are balanced, it will contain only six independent elements.
1.4. Strain
A change in shape and dimension of a body as a result of the effect of stress is termed strain. Strains
are of different fundamental types, normal strain and shearing strain. In addition to these strains, the
body is subjected to simple rotation about its three axes.
Longitudinal strain is defined as the fractional change in length of an element along a specific
direction. The component of strain parallel to the x-axis resulting from a small displacement parallel
to the x-axis is denoted by
The longitudinal strains
where
and is given by Dobrin and Savit (1988) as;
are of opposite signs but proportional to
is the constant of proportionality called Poisson’s ratio and is given by;
. i.e
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Dilatation,
which is defined as the fractional change in volume per unit volume of an element in the
limit when its surface area decreases to zero and is given (Lowrie, 2007) by;
Shear strain is a products of the shear components of stress (
) due to changes in the
angular relationship between parts of a body. When for instance, a square is sheared parallel to the xaxis, the side parallel to the y-axis rotates through an angle
axis, the side parallel to the x-axis rotates through an angle
. When it is sheared parallel to the y.
The shear strain in the x-y plane is half the total angular distortion (Lowrie, 2007). i.e.
It could be illustrated in a symmetric 3×3 strain matrix as
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The deformation of a body gives rise to a relationship between stress and strain. The ratio of such
relationship defines the elastic modulus of that body. The unit for elastic moduli is
(Newton
per meter squared). Elastic moduli is defined for three types of deformation;
i.
Young’s modulus, E: This results from deformation due to extension. i.e.
,
where E is Young’s modulus and is the constant of proportionality.
ii.
Shear or rigidity modulus, : This results from shear deformation. i.e.
,
where constant of proportionality is shear modulus .
In terms of Young’s modulus and Poisson’s ratio,
iii.
is given by
Bulk Modulus, K : This is the ratio of hydrostatic pressure to the dilatation. That is
where P is the hydrostatic pressure, K is Bulk modulus and
In terms of Young’s modulus and Poisson’s ratio,
is the dilatation.
is given by Telford (1990) as,
(1.8)
1.5. Seismic wave equation
To derive the wave equation for both P and S waves, we consider the stress-strain diagram (fig. 1.1),
from which we obtain the stresses on the front face e-f-g-h, as,
(1.9)
Because these stresses are opposite to those acting on the rear face a – b – c - d, some stresses balance
and cancel out. Unbalanced stresses are given as;
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Hence, the net forces acting per unit volume in the x, y, z directions have values;
Other faces hold similar expressions. Therefore, we derive total force per unit volume in the xdirection as
We apply Newton’s second law to the total force per unit volume in the x-direction to set;
(1.10)
where U is displacement in x-direction and
is the body density.
Equation 1.10 relates the displacement to the stresses.
But by Hooke’s law,
.
Likewise,
We substitute these expressions in equation (1.11) also noting that
,
where
(1.12)
is called dilatation and is defined as the fractional change in volume per unit volume of an
element in the limit when its surface area decreases to zero.
We therefore obtain the expression;
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Similarly, the corresponding expressions for
and
are
By multiplying (1.13 a) by i, (1.13 b) by j and (1.13 c) by k, we obtain
By defining displacement as
becomes
Equation (1.15) is the equation of wave propagation in a homogeneous isotropic elastic solid. It is an
approximate expression which neglects body forces such as gravity and velocity gradient terms and
assumes a linear isotropic earth model.
If we take the divergence of both sides of (1.15), we obtain
But,
Equation (1.16) then becomes,
=
Equation (1.17) is the P-wave equation in which dilatation propagates with a velocity,
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.
For S-wave equation, we take the curl of both sides of (1.15)
But the rotational vector,
=
is the curl of displacement vector. i.e.
Substitute (1.19) into (1.18) noting that the curl of a gradient is identically zero, i.e.
to
obtain;
Or
Equation (1.21) is the S-wave equation from which S-wave velocity is given by
.
1.6. Seismic waves
Seismic waves are disturbances that propagate through a medium. When these waves penetrate a
medium, the particles in that medium are displaced and deformed elastically to permit the passage of
the waves. There are two types of seismic waves and they are surface waves and body waves.
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1.6.1. Body waves
These are waves or energy that propagates through the body of the medium. They gradually transform
from spherical waves to plane waves as the distance from its source increases due to the reduction in
curvature of its wave front. Body waves are of two types, longitudinal and transverse waves.
Longitudinal or compressional waves are waves that pass through a medium as a series of rarefactions (dilatations) and compressions.
Transverse or shear waves are concerned with the vibrations along the y and z directions which are
parallel to the wavefront and transverse to the direction of propagation in the x-direction.
The general shear wave motion within the plane of the wavefront can be resolved into two orthogonal
components, the vertical and horizontal components. For the vertical component in the z-direction,
the equation;
describes the shear wave with particles displaced in the z-direction. This wave is considered to be
polarized in the vertical plane. The horizontal component in the y-axis is analogous to the above
equation with particles displaced in the y-direction. The resultant wave is horizontally polarized and
can be called SH-wave. Shear wave in the x-direction is given by the equation (Lowrie, 2007),
where ψ is the rotational vector and
.
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Longitudinal waves are the fastest of all seismic waves and so arrive first at a detector (seismometer)
when an earthquake occurs. The first arrivals are called primary waves (P-waves). P-waves travel
through solid, liquid and gas because they are compressible. Transverse waves on the other hand are
much slower and so constitute later arrivals at recording stations. These waves are called secondary
waves (S-waves). They penetrate solids but can’t transmit through liquids and gases.
1.6.2. Surface waves
These are disturbances propagating close to a free surface of a medium. Surface waves are classified
into several types, the most common types are Love waves and Rayleigh waves.
Love wave are waves that can propagate only in a velocity layered medium. The particle motion is
horizontal and perpendicular to the direction of propagation, with its amplitude decreasing with depth.
For a two-layer medium with S-wave velocities
closer to the slower velocity
and
, with Love waves of short wavelengths
and long wavelengths travelling at speed closer to the faster velocity,
. Love waves are dispersive (Lowrie, 2007).
At the surface of homogenous and isotropic half space, the particles in Rayleigh waves are polarized
to vibrate in the vertical plane containing the direction of wave motion. The resulting particle motion
is a combination of the P and SV-vibrations. Rayleigh waves are non-dispersive. Raleigh waves
(often called ground roll) are a main source of signal generated noise on seismic reflection records.
1.7. Seismic wave propagation
Propagation of waves through a medium is as a result of the periodic elastic displacements of the
particles in that medium and its progress is determined by the advancement of the wavefront. Two
principles can be used to handle this. These principles are Huygen’s principle and Fermat’s principle.
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1.7.1. Huygen’s Principle:
This principle as proposed by a Dutch mathematician/physicist, Christiaan Huygens in 1678 describes
the behavior of wavefronts. It is based on simple geometrical construction. It states that “all points on
a wavefront can be regarded as source points for the production of new spherical waves. The new
wavefront is the tangential surface or envelope of the secondary wavelets”. This method permits the
calculation of the future position of a wavefront if the present position is known. This principle is
applied in reflection and refraction, mainly at their boundaries.
1.7.2. Fermat’s Principle:
Formulated by a French mathematician, Pierre de Fermat, this principle states that of the many
possible paths between two points, the seismic ray will follow the path that gives the shortest traveltime between the points. This principle describes the geometry of the ray paths at an interface.
1.8. Wave attenuation
This is the decrease of amplitude of a wave with increasing distance from the source. This reduction
is due to either the geometry of the propagation of the wave or the anelastic property of the medium
through which the wave travel. Amplitude attenuation is more rapid in body waves than in surface
waves. They have a corresponding attenuation of
respectively.
Absorption of energy due to imperfect elastic property of the medium causes attenuation. This is as a
result of energy loss amongst rock particles and can also be called anelastic damping. Damping of
waves is described by a parameter called quality factor which is defined as the fractional loss of
energy per cycle. Damped amplitude of a seismic wave at distance r from its source is given (Lowrie,
2007) by;
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where D is the distance within which the amplitude falls to
of its original value.
Its follows from equation (1.24) that absorption co-efficient is inversely proportional to the
wavelength
and is dependent on the frequency of the signal.
1.9. Seismic velocity
Velocity plays an important role in data interpretation. The knowledge of seismic velocity values is
very necessary in the determination of depth and dip of reflectors and refractors and in the general
assessment of the nature of the rocks and interstitial fluids in the sub-surface. Velocity is obtained
from measurements taken in the field, well logging or from samples in the laboratory.
There are different types of velocity. These include average velocity, root mean square velocity,
interval velocity, phase velocity, stacking velocity, instantaneous velocity and apparent velocity
Average velocity is defined as the distance travelled divided by the time taken to traverse path. Root
mean square velocity refers to the velocity of a specific ray path as it travels through layers. Interval
velocity on the other hand is the average velocity over some interval of the travel path. Phase velocity
refers to the velocity of perpendicular distances between phases of a wavefront. Stacking velocity is
the velocity value determined by velocity analysis usually made by finding the best fitting hyperbola
to data that are not perfectly hyperbolic. It is used to correct the quasi-hyperbolic primary reflection
event to time alignment with zero offset. Instantaneous velocity is the velocity with which a
wavefront passes through a point measured in the direction of travel. Apparent velocity refers to the
apparent speed of a given phase in a particular direction usually, spread direction on the surface.
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1.10. Factors that affect seismic velocity
Due to the non-homogenous nature of the earth, seismic velocity varies from location to location and
an understanding of these factors helps us foresee the kinds of velocity variations to expect in such
areas. Such factors include effect of lithology (that is rock type), effect of density (where velocity is
higher in more dense rocks), porosity of rocks, depth of burial (in which case the greater the depth of
burial, the more the pressure and consequently, higher velocity). The age of the rock type is another
factor. Others are effect of interstitial fluid where more pore fluid depicts lower velocity, temperature,
frequency etc.
1.11. Purpose of study
The purpose of this study is to intensively review the different approaches applied to seismic
reflection survey and the results obtained during several operations as carried out in different parts of
the world. The study and comparison of these seismic reflection operations and the results obtained is
aimed at suggesting a more appropriate procedure in seismic data collection, processing and
interpretation, for yielding more robust results.
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CHAPTER TWO
SEISMIC DATA ACQUISITION AND PROCESSING
2.1. Introduction
Over the decade, seismology has become the key tool to exploration and developmental successes.
With advancements in computer technology, data processing has increasingly acquired a competitive
edge and resulted in the production of clearer and more detailed seismic sections. There is no specific
or generally accepted mode of processing but the choice of a processing method depends on the
information hoped to be obtained or simply the choice of the analyst. The choice of equipment also
determines processing sequence. We should also bear in mind that there are also a variety of
objectives for which a data can be used. Data processing to a certain degree can be adjusted to meet
specific requirement. Seismic data processing is characterized by a sequence of steps where for each
of these steps, there exists a multitude of different approaches.
2.2. Seismic data acquisition
Seismic data acquisition requires a controlled source of energy or signal, usually an explosion or
vibration sent into the ground at a known time and location. By taking note of the time it takes the
signal to be reflected back from the boundaries between rocks to a receiver on the earth’s surface
(usually a geophone for land use and a hydrophone for marine), the depths and features of the subsurface interfaces can be estimated after some processing and analyses.
Seismic data could be acquired on land (land survey) or over an aquatic environment (marine
survey). Both methods employ the same principle of sending signals into the ground and recording
the two-way travel time even though the equipment used differs. Energy sources for land survey
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include explosives, impact sources (weight drop), vibrators and even hammer for shallow smallscale exploration while for marine surveys, airgun, watergun, marine vibrators, aquapulse,
maxipulse, flexotir etc. are employed.
Several field procedures are in use, each being distinguished by the different layout of the
geophones relative to the shot-point. Continuous profiling is usually the routine in which the shotpoint and geophones are moved a predetermined distance after each shot is taken. Continuous
profiling could be conventional i.e. when a reflecting point is sampled once, or redundant, when it is
sampled more than once. Continuous profiling aims at reducing seismic noise. Split spreading is the
most common form of convectional coverage in which the geophones are symmetrically spread on
either side of the shot-point while the common mid-point (CMP) method is an illustration of the
redundant coverage in which shots are fired at different points and each shot has a sub-surface
coverage such that by repeatedly moving the shot-point and geophone arrays, each reflecting point
on the interface is sampled severally
As the geophones/hydrophones detect the arrival of seismic waves, the signals are converted to a
digital form in the present day, unlike in the early systems which recorded analogue signals directly to
magnetic tapes or photographic films. The recorded signals are then displayed by a computer as
seismograms for processing and analysis.
2.3. Seismic data processing
The main goal of seismic processing is to construct high quality reflectivity images of the investigated
sub-surface. Bearing in mind the inhomogenity of the earth, several factors such as environmental and
demographic restrictions can have significant impact on data quality. Other factors such as field
geometry and conditions of recording could also pull strings. Having knowledge of these restricting
factors and the need to have a clearer picture of the sub-surface to compensate for the whole effort
30
and finance put in is necessary. Therefore, obtained data undergo several stages of processing, all
primarily aimed at increasing the signal to noise ratio, thus, producing genuine reflections which
could be interpreted.
Seismic data processing comprises of three main stages; the pre-processing stage, the main processing
stage and the final processing stage as illustrated in fig 2.1.
2.4. Pre-processing
Pre-processing, as can be coined from its name, are processing operations that are carried out on the
field data before the main processing. Such operations include demultiplexing (reformatting), muting,
gain application, filtering and identing. Let us consider them briefly.
2.4.1. Demultiplexing
Field data are recorded in multiplexed form. A multiplexed data is a continuous stream of seismic
samples for the whole seismic record. i.e. in rows of samples at the same time at consecutive
channels. A digital sample is recorded for each seismic trace at a pre-selected sampling rate (2ms or
4ms) in rows of samples at several channels, consecutively. Demultiplexing sorts these data into
columns of samples, i.e. all the time samples in one channel, followed by those in the next channel
and so on.
31
Fig. 2.1. Conventional seismic data processing flowchart (Dobrin and Savit 1988).
32
It can be said that demultiplexing is the separating of all the samples to produce a time sequence for
each geophone. Mathematically, it could be seen as transposing a big matrix such that the column of
the resulting matrix can be read as seismic traces recorded at different offsets with a common shotpoint (Hatton et al, 1986).
Multiplexed data are time sequential while demultiplexed data are trace sequential.
2.4.2. Trace editing and muting
This is a manual cleaning up process of the data. Dead traces, noisy traces (ground rolls, direct
arrivals), mono-frequency signals with transient glitches and less relevant events are deleted and
polarity of reverse traces switched. Frequency distorted zones are also muted to avoid them
suppressing shallow events.
2.4.3. Gain recovery
This function is applied to data to correct for the amplitude effect of spherical wavefront divergence.
This involves the application of a geometric spreading function which depends on travel-time and an
average primary velocity function which is associated with primary reflections in the survey area. It
will also require an exponential gain function to compensate for attenuation losses.
Gain is a time variant scaling in which the scaling function is based on desired criterion. The gain
function for geometric spread compensation is given by Yilmaz, (1987) as;
(2.1)
33
Applying the geometric spread correction amplifies frequencies at the late times of a spectrum but
does not restore the amplitudes of high frequencies as much as it does for low frequencies. To correct
for their amplitudes, we apply deconvolution or time-variant spectral whitening. Gain application to
seismic data also enhances display quality.
There are three common types of gain: the programmed gain control (PGC), the RMS amplitude
automatic gain control and the instantaneous automatic gain control (Hatton et al., 1986).
The programmed gain control (PGC) is the simplest form of gain. The PGC function is applied to all
traces in a gather to preserve the relative amplitude variations in the lateral direction. A gain function
can be defined by interpolating between some scalar values specified at specific time samples.
The RMS amplitude AGC has its gain function based on rms amplitude on an input trace within a
specified time gate. To obtain this, first the amplitude of each sample in a gate is squared, then its
mean value computed and then its square root taken. Usually, the value of the gain function at the
centre of the gate is the ratio of the desired rms amplitude to the central rms value.
Instantaneous AGC is one of the most common types used. To compensate its gain function, its mean
absolute value of trace amplitudes is first computed with a specified time gate, and then the ratio of
the desired rms level to this mean value is taken to be its gain function. The gate is moved one sample
down the trace to compute the gain functions of subsequent time samples. In this case, no
interpolation is required.
This ends the procedures performed in the pre-processing stage. At this stage, a large percent of noise
in the signal must have been eliminated, parameters properly idented and a fairly better quality of
display achieved due to applied gain. The data is now ready for the main processing.
34
2.5. Main processing
There are three basic stages in seismic data processing. These stages are deconvolution, stacking and
migration. Each of these satisfies the goal of data processing which is improvement of the temporal
resolution of seismic data (deconvolution), enhanced signal to noise ratio (stacking) and improvement
of lateral resolution (migration).
Other processes involved in the main processing stage include normal move-out (NMO) correction,
elevation correction, dip move-out (DMO) correction and frequency filtering.
NMO correction compensates for differences in travel times due to differences in source-receiver
offset so that all traces having the same common mid-point (CMP) would appear to have zero offsets
and combined as one. This combination of traces having the same CMP is referred to as stacking.
CMPs are sorted out using a binning grid superimposed over the survey area. The grid is usually
oriented along the actual shot and geophone lines or for marine prospect, along the shooting direction.
The dimensions of the bins are usually based on the shot spacing and geophone spacing. A number of
traces or CMP will fall within each grid cell or bin. These traces are assumed to be imaging the same
general area of the sub-surface.
After applying NMO and static corrections, there is need to perform the dip move-out (DMO)
correction to correct for travel time differences due to dipping reflectors. The traces are then stacked
within each bin to produce a single trace usually at the bin centroid. After stacking, other multi-trace
processes such as migration are possible.
Some factors can just not be left out. In the processing stage, about the most important element is the
velocity information. Without this information, NMO, DMO and static corrections would not be
feasible. CMP stacking, migration and time-depth conversion will also not be possible. This makes it
very expedient.
35
Velocity information also helps in rock identification. Before the computer era, velocity
determination used to be analytic but these days, velocity can be determined directly from reflection
data, well logs, sonic logs etc.
Filtering is another sequence in the processing stage. As the name implies, it simply means
eliminating unwanted data. But in this case, it is usually data outside a particular range. There are
several types of filters and they will be discussed in a few pages.
2.5.1. Deconvolution
Deconvolution aims at generating a smoothly tampered wavelet with a wide spectrum. It improves
temporal resolution by compressing effective source wavelet contained in the seismic trace to a spike
of zero-lag. This is called spiking deconvolution. Since this broadens the spectrum of seismic data,
traces tend to contain much more high frequency energy after deconvolution. To have a better
understanding of the deconvolution process, let us take a brief look at convolution and its model.
The convolution model attempts to explain how seismic trace is formed. It approximates the earth by
a linear system. A linear system is one whose output, say
with its response
, such that
In the convolution method, the seismic trace
where
is given by the convolution of its input
is the source wavelet and
is given by
is the earth’s response.
36
If the noise component
is present, equation (2.3) becomes
Convolution is a time-domain operation which involves the replacement of each element on an input
function with an output function and superimposing the output on the input function. For linear
systems, which the convolution model approximates the earth to, the output is time invariant and
directly proportional to the input. We can convert from output to input if the impulse response of the
system is known.
The forward convolution model of the seismic trace is used to compute the synthetic seismogram
given the source wavelet and earth’s reflectivity. The convolution model of the seismic trace is widely
accepted because it agrees well with the observed seismic traces even though it assumes a lot.
Deconvolution, which is the inverse convolution model, is used to compute the earth’s reflectivity
given the seismic trace,
If the output
. Seismic trace here refers to the output.
is known and we want to regain our initial input signal as it was before it was
modified by the filter, we need to find another filter called the inverse filter with a response, say
through which
would pass to recover
. This process of cancelling the effect of a filter with
another filter is known as deconvolution or inverse filtering.
In the time domain, deconvolution is given by;
,
37
In the frequency domain, it is given by;
Deconvolution is divided into spiking deconvolution and predictive deconvolution. If the source
wavelet is known, then the deconvolution is deterministic and we use spiking deconvolution (inverse
filtering) to find the earth’s response. If source wavelet is unknown, then it is said to be statistical and
in such cases, predictive deconvolution is applied.
Spiking deconvolution is mathematically accomplished using the z-transform. Predictive
deconvolution on the other hand cannot be solved using normal equations except we require the
desired output to be a time-advanced version
of the input
, where
is the prediction lag
which is known (Telford et al., 1990). Its prediction lag gap is equal to the first or second zero
crossing of the autocorrelation function. As its name implies, predictive deconvolution is used to
predict the value of an input at some future time. It also gives room to calculate the error series.
Predictive deconvolution is also a tool for marine multiple suppression.
Deconvolution is a filtering process designed on each trace even before NMO correction and stacking
so that distortions due to time variant operations in the processing sequence are eliminated. A
deconvolution operator aims at summing all the traces so that they represent the same propagating
wavelet.
Deconvolution techniques used in conventional processing are based on the optimum Wiener filtering
theory and all filtering algorithms based on this theory are known as Wiener-Levinson algorithms
(Robinson and Coruh,1988).
38
2.5.2. CMP sorting
This is the transformation of data from shot-receiver to mid-point-offset coordinates. It requires
information about the field geometry. Conventional seismic data processing is done using midpointoffset coordinates and this is achieved by first sorting the data into CMP gathers. Traces with the
same mid-points are grouped together to form a CMP gather. Fig. 2.2 shows a stacking chart which
will make our understanding of these gathers clearer.
Stacking charts are useful when setting up the geometry of a line for pre-processing and makes
identification of missing traces easy to find.
Due to the large number of traces involved in CMP acquisition, a stacking chart is used to keep track
of values. A Stacking chart could either be surface, which has geophone location g, as one coordinate
or source location s, as another or sub-surface, which has the trace plotted at
(Telford
et al., 1990).
Stacking charts are useful in making static and NMO corrections and ensuring proper trace tracking.
Displays of the data in different directions are called gathers or domains. We have the common
source gather, common geophone gather, common mid-point gather and common offset gather. All
these prove useful in the study of noise such as multiples, ghosts, converted waves etc. For horizontal
interfaces, a source placed at the centre of a geophone spread will give a symmetrical curve about the
source point. If however the reflector has a uniform dip, the path of reflected ray on the down-dip side
of the shot-point will be longer than those on the up-dip side and this has corresponding effect on the
travel times. If reflected records are not corrected for layer dip, an error results in plotting the position
of dipping beds.
39
The CMP technique uses redundant recording to improve signal amplitude to noise ratio by a
theoretical factor of
, where N is the total number of elements in the recording system. i.e.
(multiples per trace) assuming identical CMP gather and that the noise of each trace is
uncorrelated. This technique also attenuates coherent noise. This is possible due to the difference in
stacking velocities of coherent noise and reflected signals.
Fig.2.2. A stacking chart showing traces and common gathers (Telford et al., 1990).
2.5.3. Velocity analysis
The objective of velocity analysis is to determine the seismic velocity of layers in the sub-surface.
Seismic velocities are used in many processing and interpretation stages such as mentioned in the
introduction of this chapter. Velocity analysis is performed on selected gathers or group of gathers.
40
The output of one type of velocity analysis is a table of numbers as a function of velocity versus twoway zero offset time (velocity spectrum). These numbers represent some measure of coherency along
the hyperbolic trajectories governed by velocity, offset and travel-time. Velocity time pairs are
selected from velocity spectrum based on maximum coherency peaks. These velocity functions are
then spatially interpolated between the analysis points across the entire profile. When velocity picks
become inaccurate due to complex structures, the data could be stacked with a range of constant
velocities which are then used to pick the velocities.
NMO and stacking velocities can be derived from T-X data. The T-X curve of a single constant
velocity horizontal layer is a perfect hyperbola given by
where
is the two-way travel time at offset
and
is the two-way travel time at zero offset,
is
the layer velocity. (Hatton et al., 1986).
The rms velocity can be defined in terms of the true T-X curve as the square root of the reciprocal of
the coefficient of the
term in the series approximation of the exact
layers. It can be shown that
tangent to the exact
where c is the wave velocity
curve for multiple
is equal to the square root of the reciprocal of the slope of the
curve at x = 0, that is;
41
The stacking velocity,
is found from the T-X data by fitting a best fit hyperbola to the true (non-
hyperbolic) T-X curve, which takes the form;
When fitting a hyperbola to a true T-X curve, the constant term should be equal to
, a condition
that must be satisfied.
NMO velocity:
is found from the T-X data by searching for the velocity that will best NMO-
correct a certain reflection.
can be related to sub-surface properties but
cannot.
However, at small offsets,
are approximately equal to
.
We can therefore relate
to layer properties directly using the Dix formula (Constain,
2004);
where
are bottom and top layers and
are zero offset travel-times at
the top and bottom of Nth layer.
We can also find velocity by producing panels of stacked traces (50 or more) with constant velocity.
This method is called constant velocity stacks (CVS). In this method, a selected CMP gather is
repeatedly NMO-corrected using a range of constant velocity values. The NMO corrected values are
displayed side by side and the velocity that best flattens the event is chosen as the
The CVS method is especially useful in areas with complex structures.
of that event.
42
The velocity spectrum attempts to find the stacking velocity to each reflector. It maps the T-X data of
a single CMP gather onto the velocity-spectrum plane where the vertical axis is the
horizontal axis is
and the
. The velocity spectrum method is more suitable for noise contaminated datasets.
Spread length, stacking fold, S/N ratio, time gate length etc. are some factors that limit the accuracy
and resolution of this method.
2.5.4. NMO correction
NMO is a non-linear stretching of the seismic time axis. To remove travel-time components due to
source-receiver offset, NMO correction is applied. NMO correction prepares the data for stacking and
finds the NMO velocity to the reflector.
After NMO corrections, the events are noticed to be virtually flattened across the offset range. The
resultant effect of this is the stretching of traces in a time-varying manner consequently, shifting the
frequency component towards the left end of its spectrum. If a high velocity is used for NMO
correction, the event would be under-corrected or over-corrected if a lower velocity is used (fig.2.3).
The right velocity will align the event horizontally at
.
Because velocity increases with depth, the NMO correction applied for a later
that applied to an earlier
. Therefore, the two events with a time separation equal to
NMO correction will have a separation of
is NMO correction time,
before
after NMO correction. This is called NMO
stretching and it results in frequency distortion. Stretching is quantified as
where
will be smaller than
is zero offset travel time
43
It is only but wise to remove the stretching before stacking. This is done by muting the considerably
stretched zones from the gather.
In the case of a single horizontal layer with constant velocity, the T-X curve is exactly a hyperbola
given (Hatton et al., 1986) by;
We have NMO correction given (Hatton et al., 1986) by;
NMO correction is derived by subtracting
from
.
2.5.5. Dip move-out (DMO) correction
The CMP method explicitly assumes two rather oversimplifications.
1.
That the seismic velocity is constant
2.
That the sub-surface reflectors are horizontal
The latter assumption is a clear violation when processing data over dipping reflectors. In the
presence of dip, the reflections in a CMP are described by a hyperbolic curve with the apex located at
the offset from the source. The DMO correction attempts to correct the apex to zero offset which
allows for better velocity analysis and stacking.
44
Fig.2.3.A CMP gather (a) containing a single event with a move-out velocity of 2264m/s; (b) shows
NMO correction of (a) using appropriate velocity (2264m/s); (c) shows an over-corrected event
because a very low velocity was used (2000m/s) and (d) shows under-correction resulting from too
high a velocity (2500m/s). (Yilmaz, 1987)
The travel-time for reflection from a flat reflector is given by;
where T is the reflection time,
is zero-offset two-way time to reflector and h is reflector depth
(Robinson and Coruh, 1988).
For dipping reflectors,
where is the angle of dip (Lowrie, 2007).
45
Equation 2.15 can be expressed in a hyperbolic form as;
with axis of symmetry given by
.
The benefits of DMO correction include noise filtering through dip filtering, minimized reflection
point scatter, improved velocity analysis, improved ties with crossing lines etc.
2.5.6. Statics corrections
CMP gathers don’t always conform to a perfect hyperbolic trajectory. A cause of this could be as a
result of near surface irregularities. Lateral velocity variations could cause a reflection event to switch
from long offset traces to short offset traces (negative move-out) as a result of complex overburden.
Static correction is targeted towards compensating for elevation differences such that all traces are
corrected to a datum level by removing the calculated travel times from the source to the datum and
from the receiver to the datum. Fig. 2.4 illustrates this concept.
Assuming the reflection paths are vertical, travel-time of wave from source down to datum is
The correction for the travel-time for a geophone at the source point
is given by
46
where
Fig.2.4. Illustration of elevation statics (Yilmaz, 1987).
Subtracting
from the arrival time is the equivalent to placing the shot and receiver groups on a
datum plane. Elevation correction is given (Yilmaz, 1987) by;
.
Another form of static correction is the near surface (weathering) correction. It corrects for the effect
of variable thickness and lateral variation of the weathering layer. Methods used to correct for these
effects are up-hole surveys, refraction statics and residual statics. The term static is used because the
time shifts applied are constant for the entire trace.
47
2.5.7. Residual statics correction (RSC)
Of course, little random errors in static alignment on traces remain after static corrections are applied.
These little random errors are called residual statics and are obvious in corrected gather. Residual
statics cause dim spots along reflection horizon as well as false structures on stacked sections which
could be misleading. RSC tends to better align CMP gathers with travel-time deviation and usually,
velocity analysis is repeated to improve velocity picks after it is applied.
In general, RSC involves three phases: these phases include picking (calculating) the total residual
time shift
, decomposition of
into receiver, source, structural and residual terms and
application of derived source and receiver terms of travel-times on pre-NMO-corrected gathers.
RSC is basically effective in estimating short wavelength statics with the most widely used method
being the surface consistent method. This method assumes that static shifts are time delays that
depend only on the shot-receiver location on the surface and not the ray-paths in the sub-surface. The
total residual time shift
can be expressed as
. (Yilmaz,1987).
is a structural term while
is a hyperbolic term.
To minimize the error energy, we could use the least squares approach given by Yilmaz (1987) as;
48
2.5.8. Stacking
Stacking simply means summing up of individual traces to produce one stacked trace that represents
the CMP. The stacking process involves making a numerical average of all the traces samples in a
CMP gather for each sample time. By this averaging process, non-coherent events add out of phase
and are cancelled out. Stacking n traces enhances the S/N ratio by
displayed with the CMP number in the horizontal direction and
. The stacked section is
in the vertical direction. Since
stacking enhances horizontal events in a CMP gather, anything not horizontal is suppressed.
Proper measures must be observed before stacking since it would be almost impossible to undo the
process.
2.5.9. Digital filtering
To an extent, we can regard all pre-processing and the main processing stages as filtering processes
since they are directed towards eliminating unwanted signals. We can therefore describe a filter as a
system which discriminates against a certain range of its input. Frequency and apparent surface
velocity are two basic properties upon which filters base their discrimination.
In frequency filtering, a zero-phase band limited wavelet can be used to filter a seismic trace. The
filter operator is the time domain representation of the wavelet, while the filter coefficients are the
individual time samples of this operator. The above described process is termed zero-phase frequency
filtering. It does not modify the phase spectrum of the input trace but limits the band of its amplitude
spectrum.
49
Frequency domain filtering involves multiplying the amplitude spectrum of the input seismic trace by
that of the filter operator while in the time domain. It involves convolving the filter operator with the
input time series. The summary of both frequency and time domain filtering is illustrated in figures
(2.5) and (2.6). Both formulations are based on the concept that convolution in the time domain is
equivalent to multiplication in the frequency domain and vice versa (Bracewell, 1965).
Frequency filtering could be in the form of band pass which only extracts a defined band of frequency
but does not alter its phase. Examples of frequency filters are high-pass, low-pass, band-pass and
notch frequency filters. Band-pass filters are the most used.
In velocity (f-k) filtering, the f-k plane allows us to separate events that dip in the (t,x) plane by their
dips. This consequently eliminates certain types of noise from a data. This filter suppresses coherent
linear noise arising from side-scattering energy in stacked common shot gathers.
The basis for f-k filtering is that events with the same dip in the t-x plane are mapped into a single
line in the radial direction of the f-k plane irrespective of their location on the t-x plane. This results in
the defining and application of a reject fan in the transform domain and then inverse transposing the
data back to the (t,x) domain.
F-k dip filtering is recommendably applied on shot gathers instead of CMP gathers. Its application
after stacking further suppresses noise. Likewise, coherent linear noise on stacked data can be
suppressed by post-stack migration process which also incorporates dip filtering.
2.5.10. Migration
The goal of migration is to make the stacked section as close to the geological cross-section as
possible. When displayed, the stacks appear to be a geological image. Thus, migration is also called
seismic imaging.
50
Migration is an inverse wave scattering calculation that relocates seismic reflections and diffractions
to the location of their origin, arranging data laterally in the image volume. It is inherently a 2-D or 3D procedure and of course, the 3-D migration produces a more accurate seismic image. Migration
operation collapses subtle diffractions associated with growth faults and also unties ‘bowties’ into
synclines.
Migration could either be pre-stack or post-stack. Pre-stack migration is recommended for survey
areas with complex structures such that each seismic trace is migrated solely before stacking.
Velocity analysis algorithms in pre-stack migration allow processors to improve their velocity models
between migration iterations. Its major disadvantage is the large data size to be migrated. Post-stack
migration involves migrating a stacked section.
Besides distinctions between 2-D versus 3-D and pre-stack versus post-stack migrations, migration
implementations are also distinguished as either time migration or depth migration.
Time migration assumes that local variations in velocity is a function of depth alone and usually
ignores the refraction that occurs when rays cross the velocity boundaries. When lateral variations are
much, time migration is not recommended, the migration algorithms for time migration are quite
simple.
51
Fig.2.5. Design and application of zero-phase filter in the frequency domain (Yilmaz. O,. 1987).
Fig.
2.6. Design and application of zero-phase filter in the time domain (Yilmaz, O., 1987).
52
Depth migration on the other hand assumes a known velocity model and estimates the correct shape
of diffractions by ray-tracing or wave front modeling. It is a better option for areas with complex
structures.
Migrated sections are commonly displayed in time rather than depth to avoid errors introduced by
inaccurate time to depth conversion and also to facilitate the comparison of migrated section with
unmigrated section which is usually displayed in time.
In 2-D migration, we migrate the data once along the profile while for 3-D migration, we first migrate
the data in the in-line direction and then migrate the already migrated data in the cross-line direction.
This is the two-pass 3-D migration. One-pass 3-D migration can be done using a downward
continuation approach (Yilmaz, 1987).In this technique, also referred to as finite difference method,
one uses the seismic data as recorded at the surface datum plane to calculate what it would have
looked like if it were recorded on a plane one depth step deeper in the earth. The same is done for
deeper steps until one approaches the actual depth of a diffractor.
As one steps closer, the diffraction hyperbola becomes smaller and collapses eventually at a point
when t = 0, i.e. the diffractor depth. The diffractor is said to be imaged.
The above explained technique is one among other migration algorithms that have been developed to
ease degrees of complexities in the process. Others include Kirchoff migration and f-k migration.
Kirchoff migration employs Huygen’s principle (1.7.1). Here, diffraction energy is collected along
diffraction curves. Kirchoff migration assumes straight rays and takes into account amplitude and
phase corrections that can be derived from wave equations. The purpose of Kirchoff migration is to
sum up the energy produced by every Huygen’s secondary source and map it into its point of
generation.
53
F-k migration uses the 2-D Fourier transform to convert the input t-x section into an f-k section.
Migration of seismic data is carried out in the frequency domain. The seismic time section is first
converted to an apparent depth section. In the apparent depth mode, a constant velocity is assigned to
all reflections. Then, this seismic data is two-dimensionally transformed into frequency and wave
number domain. The inverse 2-D FT provides the migrated t-x section
2.6. Mathematical operations in seismic data processing
Three types of mathematical operations constitute the heart of most data processing. They are Fourier
transform, convolution and correlation, however, the z-transform can be included as another
mathematical operation.
Fourier transform (FT) is used to convert from the time domain to the frequency domain and vice
versa. It can also be used alongside other transforms to convert into and out of other domains.
Convolution operation replaces each element of an input with a scaled output function. This operation
better explains the limitations placed on sampling and signal reconstitution.
Correlation is a method of measuring similarities between two data sets by determining the time shift
that will maximize such similarities. It is also used to extract short signals of known wave shape from
long train waves as used in vibroseis processing. Auto-correlation is a situation where a data is
correlated by itself and cross-correlation occurs when different data are correlated.
2.6.1. The Fourier series/transform
Consider a mono-frequency sinusoidal function
given by
54
A time-dependent (periodic) signal
can be analyzed to an infinite series of sinusoids, each having
its own frequency, peak amplitude and phase using the Fourier series by;
The basic Fourier series expansion of a function
is given as
(2.24)
Where for any non-negative integer,
It can be expressed in exponential form as;
(2.25)
where
Here,
formula.
is an imaginary unit and
in accordance with Euler’s
55
Fourier transform on the other hand, transforms a function into a related function of different
variables. This is done by multiplying the original function by a function of both sets of variables
technically called a kernel and then eliminating the first set of variables by integrating the product
with respect to these variables between definite limits. By integrating using the inverse of the initial
kernel, the first variable can be re-obtained. This process is termed inverse Fourier transform. Table
2.1 shows a table of Fourier transform theorems as given by Bracewell (1965).
In signal processing, the Fourier transform often takes a time series or a function of continuous time,
and maps it into a frequency spectrum. That is, it takes a function from the time domain into the
frequency domain; it is a decomposition of a function into sinusoids of different frequencies; in the
case of a Fourier series or discrete Fourier transform, the sinusoids are harmonics of the fundamental
frequency of the function being analyzed.
Discrete Fourier transform (DFT), also called finite Fourier transform is widely employed in signal
processing to analyze frequencies contained in a sampled signal and in operations such as
convolution. Its operation is based on the fast Fourier transform (FFT) algorithm. We use the fast
Fourier transform (FFT) to compute the peak amplitude
FFT,
of the time function
Conversely, given
and
and phase
at every frequency. The
is given by;
, we can synthesize
using the inverse transform
56
Table 2.1.Basic Fourier transform theorems (Bracewell, 1965).
In our equations,
to as transform pairs.
is the FT of
and so,
is the inverse FT of
and they are both referred
is usually complex and so may be separated into real and imaginary parts.
Equation 2.26 then becomes (Telford et al., 1990);
The integrals
are called cosine and sine functions respectively.
Just as the Fourier transform describes the analysis of a time function in its individual frequency
components (Fourier analysis), the inverse Fourier transform synthesizes the time function from the
constituent frequency components. From the appearance of the two transforms it is easily seen that
two successive transformations will yield the original time-function within a constant factor.
Fourier transform operations could be either 1 dimensional or 2 dimensional. Using the onedimensional Fourier transform for a single trace (independent of distance) yields the complex
spectrum as a function of frequency. However, by considering the array of traces only (keeping time
constant), the Fourier transform becomes a function of wave-number only. Obviously, one must
consider both variables, distance and time simultaneously which can easily be achieved by combining
57
the two one-dimensional transforms into one two-dimensional Fourier transform, giving rise to 2-D
Fourier transform. 2-D transform is the basis for both the analysis and implementation of multichannel processes. It is a mathematical process which basically involves transforming a set of data
from time-space domain to the frequency-wave-number domain. The two-dimensional complex
spectrum, therefore, is a function of both frequency and wave-number. 2-D transforms decompose a
wave field into its plane-wave components.
Other types of transform also exist. They are Laplace transform, Radon transform and z-transform.
Transforms and their functions are in domains specified by the variables. They are designed to
preserve information as they switch domains even though minor alterations occur due to
approximations and truncations.
2.6.2. Convolution
Convolution is a process whereby an input time-function is filtered by another time-function to
produce an output time-function. Simply put, it involves multiplying two arrays in a fashion that
results in a third array!
Convolution can be expressed as an operation on sampled data where the unit impulse is denoted by
or as a sample set with a continuous function
. This operation is performed in what is properly
called, the time-domain. Utilizing the Fourier transform, this process can also be described in the
frequency-domain as well. The convolution of two-time functions
dependent function
given by
where denotes the convolution operator.
yields the time
58
Convolution can be defined based on equation 2.29 as the integral of the product of the two functions
after one is reversed and shifted. As such, it is a particular kind of integral transform. The integration
range depends on the domain on which the functions are defined. Using zero-extended or infinite
domains is sometimes called a linear convolution.
However, for sampled functions x(t) and h(t), we simply fold, shift, multiply and add. If the sampled
functions have number of samples
samples
respectively, the convolution y(t) has a number of
given by;
Convolution can be commutative, i.e.
.
If X and Y are two independent random variables with probability distributions x and h respectively,
then the probability distribution of the sum X + Y is given by the convolution
It is usually easier and faster to perform convolution in the frequency domain for functions of long
duration.
Finally, let us consider the convolution theorem. It states that the convolution of two functions in the
time domain is equivalent to multiplying their Fourier transforms in the frequency domain. This is
given by;
The inverse of the convolution theorem is also given as,
59
2.6.3. Correlation
The correlation of two time dependent functions x(t) and h(t) yields the time dependent function as;
The process of correlation is the same as convolution except that the operator is not reversed and
there is no folding around t=0. The output trace is called a correlogram. It aims at comparing traces /
signal and locating the position where similarities exist. In signal processing, the cross-correlation (or
sometimes "cross-covariance") is a measure of similarity of two signals, commonly used to find
features in an unknown signal by comparing it to a known one. It is a function of the relative time
between the signals, is sometimes called the sliding dot product.
When we correlate functions, one of them is usually termed the operator. For sampled functions, we
simply shift, multiply and add. If the sampled functions x(t) and h(t) have number of sample
, the correlation y(t) has a number of samples
given by;
When corresponding values of varying data are multiplied and their product added, what results is
their cross-correlation expressed by Yilmaz (1987) as;
In an Autocorrelation, which is the cross-correlation of a signal with itself, is a measure of how well a
signal matches a time-shifted version of itself, as a function of the amount of time shift and there will
always be a peak at a lag of zero. Autocorrelation is useful for finding repeating patterns in a signal,
60
such as determining the presence of a periodic signal which has been buried under noise, or
identifying the missing fundamental frequency in a signal implied by its harmonic frequencies.
If X and Y are two independent random variables with probability distributions x and h, respectively,
then the probability distribution of the difference − X + Y is given by the cross-correlation of f and g.
In contrast, the convolution, x * h, gives the probability distribution of the sum X + Y.
Applying the convolution theory on the correlation yields;
Correlation is not commutative.
In vibroseis processing, correlation is used to recover a nice pulse from a long out-going sweep (pulse
compression). The uncorrelated seismogram generated from the vibroseis sweep is the convolution of
the sweep with the earth’s response, analogous to equation 2.2. And so, in processing the uncorrelated
seismogram to remove the effect of long sweep, we correlate the vibroseis sweep with the
seismogram to obtain the correlated seismogram.
Cross-correlating recorded data with the oscillatory source itself is very imperative in vibroseis data
processing (Dobrin and Savit, 1988). This involves a comparison of the two signals with
progressively delay times. Related seismic events show greater coherency with source signal than
random noise.
2.6.4. The z-transform
When a continuous function
is sampled, its sampled form
is given as
61
That means, in sampling a continuous function,
by the impulse function every
at a sampling interval
, we simply multiply it
.
In mathematics and signal processing, the z-transform converts a discrete time domain signal, which
is a sequence of real numbers, into a complex frequency domain representation. The z-transform is
used to express the amplitude of successive uniformly spaced time intervals as a polynomial in ; the
power of variable z is the number of unit delays associated with each sample in the series.
Multiplying by
, delays the time series by n time units, while multiplying by
time series by n units. Taking the FT of (2.37), we obtain;
The convolution theory also applies to the z-transform as;
, advances the
62
CHAPTER THREE
LITERATURE REVIEW
3.1. Introduction
The seismic reflection method is a method of exploration geophysics that uses the principles of
seismology to estimate the properties of the earth’s sub-surface from seismic reflected waves. This
method is mainly directed at locating depths of reflecting surfaces and the seismic velocities of subsurface rock layers. It is mainly used for hydrocarbon and mineral exploration due to its ability to
penetrate deep into the earth, up to several hundred meters. Over the years, this method has been
developed to quite a high degree of sophistication.
The method requires a controlled source of energy or signal, usually an explosion or vibration, sent
into the ground at a known time and location. By taking note of the time it takes the signal to be
reflected back from the boundaries between rocks to a receiver on the earth’s surface (usually a
geophone for land and a hydrophone for marine), the depths and features of the sub-surface interfaces
can be estimated after some processing and analyses.
Several field procedures are in use, each being distinguished by the different layout of the detectors
relative to the shot-point.
This report reviews ten (10) reflection operations carried out in different parts of the world, at
different times and for different purposes. The choice of the cases reviewed in this work was carefully
and thoughtfully arrived at. This is to enable a better comparison of data acquisition parameters,
equipment used and the different approaches to data processing and interpretation.
63
The cases reviewed in this report are: Seismic reflection studies in the Skellefte Ore district. Northern
Sweden, Seismic reflection tomography: A case study of a shallow lake in Lake Balaton, COCORP’S
deep seismic reflection profiling in the Williston Basin (comparison of explosive and vibroseis source
energy penetration), Reflection seismic and ground-penetrating radar: Study of previously mined
(lead/zinc) ground, Joplin, Missouri, Seismic processing and velocity assessments in the oil and gas
resource potential of the 1002 area, Arctic National Wild Life Refuge (ANWR), Alaska, Seismic
reflection investigation of C-1 to F-1 sector of the Superconducting Super Collider (SSC) ring west of
Waxahachie, Texas, Analysis of existing seismic reflection data in South Florida for Aquifer Storage
and Recovery (ASR) regional study, 4D acquisition using two boats in the Enfield oil field offshore,
Western Australia,3D interpretation of the Espoir field area, offshore Ivory Coast and Structural and
stratigraphic mapping of Emi field, offshore Niger Delta, Nigeria.
.
3.2. Case 1: Seismic Reflection Studies in the Skellefte Ore District, Northern Sweden
Several world class mines are located in the western part of the Skellefte Ore district, for instance, the
Kristineberg VMS mine. The Skellefte district is a volcanic arc which formed betweena sedimentary
basin (Bothnian Basin) and a continental basin (the Aindsjaur group)(Weihed, 2001). Today,the area
is freely defined (150 by 150km in size) in Northern Vasterbotten and southern Norbotten in the
Northern part of Sweden. The area is rich in mineral occurrence. The surface geology in the
Kristineberg area consists of volcanic rocks overlain by fine-grained turbiditic metasediments.
Data was obtained in this area along two north-south oriented profiles (profiles 1 and 5) between late
August and early September, 2003 (fig. 3.1). Each data was about 25km long.
64
Fig. 3.1 Bedrock geological map of the Kristineberg area, with the locations of the seismic profiles,
the CDP lines and the temporary seismic stations
65
The purpose of the survey was to obtain high resolution images of the upper10km of the crust. The
aim of the survey was to study the very complex crustal architecture of the region in the Skellefte Ore
district in the Kristineberg area at depth and to determine the relationship between the submarine ore
bearing Skellefte group and the overlying volcanic and sedimentary rocks south of the ArcheanProterozoic boundary. Barret and Maclean (2000) has more details on the geology of this area.
Profile 1 was first decided to run parallel to the major tectonic ligaments of the region. The profile
passed on top of the Kristineberg mine and crossed the exposed ore-bearing volcanic units of the
Vargfors group (coarse clastic unit of a sedimentary sequence).
Profile 5 was mainly located along very small roads or sections through rough terrains and runs
parallel to profile 1. This choice was due to lack of infrastructure in the east-west direction and its
ability to allow cross-profile recordings to help correlate observed structures between the profiles.
The separation between both profiles was about 6-8km. Both profiles started in the Revsund granites
in the south, crossed the ore-bearing Vangfors formation and ended in/near the Revsund granites
bordering the Skellefte field to the north (Tryggvason et al., 2003).
Data acquisition:
The 3D reflection survey was conducted within an interval of approximately 3 weeks. Two data
recording systems were used, the SERCEL 348 and SERCEL 408. SERCEL 348, which is an older
version is heavier and more strenuous in rough terrains, was used for profile 1. Also for profile 1, a
group of six 10Hz geophones was used. The more modern SERCEL 408 was used for profile 5
alongside single 28Hz geophones. A summary of the acquisition parameters are shown in table 3.1.
An alternative source of a vibratory system was contemplated but the idea discarded due to very high
mobilization costs. Recording started at the southern ends of the profiles. Data was recorded on
66
temporal seismometers spread out in the area to 20s and sampled every 2ms. GPS instruments used
ensured high precision of shot-hole and geophone locations.
Processing:
PROMAX was the processing package used to process the data which was done at the Department of
Earth Science, Uppsala University. Though recording was done for 20s TWT, processing was done to
4s TWT only (about 12km depth). Similar processing schemes were used for both profiles. Editing
was done in shot domain to remove cultural traffic and ambient noise. Processing sequences applied
to the data are summarized in table 3.2.
Some data on profile 5 were corrected for reverse polarity. First arrivals were picked with an
automatic neutral network algorithm and proper correction effected where necessary after manual
inspection.
3.3. Case 2: Seismic Reflection Tomography: A Case Study of a Shallow Lake in Lake Balaton.
Lake Balaton is the largest lake in Central Europe. It is situated near Budapest in Hungary and is
home to precious natural treasures (fig. 3.2). Lake Balaton is 77km in length and has a width range of
4-14km. Near surface material of the lake consists of unconsolidated, medium to coarse-grained mud
and medium to coarse gravel interspaced with thin discontinuous Paleosols and clay. Test hole of
seismic section in the area indicates a thick Holocene mud layer (3.5 – 4m thick) present just below
the water and a stiff saturated clay stratum just below it (McLamore et. al., 1978).
67
Fig. 3.2. Location map of the broader study area of Lake Balaton.
68
Table 3.1. Data acquisition parameters for profiles 1 and 5
Type of survey
Nominal spread
Nominal fold:
Profile 1
Profile 5
Energy source parameters
Source type
Nominal shot spacing
Charge size
Recording parameters
Number of active recording channels:
Profile 1
Profile 5
Record length
Sampling rate
Maximum offset:
Profile 1
Profile 5
Geophones
Profile 1
Profile 5
Total profile length
2-D Crooked line
End on/shoot through
17
25
Dynamite
100 m
0.5-3 kg
140
200
20 s (about 60 km depth)
2 ms
4000 m
5000 m
Bunch of six 10 Hz
Single 28 Hz
55 km
Data acquisition
The operation was a shallow marine reflection operation. Data was collected using the seismic
profiling system,
receiver with boomer source operating at 150 joules and firing
at 0.25m interval. Table 3.3 shows acquisition parameters.
Acquired data, which was done on a PC-based 50KHz, 16 bit sampling of filtered Seistec data
recorded in SEG-Y format, was done digitally (Soupios, et al., 2005).
Processing
The data was processed on a SUN work station using CWP/ Seismic Unix. In the data processing,
elimination of superimposed surface-related noises on shallow reflections, which is common
especially in shallow marine seismic surveys, consumed most of the processing stages. Such surface-
69
related noises which include direct waves, guided waves, water bottom multiples, reflected refractions
and side scattered noise (Lee, 1999).
Table3.2. A summary of the processing operations on profiles 1 and 5
Read SEG format files
20,000 ms
Reverse negative polarities in
first breaks
Profile5 only
Spike and noise edit
Both profiles
Pick first breaks
Both profiles
Refraction statics
Profile 5: 240 m datum, 1 layer model,
5700 m/s replacement vel.
Profile 1: 300 m datum, 1 layer model,
5500 m/s replacement vel.
Spectral Whitening
Profile 1: Time-variant
Profile 5:
10,20,75,105 Hz
Band-pass filtering
Profile 1: Time & space-variant
Profile 5:
20-40-70-120 Hz, 0-4000 ms
Air blast attenuation
Profile 5 only
Sort to CMP domain
Both profiles
Velocity analysis
Both profiles
NMO correction
40% stretch mute
Residual statics, maximum power autostatics, pass1
Velocity analysis
Residual statics, maximum power autostatics, pass2
Stacking
Both profiles
F-X Deconvolution
Both profiles
Time-variant scaling
Profile 1
Trace equalization
Profile 5
Processing tools applied on the data include band-pass filtering, mute (windowing), f-k filtering,
spectrum analysis, gain deconvolution and migration. Velocity analysis was not possible because of
the single fold nature of the survey, however, a tomographic approach of the model using reflection
travel-times from the pre-defined reflectors was attempted to tackle that issue. In this case, a 5layered, 1D background velocity extracted from a bore-hole in the vicinity of the seismic section was
used. The forward model was solved using finite-difference wave equation modeling.
70
Table 3.3. Acquisition parameters for the Lake Balaton survey
Waveform format
Source
Firing interval
Precision of Positioning
Number of traces
Record length
Sample frequency
Length of profile
Number of channels
Gain
Low cut filter
High cut filter
SEG-Y, 2 bytes integer
Boomer at 150 Joules
0.25 meters
< 1 meter
512
1251 samples
50 kHz
250 m
1
Linear
1 kHz, second order
10 kHz, second order
3.4. Case 3: COCORP’S Deep Seismic Reflection Profiling in the Williston Basin (Comparison
of Explosive and Vibroseis Source Energy Penetration)
In 1990, a joint explosive/vibroseis experiment was scheduled for the fall of that year in the Williston
basin of North Dakota and Montana. However, the dynamite experiment was not performed due to
permit and liability issues. The vibroseis experiment was successful. A key observation from the
vibroseis experiment was the apparent lateral truncation of prominent moho-reflectivities near the
western boundary of the Trans-Hudson Orogen which interpreters thought might be related to phase
changes associated with its formation. Other details of the 1990 survey are summarized in Nelson et.
al., (1993).
In 1992, COCORP (Consortium for Continental Reflection Profiling) assisted by LITHOPROBE,
returned to North Dakota and Montana to collect a new set of explosive/vibroseis data, being guided
by the previous survey of 1990.
The aim of the new survey was to re-image key portions of the 1990 vibroseis profiles with explosive
sources, to collect a north-south profile providing 3D control on Trans-Hudson structure (fig. 3.3) and
71
to confirm the extent of prominent moho reflections as stipulated by the earlier survey.
Fig. 3.3 A map showing the survey area of the Williston basin and Trans-Hudson Orogen of North
Dakota.
72
Another objective of the survey was to test the feasibility of using explosive source in low-fold mode
for deep reflection profiling. The objective of this survey which is of the most interest in this review
is the comparison in the effective imaging of deep structures between the 1990 vibroseis survey and
the 1992 explosive survey.
Data acquisition
Three explosive data were collected in the Williston basin in the 1992 survey; MT12, ND2 and ND3,
each having its own objective. MT12 was designed to test the moho-reflectivity, ND2 was collected
to confirm the along-strike correlation of primary crustal fabrics as suggested by comparison of the
COCORP’s data with subsequent LITHOPROBE surveys in the Saskatchewan to the north (Baird,
1995) and ND3 was acquired to extend imaging further into the bounding superior craton east of the
Trans-Hudson Orogen.
A total of 129 shots were taken for MT12, ND2 and ND3 surveys, of which 97 shots were used to
study shot-to-shot variability. A high fold (30) data was acquired for the vibroseis data while a low
fold (6) data was obtained for the explosive data.
For the 1992 explosive survey, multiple DFS V systems, each equipped with 120 channels recording
system was used. Explosive sources were positioned with a nominal 3km spacing. Explosive shots
varied (from 9.5-90kg) with depths varying from22-38m (140-180ft). This was due to shots being
placed in different media (clay, sand, shale and sand stone). The MT12 survey was shot from west to
east (fig. 3.3) with multiple DFS V systems used in-line to create a 24-30km spread. Nine (9)
L21A(10Hz) geophone was used as detectors.
For the vibroseis survey of 1990, Amoco’s 600+ channel telemetric recording system using radiolinked seismic group recorders deployed in a roll-along configuration was used. The source
comprised eight (8) Mertz vibrators [52,000 lb (23,000N) peak force P-wave] positioned bumper to
bumper to form a 40m in-line linear array. The source array was moved one pad length per sweep
73
with vibration points every 200m. Twelve (12) SM-7 (10Hz) geophones acted as detectors per station.
Production shots for ND2 and ND3 used 45kg of explosive in two shot holes with a separation of
85m.
Processing
It is clearly obvious that the geometries, equipment and processing procedures for both data were
different in every sense. Another major difference was the two years interval separating both
operations. As a result, no quantitative or definitive comparison could be made on both data.
However, given that eleven (11) points from the approximately 35km of vibroseis and explosive data
overlapped on both the western and eastern portions of the transect MT10/12 and ND1-ND3, an
analysis became useful and necessary. Data in the overlapped region included shots and receivers
placed in both surface media (Steer et al., 1996).
Since the acquisition and time of acquisition of both data sets were different, both data were
normalized to minimize amplitude variations caused by the effect of varying ambient noise conditions
and were sealed to reduce synthetic biases which resulted from the use of different recording systems
and geophone patterns. The amplitude normalization was done by taking the absolute amplitude of
samples from the eleven coincident channels centered at 4km (a distance thoughtfully chosen),
summing them up to a single channel and then calculating the RMS amplitudes for selected 100ms
windows from 2-50 TWT.
The correlation process left the vibroseis data bandwidth limited. Although no much difference will
be noticed if the explosive data was band-pass filtered to match the vibroseis bandwidth.
74
3.5. Case 4: Reflection Seismic and Ground-Penetrating Radar Study of Previously Mined
(Lead/Zinc) Ground, Joplin, Missouri.
The Missouri Department of Transport (MoDOT) proposed constructing an inter-state route Alternate
“E” across the Joplin area of Missouri (fig. 3.4). The site had been previously mined for Lead/Zinc
and had been extensively open-pit and so the integrity of the ground became a thing of concern. In the
winters of 1996/1997 and 1997/1998, the MoDOT contracted the departments of Geology and
Geophysics at the University of Missouri-Rolla to conduct a geophysical survey on the entire area to
ascertain the condition of the ground.
The survey had the following objectives;
1.
To map the Mississippian bedrock
2.
To identify and Paleosinkholes and abandoned mine features
3.
To determine the structural geologic trends in the area
4.
To identify major bedrock faults and/or fracture zones
5.
To identify and locate abandoned mine access and ventilation shafts overlain by surficial
milled ore rock.
The applicability of these objectives was primarily to aid MoDOT engineers involved in route
planning, hazard assessment and site mitigation. To achieve this, the University of Missouri-Rolla
conducted two geophysical surveys on the area; a reflection survey to investigate objectives 1- 4
above and a ground-penetrating radar (GPR) survey to attempt achieving the fifth objective.
75
Fig 3.4. Map of the survey area in Joplin, Missouri.
76
Data acquisition
A total of 14,600 lineal meters of shallow reflection seismic data, 15,000 lineal meters of GPR data
and 9 down-hole seismic calibration check-shots were acquired.
An elastic wave generator weight drop was used as source while single channel 40Hz geophones
served as detectors. Seismic reflection data was acquired using a 24-channel Bison Engineering
seismograph with roll-along capabilities. Weight drop source was impacted 6-14 times per shot
record. An interval of 3m was employed for shot, receiver and near-offset spacings. Data processing
was on a Pentium PC using WINSEIS.
Data processing
Processing steps applied to the seismic data include reflection identification from bedrock, muting of
all arrivals prior to bedrock event, first breaks and ground rolls, restoration of shot-gather traces to
CMP gathers, elevation correction, velocity analysis for each line, calculation and application of
surface consistent statics, NMO correction, stacking, residual statics, re-stacking statically corrected
data and band-pass filtering (Shoemaker et al., 2000).
To provide sub-surface lithologic/velocity control and to confirm the interpretation of the reflection
data, MoDOT drilled 23 bore-holes in the area but down-hole calibration check-shot survey data was
acquired at 9 bore-hole locations. Surface to bore-hole tomography was applied with a spacing of 1m.
The check-shot data acquired was used to determine P-wave time-depth and velocity time-depth
relationships for shallow sub-surfaces.
For the GPR profiling, a Geophysical Survey System Inc. sub-surface interface radar unit (SIR-8)
equipped with a 500MHz monostatic antenna was used. A total of 1,500 lineal meters of GPR data
was obtained. The data was acquired as suites of parallel GPR profiles with a separation of 1.5m.
Sampling interval was 30scans/foot and trace length was 100ns.
77
The GPR was processed on a Pentium PC using RADAN. Processing operation applied to the data
include trace normalization, vertical gain application, horizontal and vertical filtering.
3.6. Case 5: Seismic Processing and Velocity Assessments in the Oil and Gas Resource Potential
of the 1002 AREA, Arctic National Wild Life Refuge (ANWR), Alaska.
The Geophysical Service Inc. (GSI), under a contract to a consortium of petroleum exploration
companies in 1984 and 1985, acquired and processed 1,451mi (2,351km) of multi-channel seismic
data on and adjacent to the Arctic National Wildlife Refuge (ANWR). That was part of a petroleum
resource evaluation study which was later performed in 1987 by the Fish and Wildlife Service (FWS),
The Bureau of Land Management (BLM) and the US Geological Survey (USGS). Details of this
evaluation can be seen in (Bird and Magoon, 1987).
At that time in 1987, the USGS reprocessed a part of the seismic data on a VAX11/780 computer
using the DIGICON, Inc., DISCO software package. The reprocessed section was stacked and
intended for use as illustration in the resource assessment publication of 1987.
A renewed interest as to the amount of petroleum that could be buried beneath the ANWR coastal
plain led to a re-evaluation of the resource potential and a re-processing of the 1984/1985 seismic data
with a more modern and more sophisticated workstation in 1996. It was done on a SUN workstation
using PROMAX processing software developed by Landmark, Inc., together with in-house developed
software. This project started in 1996 and the processing was performed on a SUN SPARC Station2
but in August, 1997, the system was upgraded to a SUN ULTRA1because the latter was fast enough
to run some testing of pre-stack depth migration (Lee et al., 1999).
Data acquisition
982.7km (606.6mi) of seismic reflection data using dynamite sources and 247.1km (152.5mi) using
vibroseis source was acquired by GSI from January, 1984 to April 1984. In 1985, another 1121.5km
(692.3mi) seismic reflection data using vibroseis source only was obtained by GSI. A summary of the
78
recording parameters for both dynamite and vibroseis data is shown on table 3.4. Notice the change in
vibroseis sweep for both years and also the change in correlation time.
Data processing
Firstly, the rest of the data was demultiplexed from SEG-B to SEG-Y format and then the geometry
was defined and data stored from shot gather to CMP gathers. At that stage also, the elevation and uphole times or depths were stored in the data base.
A two-window spiking deconvolution with operator lengths of about 180ms was applied to both
dynamite and vibroseis data. That was done to improve temporal resolution. A minimum phase
deconvolution was applied to the dynamite data and a zero-phase deconvolution applied to the
vibroseis data. To correct for elevation, a floating datum was determined. By that floating datum,
elevation statics was computed and applied to individual traces.
F-k filtering was applied on shot domain only even though receiver domain f-k filtering was used in
certain parts of some profiles as an option to improve stack quality. F-k filtering suppresses linear
move-out events and improves S/N ratio significantly.
NMO correction using an automatic stretch with a maximum percentage of stretch of about 20% was
then applied to the data.
3.7. Case 6: Seismic Reflection Investigation of C-1 to F-1 Sector of the Superconducting Super
Collider (SSC) Ring West of Waxahachie, Texas.
Between 19th June and 29th June, 1990, LCT Houston and its sub-contractor, Dobecki Earth Science
(DES) performed a seismic reflection survey along a 16,600ft segment of the SSC, ring west of
Waxahachie (fig. 3.5). The contract was awarded to them by The Earth Technology Corporation
(TETC). That survey followed an earlier feasibility survey carried out some one mile south of the
present survey area in February of the same year (LCT, 1990).
79
Table 3.4. A table showing recording parameters for both dynamite and vibroseis data of the 1002 area
The initial survey was able to define the nature of the Austin Chalk/Eagle Ford shale contact as well
as deeper beds. However, the lack of clarity at the Austin/Eagle Ford (A/E) interface reduced
amplitude at the interpreted base of Eagle Ford and on very deep reflections. This issue necessitated a
more elaborate survey with the major aim of accessing the continuity (faulting) of this key interface
(ie. A/E), which was of major importance to the planning and construction of a ring tunnel.
80
From the earlier feasibility survey, depth to the A/E contact was of the order of 200ft. the thickness of
soils over chalk in the area was much thinner. Typical soil thickness was about 2 inches only, even
though thicker soil and deeper Austin weathering were encountered in some deeper ravines along
several check runs. Surface materials included grass fields, corn fields, treed thickets and water in
cattle stock pond.
The latter survey was decided on to be executed in a similar fashion as the previous. That means same
acquisition, source/receiver type, instrumentation, CDP fold and processing parameters. But due to
the thin nature of the soil coupled with the dry nature of the soil (temperature was typically in excess
of
F), a sledge hammer source was opted for as opposed to the use of ‘Buffalo gun’ source as on
the previous survey. Another reason for this choice was based on the initial noise survey and periodic
comparisons made during the production phase. However, in areas like the northern third of the line
with heavy trees and steep under-growth, a Buffalo gun source was used since it did not require
swinging.
Data acquisition
Data was acquired using a 24-channel seismograph for recording, single 40Hz geophones for
detectors and a sledge hammer (nine stacked sledge hammer blows on a steel plate) as source. Shots
were taken at a distance 60ft from geophone points, geophone spacing was 10ft and consecutive shots
were fired at 20ft interval. The acquired data had a 600% coverage.
81
Fig. 3.5 Location of seismic line segment C1 – F1
82
Processing
The data was processed in the Houston offices of Entropic Geophysics, Inc. (EGI) under the direct
QC supervision of Dr. Thomas, L. Dobecki (DES). The processing sequence applied to the acquired
data is basically the same sequence followed in the processing of the initial data.
Surface consistent residual statics using maximum power of CMP stacks was computed using a
500ms window with multiple gates and a pilot trace containing 31 traces. Though this method worked
well for the coastal plain, however, in complexly deformed areas, a window to guide the static
computation could not be picked, and so, the method didn’t go down well in such areas.
In such cases however, iterative measures were employed, usually 2-3 iterations was required to
obtain the best velocity for stacking. Optimum velocities and statics were used to stack the data.
Several stack methods such as the mean, median and weighted mean were employed in the stacking
process
DMO correction which has an advantage of improving velocity analysis, even velocities for migration
(Deregowski et al., 1981), had been performed on the shot domain of most of the data in the 1987
assessment. DMO velocity analyses were estimated using the same range of CMP gathers. It was
observed in a velocity analysis of sections without and with DMO correction that the DMO velocities
are higher in those sections without DMO correction than those with DMO correction applied to
them.
This can be attributed to the compensation of the dip dependence on velocity performed during the
dip velocity analysis.
Despite that DMO velocities were appropriate for subsequent migration velocity analysis, DMO
stacked sections appeared less inferior to normal stacked sections, as such, majority of the final depth
section came from processed stacked data without DMO correction.
83
A post-stack wavelet deconvolution was applied to all stacked data to correct for differences in source
types and to increase resolution. Prior to the post-stack wavelet deconvolution, a 1,000ms AGC was
applied to the data using a window with operator length 180ms.
A variable norm deconvolution method developed by Gray, (1979) was applied in the wavelet
processing. Different norms, window length and design area were employed in estimating wavelet
between 200 and 1,000ms in the stacked data. About 200 random windows were selected. A single
deconvolution operator was derived from the optimum parameters and applied to the whole line. A
datum correction using simple static shifts, corrected the data to a final flat datum (sea level).
Filtering was done using a band-pass filter with 8-10-68-92 Hz pass-band.
Migration of stacked data was done using Stolt’s wave –number frequency domain migration method.
The method employed migration velocity models derived from the DMO stacking velocities. Prestack depth migration was performed on the data and this produced a clearer image as seen in figure
14, particularly at 10,000ft on the right side of the section.
Depth conversion of the unmigrated data was done using average velocities strictly derived from the
DMO velocities. No well data or VSP data was available in the area to tie to the reflection data.
Velocities were modified where necessary in ascertaining the validity of depth sections. Although the
converted depth section, due to lack of well or VSP data, may lack full certainty, the obtained depth
sections were geologically accurate.
To enhance the visibility of seismic reflections in the data, a 8-12-56-68Hz pass-band was applied by
a median filter using a 3 by 3 grid and their amplitudes were modulated by the trace envelope as
illustrated by Lee et. al.,(1988).
Migration velocity was derived by smoothing DMO velocities using triangular tapers in time and
space. A 300 CMP was used as the half height of a triangular smoother in space and a 500ms was
used as the half height of a triangular smoother with time. Modification of migration velocity was
84
carried out where necessary. Its average velocity was derived using smooth gradient function with the
migration velocity as its input.
3.8. Case 7: Analysis of Existing Seismic Reflection Data in South Florida for Aquifer Storage
and Recovery (ASR) Regional Study.
Several reflection lines are known to exist near and south of Lake Okeechobee in South Florida. Most
of which were collected for oil exploration purposes at depths of about 10,000-14,000ft below ground
surface.
To restore the ecosystem in that region, the Comprehensive Everglades Restoration Plan (CERP)
came up with several projects, one of which was the ASR studies. The ASR studies project was
designed to evaluate the viability and potential effects of constructing about 333 ASR wells in the
area. To do this, a better sub-surface characterization of the area which will optimize effective
location of the ASR became expedient.
Given the numerous seismic lines that traverse the area, a question on the possibility of re-processing
selected portions of available deep reflection data to resolve shallow sub-surface stratigraphy in an
area south of Lake Okeechobee was considered.
In that regard, the US Army Corps of Engineers – Jacksonville district (USACE) teaming up with the
South Florida Water Management District (SFWMD), contracted the URS Group Inc. to review the
assessment in line with CERP’s project.
A seismic data coverage map was obtained from Seismic Exchange Inc. (SEI) for a large area of
Florida. After extensive considerations of the numerous lines on the data, two 5-mile sections, one for
each of the selected lines were chosen. One of the lines utilized a dynamite source with an east-west
orientation and the other utilized a vibroseis south with the same east-west orientation. The selected
85
seismic data were 2D seismic lines acquired in the mid to late 1980s by SEI, both of which targeted
petroleum in very deep zones.
Data acquisition
A total of 399 inputs were recorded for the dynamite line and 179 recorded for the vibroseis line.
CMPs for the dynamite line were 1,157 and 1,055 for that of the vibroseis. The acquisition parameters
for both lines are shown in table 3.5.
Processing
Processing operations applied to the data include demultiplexing of data and converting it to internal
Focus format, trace editing, geometry definition, spherical divergence correction and application of
surface consistent gain. Other processing operations performed on the data are stated in table 3.5.
No elevation data was available for the vibroseis line and so elevations were estimated to be 20ft
MSL at all point. Additional corrections to the data in the CMP domain were done using residual
static programs.
The final migrated stack had a technique known as XFreq applied to it. XFreq is a program designed
by Tricon to boost amplitudes of the higher frequency range thus, enhancing the frequency content of
the data. Its principle is based on dipole filtering. Colton and Nautiyal, (1996) deals more on its
operation.
3.9. Case 8: 4D Acquisition Using Two Boats in the Enfield Oil Field Offshore, Western
Australia.
In 2004, Woodside acquired a baseline seismic data using one boat and dual sources. At the time,
water depth was about 500m and the streamer configuration was 6 by 100m. The data obtained had
good quality with a usable bandwidth of 15-80Hz and the TWT at a target level of 2s. The survey
86
encountered a high degree of feathering which occurs due to current and a change in vessel track
resulting in about 50% infill.
Table 3.5. Acquisition parameters for the ASR studies seismic survey
Vibroseis data parameters:
Number of Recording Channels
Geophone Group Interval
Source Point Interval
Recording Geometry
Data Sample Rate
Record Length
Maximum common Midpoint (CMP) folds
Low Filter
Notch Filter
Alias Filter
Sweep Start
Sweep Rate
Sweep Length
Taper
The dynamite data parameters:
Number of Recording Channels
Geophone Group Interval
Source Point Interval
Recording Geometry
Data Sample Rate
Record Length
Maximum CMP fold
Low Filter
Notch Filter
Alias Filter
480 channels
75 feet
150 feet
Balanced split spread (240-7-240)
4 milliseconds
6,000 milliseconds
120
Out
Out
93 Hz
8 Hz
2 Hz/second
24 seconds
0.5 second
480 channels
60 feet
60 feet
Balanced split spread (240-240)
4 milliseconds
6,000 milliseconds
240
Out
Out
93 Hz
In July 2006, the Enfield field in the north-west shelf of Australia (fig. 3.6) commenced oil
production. Seven months into producing oil in the field, in February 2007, Woodside acquired a push
reverse 4D monitor survey data on the same area, this time using two boats with separate source and
streamer vessels.
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Table 3.6. A summary of the processing steps applied to the ASR studies data.
Reformat to Focus
Trace Edit
Geometry Definition and QC
Edit for Offsets
Spherical Divergence Correction
Surface Consistent Gain
Surface Consistent Deconvolution
Automatic Gain Control (AGC)
Datum Statics
Velocity Analysis –
Normal Move-out Correction
First Break Suppression
Surface Consistent Residual Statics
Signal Enhancement on Shots
Surface Consistent Residual Statics
Common Mid-Point Gather
Stack
Finite Difference Migration
XFreq
Automatic Gain Control (AGC)
Bulk Shift to Final Datum
The reason behind the choice of two boats was to maximize coverage around obstructions, even
though complete survey using this configuration is rarely achievable and to minimize in-fill as a result
of feathering.
Another key element of the 2007 monitor survey design was the 4D repeatability, maximum offset of
2,300m and the minimal infill due to limited vessel availability (Ridsdill-Smith et al., 2008).
Source repeatability is very high for two boat operation because of the relative smaller source vessels
and easy maneuvering of the source vessel compared to that of a 3D vessel. The push reverse
configuration comprised the source boat following behind the streamer boat, maintaining a safe and
regular distance.
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Fig.3.6. Location map of the Enfield 4D monitor survey. Inset shows the feather encountered on the
baseline survey in 2004.
89
Data acquisition
The Enfield monitor survey followed the same acquisition parameters as the previously conducted
baseline
survey.
It
followed
the
same
baseline
shot-point
interval
of
18.7
5m (flip-flop), gun separation of 50m and the streamer vessel was steered to match the baseline
receiver positions at a target offset of 1900m.
At the time of the monitor survey, Woodside had only a 4-streamer vessel which couldn’t replicate
the 6-streamer baseline coverage and so, an 8-streamer coverage was used.
3.10. Case 9: 3D Interpretation of the Espoir Field Area, Offshore Ivory Coast.
The Espoir field was discovered in 1980 by a joint venture comprising Philips Petroleum Co., AGIP,
SEDCO Energy and PETROCI. It is located at about 13km (8 mi) offshore Ivory Coast. From
October 1981 to February 1982, GSI carried out a 3D seismic survey which covered 7,700 kilometer
line of data. The area is located on the edge of the continental shelf and extends into deep waters
(fig.3.7).
Before the 3D survey, a previous survey had been carried out on the same area which led to the
drilling of the discovery well A-1X, which was drilled in approximately 515m of water to test a
structural high at the Albian unconformity level. Hydrocarbon was struck.
An appraisal well, A-2X, was also drilled to confirm the presence of hydrocarbon-bearing and
reservoir-quality sands beneath the unconformity surface in the area. A similar exploration in the
adjacent B1 block also revealed interesting features and thus, the need to perform an extensive 3D
survey operation on the area became necessary. The aim of the 3D survey was to obtain a detailed
mapping of the Albian structure.
90
Fig.3.7. Location map of 1981-82 Ivory Coast 3-D seismic survey showing structure of Albian
unconformity (Contours in meters).
91
Data acquisition
The seismic program consisted of 7,700 km line of data containing 525 northeast-southwest-trending
lines acquired in a single survey area located on the edge of the continental shelf and extending into
deep water. Data was recorded using a 2,400m cable and GSI’s 4,000-cu inch air gun source.
3.11. Case 10: Structural and stratigraphic mapping of Emi field, offshore Niger Delta, Nigeria.
Emi field is located in the offshore depobelt (temporally and genetically related families of growth
fault trends or macro-structures) of the Niger Delta and covers an area of 58.24
(fig. 3.8). The
Niger Delta basin is dated as Cenozoic in age and is situated on continental margin of the Gulf of
Guinea in equatorial West Africa. The northern edge of the delta is demarcated by the Cretaceous
Abakaliki anticline, extending further to the southeast as the Afikpo syncline and Calabar flank. To
the northeast of the Niger Delta, is the structurally defined Benue hinge line, while the sediments of
Okitipupa ridge mark the western boundary of the delta (Ugwu and Ezeh., 2012).
The Niger Delta clastic wedge which spans to about 75,000
in southern Nigeria and the gulf of
Guinea, offshore Nigeria, contains reserves of about 34 billion barrels of oil and 93 trillion cubic feet
of gas and results to the 12th largest accumulation of hydrocarbon reserve the world over (Tuttle et al.,
1999).
The deposits of the Niger Delta is divided into three large-scale lithostratigraphic units; the basal
palocene to recent pro-delta facies of the Akata region, the Eocene of recent paralic facies of the
Agbada region and the Oligocene-recent fluvial facies of the Benin formation (Evamy et al., 1978;
Short and Stauble, 1967; Whiteman, 1982)
92
Fig.
3.8. Map of Niger Delta showing the location of the study area (Emi field).
93
In the present work under review, attempts were made to integrate 3D seismic reflection data with
available well logs so as to define the subsurface geometry, stratigraphy and hydrocarbon potential of
Emi field, off-shore Niger Delta. This integration of seismic data with well logs became necessary to
tackle the limitations of well logs in the definition of lateral variation of subsurface parameters. Thus,
the degree of reliability in mapping complex structural and stratigraphic sub-surfaces such as that of
the Niger Delta would be greatly enhanced by combining seismic data with well logs (Barde et al.,
2000; Barde et al., 2002; Adejobi and Olayinka, 1997).
The aims of the study included the characterization of the subsurface geometry and stratigraphy, the
determination of hydrocarbon trapping potential of the field and identification and delineation of
possible hydrocarbon prospects in the field. The composite logs comprised of gamma ray, SP,
resistivities, calliper, sonic and neutron-density logs. Vertical resolution of the physio-chemical
characteristics of the geologic formations of boreholes can be obtained from well logs (Aizebeokhai
and Olayinka, 2011).
Data acquisition
The survey area covered an area of 58.24 sqkm on a scale of 1:250000. Four wells; Emi-1, Emi-3,
Emi-4 and Emi-5 wells were selected for study based on data quality and continuity and depth column
covered by logs. Available data for the study included forty-two 3D seismic sections of which twentyseven of the seismic lines were cross-lines shot parallel to the dip direction and the other fifteen, inlines which were shot parallel to the strike direction. The spacing between the seismic section for both
the in-lines and cross-lines was 400 m. A continuous velocity log of Emi-3 well and composite logs
for six wells and two sidetracks wells which consist of gamma ray, SP, caliper, resistivities and
neutron-density logs were also available for the study.
94
Data processing
Stratigraphic cross-sections were drawn through Emi-3, Emi-1 and Emi-4 wells and Emi-4 and Emi-5
wells to depict the lateral variations of the horizons within the limits of the data provided. Horizons
within the well logs that show hydrocarbon prospect were selected for mapping The selection of
reflection events from the seismic sections was based mainly on amplitude and continuity of such
reflections, especially in areas where there were no well control.
The cross-lines and the in-lines of the migrated seismic sections were tied at their intersection points
and seismic reflection events that show reasonable amplitude and continuity were selected while areas
with closures or misties (attributed to soft shales) were rechecked and analyzed. Major faults were
identified based mainly on break in reflection events or abrupt termination of reflection events and
marked on the cross-lines. The fault traces were posted to the structure contour maps produced.
The thicknesses of these horizon were converted to two-way travel times using the time-depth relation
curve. The thicknesses of these horizon were converted to two-way travel times using the time-depth
relation curve. The faults’ throws and directions were obtained from the seismic sections and
correlated with well logs where there were well controls.
At the end, four horizons, H1, H2, H3 and H4 were mapped and structure contour maps produced for
each of the horizons. To obtain depth information for horizon H4 (the deepest of the horizons
selected), the time-depth relation curve was extrapolated to a two-way time of about 2700 ms.
After digitization of the selected horizons using appropriate scales, a structured contour map was
constructed for each.
The gamma ray, SP and resistivities logs were used in identifying the lithologies (within the Agbada
Formation) penetrated by the wells. The resistivities logs and neutron-density logs were used in
distinguishing between saline-bearing water formations and hydrocarbon pay zones. Consistency was
ensured in all the data sets.
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CHAPTER FOUR
RESULT DISCUSSION
4.1. Results of Case 1
The data acquired as described in the previous chapter, each underwent different acquisition and
processing operations and as such, one would expect different results. This chapter intends to
compare the results obtained in the cases discussed in chapter three.
For profile 1, a constant velocity analysis shows that the best stacking velocity for this profile is in the
order of 11,000m/s. Bearing in mind that the stacking velocities for dipping events depend on the dip
of the reflector, imaging clearly, all dip directions in one location tends to be a difficult task. To
correct for this, a DMO correction was applied to the data set. After which, reflections with
conflicting dips were successfully imaged with stacking velocities in the order of 6,500m/s.
A brute stacked section of the data (fig. 4.1a) before and (fig. 4.1b) after the application of refraction
and residual statics correction as well as time and space-variants band-pass filters can be seen in
figure 4.1. The band-pass filter enabled clearer coherent reflections as seen in the comparison of both
displayed sections. Strong reflectivity can be seen after 2s in (b), dipping northwards between CDPs
1,000 and 1,800 and series of sub-horizontal reflections are imaged between CDPs 90 and 800,
starting at 2.5s. Several clear reflections can be observed within the region but a very interesting
feature is an apparent south dipping that appears to approach the survey very close to the Kristineberg
mine.
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(a)
(b)
Fig. 4.1.A brute stack showing (a) before and (b) after applying refraction and residual statics, and a
time and space-variant band-pass filter. The reflectivity is much more coherent.
For profile 5, figure 4.2 shows (a) an unmigrated and (b) migrated line-drawing of a 4s TWT of
profile 5 after processing. Figure 4.2a shows several reflections most of which can be traced to the
surface of which, most information deduced from it can be misleading. Figure 4.2b shows a better
image of the dip of the reflectivity. For instance, figure 4.2a shows B1 and B2 dipping opposite to B3
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and B4 and another strong event G1 to G2, visible from 1s to about 3.5s, dipping to the north, and
both (i.e. B and G) crossing each other. Figure 4.2b clearly reveals that both reflective packages do
not cross.
Due to the complex geologic setting of the Kristineberg area and the need to analyze the 3D
orientations of reflections observed on stacked section on profile 5, the strike and dip of some of the
reflectors were modeled. Velocity was assumed to be fairly homogenous and the reflector was
allowed to be a plane with any orientation in space instead of occurring in the line of the profile.
4.2. Results for Case 2
Figure 4.3 shows the raw zero offset section of the data acquired in Balaton Lake. Three distinct
reflections can be observed. Also observable is a trapped gas bubble visible at about trace 430.
Figures 4.4a-d shows the same data after (a) band-pass filtering, (b) gain recovery function, (c)
deconvolution (optimum Wiener deconvolution) and (d) phase-shift migration of zero-offset data
based on the work of Gazdag and Sguazzero (1984) have been applied to it. A significant
improvement in data quality can be observed. The reflection tomography approach provided better
control of the configuration of the reflecting interfaces so that all that was done was to invert for
reflector geometries. Figure 4.5 is a tomogram showing the position of the reflectors before (dashed
lines) and after (thick lines) inverting reflection travel-times.
The final tomographic solution suggests a shallower reinstatement of the initial interfaces, especially
the first and second interfaces, as described by a flat reflector at depths 3-9m and 8m respectively.
The third reflector is a dipping interface with depth starting from 14m to 9m at the end of the
tomogram.
98
Fig.4.2. Line drawing of the stacked section along Profile 5 (a) before and (b) after migration and
depth conversions. B1 to K1 denote reflective segments.
99
Fig. 4.3. Raw zero offset section (512 traces with 25cm trace spacing) containing three reflectors
acquired in Balaton Lake.
4.3. Results for Case 3
Comparing the stacked seismic sections for the COCORP Williston basin survey, that is, the high fold
(30-fold) vibroseis data with the nominally low fold (6-fold) sections from the explosive data (fig.4.6)
reveals the superiority of the vibroseis data in imaging the shallow sections (0-5s) over the explosive
data. The vibroseis data, due to its high redundancy and more uniform source-to-group coupling,
facilitated static corrections, enhanced stacking and provided adequate energy penetration to at least
moho travel times in the thick cratonic crust.
However, explosive data was observed to provide a more superior imaging of the lower crust on a
shot-to-shot basis with amplitudes above ambient noise levels to much greater travel-times. As
such, the explosive source has a higher penetration power through the sediments of the lower crust
than the vibroseis. Nevertheless, size of charge proved to be irrelevant in the imaging of the lower
and upper crust as a single , well-placed, moderately sized (30kg) charge was as good in penetrating
to mantle travel times as larger (90kg) charges.
100
(a)
(c)
(b)
(d)
Fig.4.4. The same data in figure 4.3 after (a) band-pass filtering, (b) gain recovery function, (c)
deconvolution (optimum Wiener deconvolution) and (d) phase-shift migration of zero-offset data.
This can be seen in the relative lack of amplitude variation with increasing or varying charge size for
travel-times beyond 35s in the region.
101
A technical comparison between both surveys, confirmed that there is no distinct moho underlying
the Trans-Hudson Orogen in the region. The relative lack of amplitude variation with increasing or
varying charge size for travel times beyond 35s in the region suggests that moderately sized sources
were as effective as larger sized ones.
Fig.4.5. Reconstructed reflectors after inverting reflection times and appropriate regularization
factors have been applied. Dashed lines represent initial reflectors and thick continuous lines
represent the final inverted geometry.
Further deductions from the comparison of both sources showed that low-fold source-to-ground
coupling, large offsets spanned by CDP gathers (30km versus 15km for vibroseis), ray-path
distortions across large spread and large distances between explosive shots and source-to-source
variability could be problematic to explosive data acquisition.
It was also confirmed that there is the presence of a dipping reflection fabric in the upper mantle
along the western flank of the Trans-Hudson Orogen buried beneath the Williston basin.
102
Fig. 4.6 A stacked section of data obtained from lines (a) MT10(vibroseis) and (b) MT12 (explosive)
imaging the same unmigrated sub-surface features through 30s TWT. (c) is still MT12 imaging the
upper mantle features.
4.4. Results of Case 4
Correlated reflections from the Mississippian bedrock with profiles from the seismic data
corresponded consistently with each other. Bedrock depth variation was observed to be 3-33m which
was the reason for the limited bore-holes in the area. Pennsylvanian karst-collapse features
(paleosinkholes) were seen as structural lows and appeared elongated, bounded by fault scarps. They
were developed along solution widened fractures and were denoted as high risk zones due to mining
103
activities performed on the area. Low risk areas are those areas that were structurally elevated and
were less likely to be mining sites.
The GPS profiles crossed undisturbed clay-rich residual soil overlain by a thin veneer of chat. The
chat/soil contact was easily identified and correlated across the GPR profile. Those profiles imaged
in-fill mine access shafts. The result suggested that materials had been displaced possibly by a
bulldozer to fill local drive way surfaces, as much of the chat had been illegally gathered away. The
chat/soil interface is regular in character and continuous except where the GPR profile crosses the
mine access or ventilation shaft.
4.5. Results of Case 5
The USGS at the time, lacked software packages to interpolate automatically and smoothen all the
average velocity analyses generated in the ANWR seismic re-processing into a 3D model. If it
weren’t so, an improved second order anomaly in the velocity model would have been obtained.
However, only one 3D velocity model for the top of the pre-Mississippian meta-sedimentary rock
horizon could be obtained in the survey.
The re-processing of the seismic data derived in 1984/85 for the 1998 assessment in 1996/97 was able
to improve significantly the temporal resolution of the data over the area. Consistent wavelets were
generated through the wavelet processing which merged satisfactorily to improve data quality. By
merging appropriate lines also, the edge effect of the migration and velocity field was reduced. This
also improved image quality of the data.
4.6. Results for Case 6
The results obtained showed the same basic reflection as in the initial survey although the image
clarity differed as a result of the different sources used, different ground conditions and differences in
depth to A/E. The very dry ground in the latter survey resulted in the less effective power of both the
104
hammer and Buffalo gun sources at higher frequencies when compared to the former, thus, reducing
energy seen at depth and consequently, their reflection amplitude.
Data from both surveys were observed to tie well in areas where the depth to A/E is similar and show
poor events in areas where the depth to A/E is at 50% of its depth at the other side.
The very shallow parts of the data was degraded by noise generated at the shot-point and so produced
fewer usable traces. Though degraded in quality, the obtained sections reproduced the desired crosssectional information which includes the A/E interface, intra-Eagle Ford reflection, the base of the
Eagle Ford and the strong deeper reflections from below the Eagle Ford.
The processing operation was able to remove noise, especially surface waves, increase data resolution
and adjust for topographic and irregular weathering-related time shifts. It was also able to combine
separate results which increased signal levels.
The interpretation was based on the continuity of all cross-sectional information and how they tie to
sonic logs and shallow bore-hole data along the line. By following the continuity of a specific
reflection, the dip changes and/or fault offset of that bed can be determined.
The data was structurally interpreted to have significant disruptions in a few zones along the line.
Three zones of possible faulting were interpreted. These zones were generally grabens or normal
faults with displacements of about 20ft. Those fault features were fairly distinct and separate. There
was no clear tie between faults at depth with shallower faults which cut the Austin.
4.7. Results for Case 7
An obvious improvement was observed on the stacked section, the migrated section and the stacks on
which the enhanced frequency technique was applied, with the latest showing better reflection
continuity and resolution. The migration process significantly reduced scattering of reflections.
105
Higher frequencies were observed in the explosive data even though the vibroseis sweep was limited
to 56Hz. However, the vibroseis data contains less significant gaps which were more obvious in the
explosive data.
Both the explosive and vibroseis lines suggested good resolution of reflections. While the vibroseis
data displayed good resolution from approximately 200ms (depth of 500-600ft), the explosive data
showed good resolution at about 100ms (minimum of 400ft).
On several portions of the explosive line, observed anomalous features suggested potential buried
channel-like sequences, faulting and/or fracturing as well as several potential collapse structures.
While on the vibroseis line, anomalous features were detected as well as potential collapse structures.
4.8. Results for Case 8
Feathering or cross-drift of streamer and sources which arose mainly due to currents and changes in
vessel track was the major problem encountered in the one-boat baseline survey which necessitated
the use of two boats to curb the effect. The latter survey significantly suppressed that effect.
A good positional repeatability in the monitor survey with a mean
error of 27m at the
target offset of 1900m was achieved with the two-boat operation. Desired far offsets were accurately
targeted by decoupling the sources and receivers unlike in the one-boat case where good repeatability
is assured at near offsets if the vessel is steered to the source track and far offsets are reasonably
affected by feather mismatch. However, the receiver motion reduced the repeatability of the two-boat
monitor survey because it moved against the water current during the recording of each shot.
4.9. Results for Case 9
The 3D data obtained was processed by the GSI and migrated in 3D. Bulk of the mapping was based
on a combined interpretation of vertical sections and time slices. The latter was most useful in areas
with poor lateral resolution. Figure 4.7 is a time slice well beneath the Albian unconformity. The red
bands mark the traces of major faults whose individual seismic characters can be identified. A close
106
look at the top part of the figure however, indicates a zone of poor data quality resulting to fault traces
not being adequately mapped. In such cases, closely spaced (60m) vertical sections were used to
define fault traces. They were also used to correlate weak or complex reflections and to map smaller
depositional units. An improved velocity derived from the closely spaced velocity analysis was used
to prepare depth maps at the reservoir level.
Data obtained from the 3D survey when compared to the previous 2D survey done on the same area,
aided interpretation and mapping by providing sharper definition of structural features, showing more
reliable correlation of horizons and fault traces between closely spaced tracks. It also aided in the
preparation of detailed time contour maps from time-slice sections and an improved velocity depth
model for depth conversion. In defining structural features, a comparison can be made of a migrated
section of the 2D and 3D data. Figure 4.8 shows the two versions of line 358 which crosses a feature
near A-4X. Although at about time 2.7s, an evidence of anomaly can be seen at the centre of the
figure but the feature itself is not clear. On the same data after 3D migration (right), a more improved
detail is observed. It shows a steeply-dipping intra-Albian reflection cutting through a flat spot which
is close to a fluid contact defined in well A-4X.
4.10. Results of case 10
The stratigraphic cross-section through Emi-4 and Emi-5 wells tended to be more horizontal and
parallel to one another than those of the cross-section through Emi-4, Emi-1 and Emi-3. This is
because Emi-4 and Emi-5 are more or less in the same strike direction compared to Emi-4, Emi-1 and
Emi-3.
107
Fig.4.7. In-line 525, crossing A-1X well location, Espoir field, and showing clear definition of rotated
fault blocks beneath Albian unconformity.
108
The identification of lithofacies on the seismic sections was based primarily on the amplitude of
seismicreflection events. The high amplitude and continuous reflection were found to correspond to
sand units, whereas the low amplitude reflections were found to correspond to shale facies. Poor
continuity and relatively low reflections were attributed to shaly sand or sandy shale units.
Primarily, twenty seven (27) sand and shale units of varied thicknesses were observed. Most of the
units are laterally continuous, though few cases of pitch-out/wedge-out are evident.
Variations were observed in the thicknesses of horizons especially in Emi-1 where the reservoir units
were thickening and the shale facies, thinning. The variations observed in lithofacies thicknesses
could be attributed to variation in sediment supply, rate of sea level rise and fall, paleogeomorlogy,
synsedimentary tectonism or error in data processing.
Cross-sections through its structure maps also suggest a system of growth faults, roll over anticlines
and folding. Most of the major faults picked on the seismic records were counter regional growth
faults which are characteristics of the self edge, offshore Niger Delta (Ojo, 1996). These counter
regional faults trended west-east and dipped south-east.
Two regional growth faults were picked which had throws ranging from 46 to 79 m (150 to 260 ft) for
the major faults, while that of the minor faults ranged from 24 to 36 m (80 to 120 ft). This throw is
appreciable and could have produce migration pathway for hydrocarbon.
The structure contour maps of the horizons mapped, H1, H2, H3 and H4. The horizons mapped are all
within the Agbada formation, where most of the hydrocarbon is believed to be trapped in the Niger
Delta. The contour maps showed a system of rollover anticlines associated with growth faults. The
faults appeared crescent-shaped with the concave side being towards the down-thrown block.
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Fig.4.8. Comparison of 2-D migration and 3-Dmigration sections across structure drilled by well A4X showing improved definition of erosional high on Albian unconformity and fluid contact (flat
spot).
Faulted and folded anticlinal closures were common structures observed in all horizons. These
closures serve as good traps for hydrocarbon and are therefore possible hydrocarbon prospects. Apart
from delineating the structural traps, other stratigraphic features such as pinch-outs, unconformities,
sand lenses and channels were also suspected.
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CHAPTER FIVE
DISCUSSIONS AND CONCLUSION
5.1. Discussion and conclusions
The successful interpretation of a seismic data, be it 2-D/3-D or land/marine, depends on proper
survey design, acquisition and processing, guarding against pit-falls and optimal utilization of
available technology. The cases reviewed in this work each had different objectives and as a result,
underwent different processing approaches.
This review has been able to demonstrate the usefulness and limitations of certain approaches to
seismic operations. It has also been able to establish the fact that seismic reflection method remains a
very vital and indispensable tool in the analysis and structural mapping of the sub-surface.
Today, more and more advancements have been recorded in the exploration industry, such
advancements include 4-D seismic survey (time-lapse), multi-streamer and multi vessel survey for
marine operations and enhanced oil recovery (EOR) which are current trends in seismic survey and
are gaining popularity in the area of reservoir evaluation, prediction of future productions and
hydrocarbon recovery. There is today an increasing interest in the other forms of energy such as Swaves that arrive at the surface spread other than P-waves which are only part of the wave field.
These other forms of energy may be mode converted from a down-going impulsive source or may
arise from a shear wave source capable of outputting both compressional and shear waves chances
are that more developments will be achieved having better of these waves.
Technological advancements which have also richly enhanced interactive software allow users to
iterate parameters and view effects on screen almost immediately in animated form. Such software
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have been integrated with measurements, analyses, interpretation and reservoir properties to help the
interpreters work better.
The reviewed cases have been able to demonstrate that it is possible to determine the strike and dip of
reflectors using the crooked line geometry.
It was also able to prove that the tomographic approach is capable of providing depth models even in
the absence of bore-hole control and velocity analysis. On the other hand, the tomographic approach
was not able to detect and define the biogenic gas bubbles present in the raw seismic data even though
it was able to locate the anomalous points. This was due to the relatively large wavelength used.
Another reviewed case of the Niger Delta of Nigeria was able to demonstrate that the integration of
seismic data and well logs is a useful and valid tool in structural and stratigraphic mapping and in
better definition of the subsurface geometry and hydrocarbon trapping potential.
The usefulness and limitations of vibroseis and explosive sources for deep crustal and upper mantle
exploration was also revealed in the profiling of the Williston basin. The vibroseis data produced a
more uniform source to group coupling and also a better image of the upper crust while the explosive
data, due to its high penetration ability gave a better image of the lower crust.
It also established the fact that reflection seismic survey can be used to map bedrocks and bedrock
related features such as faults and paleosinkholes and also to map boundaries of previous mine sites.
A knowledge of faults and shear zones, sinkholes etc, helped avert potential highway and/or
construction hazards and aided route planning.
The re-processing of the seismic data derived in 1984/85 for the 1998 assessment in 1996/97, was
able to improve significantly the temporal resolution of the data over the area. This goes to buttress
the improvements recorded in the industry as a result of technological advancements. Today, we have
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even more improved software some of which include Petrel,
,
,
,
to mention but a few.
We also re-established the fact that data from a small source (sledge hammer) could also be used to
image the upper crust with as much clarity as that of a large source if there is proper coupling of the
source to the ground and adequate processing sequences applied.
The use of modern software and hardware in data processing as elaborated in the seismic processing
and velocity assessments in the oil and gas resource potential of the 1002 area emphasizes on better
processing operations using more modern equipment. Much emphasis was placed in the ASR regional
study case on XFreq, a modern software (at that time), which boosted amplitudes of the higher
frequency range and thus produced a more enhanced frequency content of the data.
It also shows that explosive data have higher frequency content than vibratory data and as such would
image deeper layers better.
For marine operations, multi-streamer surveys curb such limitations such as feathering which was
inherent in the reviewed one boat survey, provides a good positional repeatability and allows better
coverage of the survey area.
The superiority of 3D over 2D survey was also demonstrated in this review. The 3D survey data
brought about a better definition of pre-unconformity reflections which resulted in better mapping of
the intra-Albian horizons and better correlations across major faults. Such improved mappings aided
in the identification of additional wells.
5.2. RECOMMENDATION
In line with the conclusions reached from the reviewed seismic reflection operations, we hereby
recommend that adequate caution should be taken against pitfalls and errors especially during the
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acquisition and processing of seismic data, since any error in either regard will be quite misleading
during interpretation.
We also recommend that acquired seismic data should be integrated with available well logs to
provide a more reliable and better definition of the sub-surface.
We were able to see that dynamite sources provided data which gave a better definition of the deeper
crust than vibroseis and other sources and as such, we recommend a well uniform source-to-ground
coupled dynamite source for deep crustal surveys and a well impacted uniform vibroseis or any other
(boomer, hammer etc) source for shallow surveys.
To curb the effect of feathering and poor positional repeatability in marine surveys, multi-streamer
vessel survey is recommended.
In seismic data acquisition and processing, we recommend the use of more modern equipment
(hardware and software).
Lastly, for a more intensive and more detailed stratigraphic and structural definition of the earth crust
be it land or marine, we recommend a 3D survey over its 2D counterpart.
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