Thesis: Geophysical Investigation of Carrizo Formation by Using Two- Dimensional Seismic Surveys in the Tullos-Urania Oilfield in LaSalle Parish, LA

Geophysical Investigation of Carrizo Formation by Using Two-Dimensional Seismic
Surveys in the Tullos-Urania Oilfield in LaSalle Parish, LA
A Thesis
Presented to
The Graduate Faculty of
The University of Louisiana at Lafayette
In Partial Fulfillment of the
Requirements for the Degree
Master of Science
Zachary T. Ghalayini
Fall 2018

© Zachary T. Ghalayini
2018
All Rights Reserved

Geophysical Investigation of Carrizo Formation by Using Two-Dimensional Seismic
Surveys in the Tullos-Urania Oilfield in LaSalle Parish, LA
Zachary T. Ghalayini
APPROVED:
_________________________ ___________________________
Rui Zhang Gary L. Kinsland
Assistant Professor of Geology Professor of Geology
_________________________ ___________________________
Raphael Gottardi Mary Farmer Kaiser
Associate Professor of Geology Dean, Graduate School

iv
Acknowledgments
Many groups and individuals have contributed directly and indirectly to the
research and preparation of this report. To begin with, I owe special thanks to Gary
Kinsland and Raphael Gottardi, from whom I have learned a great deal during the
development of this thesis, asking many questions and consuming a great deal of
their time, patience, and technical ability. Further thanks go to Mark King, from King
Drilling, for sponsoring the experimental seismic survey for this project. I would also
like to thank Dave Newman and Doug Temple from United Service Alliance for
providing the weight-drop source for this project. Also, my humble appreciation goes
to Anoop William, Jiannan Wang, Christopher Lovely, Andrea Paris, Eliene Silva and
Lingfei Mao from the University of Houston and Nathan Quick from the University of
Louisiana at Lafayette and Patrick Hart from the USGS marine geophysical program
for their invaluable input and support.
I am deeply grateful to Patrick Hart and Ray Sliter from the Pacific Coastal
and Marine Science center for their aid in processing the seismic line, and their
patience with my many questions and long WebEx calls.
Last but not least, I would like to thank Dr. Rui Zhang, my thesis advisor, from
whom I have learned a great deal during the completion of this thesis, asking many
questions that involved his time, patience, and technical ability. As an ultimate point,
it would be remiss of me not to pass my sincere appreciation and gratitude to my
family. Thank you, Dad, Mom, and my loving wife Erin for your constant love and
support, especially during this last year.

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Table of Contents
Acknowledgments ...................................................................................................iv
List of Tables...........................................................................................................vii
List of Figures ........................................................................................................ viii
List of Abbreviations ..............................................................................................xii
Chapter I: Introduction & Background.................................................................. 1
Goals and Objectives.................................................................................... 4
Geological Setting......................................................................................... 6
Chapter II: Method and Procedures ..................................................................... 12
Acquisition Equipment and Parameters ................................................... 12
Software Used ............................................................................................. 15
Chapter III: Seismic Wave Attenuation ................................................................ 16
Seismic Waves ............................................................................................ 16
Chapter IV: Seismic Data Acquisition.................................................................. 18
Introduction ................................................................................................. 18
Energy Source............................................................................................. 19
Chapter V: Data Processing.................................................................................. 20
Seismic Data Analysis Workflow ............................................................... 20
Pre-processing. ................................................................................ 20
Input raw data. ....................................................................... 21
Geometry................................................................................ 22
Trace editing...................................................................................... 23
Time-variant frequency filtering....................................................... 24
Surgical mute.................................................................................... 27
FK filter.............................................................................................. 27
Velocity analysis............................................................................... 30
Normal moveout analysis. .................................................... 31
Constant velocity stacks....................................................... 33
Semblance Analysis.............................................................. 34
Post-stack data processing............................................................. 39
Trim statics............................................................................. 39
Deconvolution........................................................................ 43
Migration and imaging. ......................................................... 49
Depth conversion. ................................................................. 51
Chapter VI: Reservoir Imaging.............................................................................. 52
Seismic Well Tie.......................................................................................... 52
Seismic Attribute Analysis......................................................................... 54

vi
Final Interpretation...................................................................................... 54
Chapter VII: Conclusions ...................................................................................... 58
References ............................................................................................................. 60
Abstract ……………………………………………………………………………………63
Biographical Sketch .............................................................................................. 65

vii
List of Tables
Table 1: Vertical Seismic Resolution......................................................................... 5
Table 2: Summary of Seismic Acquisition Parameters............................................ 13
Table 3: Time-variant filter parameters.................................................................... 27

viii
List of Figures
Figure 1: Previous regional and sub-regional CBNG Projects (modified after
Egedahl, 2012)................................................................................................ 2
Figure 2: (a) Vertical seismic section taken from the 3D seismic volume was
provided to the university by Devon Energy. It illustrates the seismic well tie to
the seismic b) Location of A-A’ (Egedahl, 2012) ............................................. 3
Figure 3: Stratigraphic Column of the critical Formations in the Tullos-Urania
Oilfield, the target interval of the survey the Carrizo Formation marked by the
gold star (modified after Schneider, 1929) ...................................................... 7
Figure 4: Type log from well (WSN) 19, with local stratigraphic nomenclature. This
well had gas production associated within the Cruze Sandstone, shown by
the red flag in the resistivity log. (altered after Quick, 2018) ........................... 8
Figure 5: Geologic Structure of the Tullos-Urania field, depicting the contours on top
of the producing Carrizo Sand with CI: 10 feet (modified after Schneider,
1929). The red line in the image is the outline of the seismic survey with the
associated geographic coordinates for the endpoints. .................................. 10
Figure 6: Location of seismic survey line with associated wells, imaged with ESRI
ArcGIS to display terrain and proximity to nearby oil wells............................ 12
Figure 7: Images from the field, depicting the acquisition hardware. The left image
illustrates the geophone setup with the recording direction perpendicular to
the line. The right image shows the shot source, which was a truck-mounted
200 lb. nitrogen accelerated weight drop. ..................................................... 14
Figure 8: Comparison of recent data (a) to existing nearby seismic data (b). (a)
Produced a much clearer, high-resolution image of the target zone. ............ 15
Figure 9: Processing workflow block diagram......................................................... 20
Figure 10: Raw shot gather with several seismic arrivals interpreted, the target
interval is highlighted in yellow...................................................................... 22
Figure 11: Fold and Shot – Receiver Geometry. The color scale below is illustrating
trace count density along the offset from low density in blue to high density in
red................................................................................................................. 23
Figure 12: The image on the left depicts the raw gather with the noisy channels
(#52, #79) producing constant noise, and the figure on the right depicts the
edited gather following the muting of the two traces ..................................... 24

ix
Figure 13: A raw field record and its band-pass filtered versions. Note that the larger
reflection amplitudes are confined to the shallower times at increasingly high-
frequency bands............................................................................................ 25
Figure 14: A raw field record with a time-varying frequency filter applied ............... 26
Figure 15: The panels show the same raw gathers with different surgical mutes
applied. Qualitatively analyzing these panels lead to a 300 m/s mute being
chosen to increase the signal to noise ratio. This is necessary because the
amplitude of the ground role is ~50 db > than the reflected signal. Even
mitigating most of the ground roll still swamps the signal. This becomes very
important later in the velocity analysis........................................................... 27
Figure 16: (a) Composite field record plotted in both the T-X domain and the f-k
domain, (modified from Yilmaz, 2001). A=ground roll, B- a backscattered
component of A, C= dispersive guided waves, D=primary reflection. (b) The f-
k spectrum of this field record. (c) The f-k spectrum of the field record after
rejecting ground roll energy A. Compare this with the f-k spectrum (b) of the
original record. (d) Dip-filtered field record whose f-k spectrum is shown in (c).
Compare this with the original in (a)............................................................. 29
Figure 17: Application of noise attenuation through an iterative process of several
FK-filters. (a) displays the raw common midpoint gather, (b) displays the
filtered data with much of the ground roll noise removed.............................. 30
Figure 18: NMO correction (equation 5.3) involves mapping nonzero-offset travel
time t onto zero-offset travel time t0. (a) Before and (b) after NMO correction.
(Yilmaz, 1987).............................................................................................. 32
Figure 19: Computational NMO correction depiction. For a given integer value for t0,
and velocity v and offset x, use equation (5.3). The amplitude at the time t
denoted by A does not necessarily fall upon the integer sample location By
using two samples on each side of t (designed by solid dots), we can
interpolate the amplitude value in t between the four amplitude values. This
amplitude value then is mapped onto output integer sample t0 denoted by A′
at the corresponding offset. (Yilmaz, 1987)................................................... 33
Figure 20: Constant velocity moveout corrections applied to all common midpoint
gathers (500 to 2000 m/s) ............................................................................. 34
Figure 21: Semblance analysis using a normal moveout hyperbola along a common
offset “super gather” (summed CDP gathers 77-83). Velocity spectrum is
shown on the right......................................................................................... 35
Figure 22: RMS Velocity field over the length of the seismic line under
consideration. This isovelocity contour was derived using the root-mean-
squared (RMS) velocity picks from the spectra in Figure 20. This figure
conveys an unrefined structural trend in the subsurface ............................... 36

x
Figure 23: The Figure above illustrates a raw CDP stack without any noise
processing for comparison to the proceeding two stacks.............................. 37
Figure 24: Cleaned up, CDP stacks created with the velocity model built from the
semblance analysis. The surgical mute stack is on the right, and the time-
varying frequency filter stack is on the left..................................................... 38
Figure 25: Zoomed in cross-sections of the stack built from the surgical mute (right)
and the stack built from the time-varying frequency filter on the left. Note the
preservation of lower reflections on the left that get muted out by the invasive
mute process on the right.............................................................................. 39
Figure 26: Two CDP stacks illustrate the effects of the statics scalar corrections
upon the reflections. The stack without residual statics corrections (b) shows
the false structure and poor coherence near cdps 51-147, Both are eliminated
by correcting for residual statics (a) ............................................................. 42
Figure 27: Two CDP gathers illustrating the effects of the statics scalar corrections
upon the Mid-Carrizo peak event .................................................................. 43
Figure 28: Comparison of CDP stacks with different methods of deconvolution
applied. (a) is the result of a minimum entropy spiking deconvolving algorithm;
(b) is the result of a multichannel predictive operator, and (c) is the result of a
single channel predictive deconvolution algorithm ........................................ 46
Figure 29: Comparison of stacked line with (b) and without a minimum entropy
deconvolution applied (a). The filter used assumes a minimum phase wavelet
and auto-correlates across all of the available traces. Note the compressed
reflectors in the traces in the left panel.......................................................... 47
Figure 30: Amplitude is defined by the grey scale bar beneath each cross section.
Zoomed in image of the minimum entropy deconvolution comparison to
illustrate the resolution differences between the two. The left image has a
broader range of frequencies and thus picks up smaller bed details such as
the pinch out at 0.425 sec ............................................................................. 48
Figure 31: Zoomed in the image of the frequency spectrum differences from before
and after deconvolution to illustrate the resolution differences between the
two. The left image, post deconvolution, has a broader range of frequencies
than before. The frequency spectrum was selected from the green window
with a time range of 0.2 s to 1.0 s and from CDPs 64 to 194 in order to isolate
the Carrizo Formation. .................................................................................. 49
Figure 32: (a) CDP Stack, (b) post-stack finite difference migration of the data in
(a).................................................................................................................. 50
Figure 33: Depth-converted stacked seismic line created by stretching and
squeezing the cross-section to fit the picks from the velocity analysis. The y-

xi
axis is depth in meters. The y-axis begins at ground level and represents true
vertical depth below ground level.................................................................. 51
Figure 34: (a) Illustrates the seismic to well tie with the two wells (SP logs plotted)
that lie along the 2d seismic section. (b) is a zoomed in plot of the seismic to
well tie demonstrating the success in fitting the seismic cross section to the
measured depths of the intervals of interest.................................................. 53
Figure 35: The cross-section represents a calculated amplitude envelope attribute.
The logs inserted here are SP logs to help identify stratigraphic changes.... 54
Figure 36: Depth-converted cross-section and the measured depths of the two wells
along the line................................................................................................. 55
Figure 37: Close-up view of an interpreted version of the acquired seismic line, with
geologic strata interpreted throughout the seismic section............................ 56

xii
List of Abbreviations
AI Acoustic Impedance
D Depth with respect to a datum plane (ft.)
Δt Change in time or time-step (seconds)
λ Lamé strain coefficient [λ = K – 2/3μ]
μ Shear Modulus – stress coefficient [μ = K – 2/3λ]
ρ Mass Density (lb. /ft.)
Λ and μ Lamé parameters
Μ P-wave modulus [M = (K+4/3μ) = (λ + 2μ)]
ms. Milliseconds
K Bulk modulus: Ratio of stress: strain
α P-wave Velocity
β S-wave Velocity

1
Chapter I – Introduction & Background
The University of Louisiana at Lafayette has also been continually active in its
search through the Wilcox Group. (Kinsland et al., 2003) tried to associate basement
structure to depositional trends within the Wilcox by using gravity and magnetic surveys.
Since 2005, over a dozen master’s thesis projects have focused on the Wilcox Group of
northern Louisiana have been completed. These studies found coals on a regional or
sub-regional scale as depicted in Figure 1. Six of these studies apply directly to this
research. (Kull, 2005) developed a “Quick Look” technique for efficiently finding coals on
digitized well logs. Kull’s technique was used in later theses. (Dew, 2007) completed a
regional study of the lower Wilcox coals. Following this, (Ball, 2007) synthesized Dew’s
regional study with several other studies into one continuous regional study over much
of northern Louisiana. (Han, 2010) completed a smaller and more detailed mapping of
individual coals over portions of Winn, Grant, and Natchitoches Parishes, Louisiana.

2
Figure 1: Previous regional and sub-regional CBNG Projects (modified after Egedahl,
2012).
Han’s thesis also included the first two seconds of a 3D seismic volume made
available to the university by Devon Energy. (Egedahl, 2012) used the same seismic
volume (Figure 2) and developed a method of showing facies trends by correlating
coals recognized by well log analysis to a specific seismic signature. The seismic
volume did not image the strata of the upper Wilcox Group with great resolution in
comparison with results accepted by the industry.

3
a)
b)
Figure 2: a) Vertical seismic section taken from the 3D seismic volume was provided to
the university by Devon Energy. It illustrates the seismic well tie to the seismic b)
Location of A-A’ (Egedahl, 2012)

4
(Quick, 2018) worked in conjunction with this study to map portions of the
Tullos-Urania and Olla Fields. Quick also generated and matched a synthetic
seismogram to the processed seismic from this thesis based on resistivity-derived sonic
logs that he modeled successfully.
Goals and Objectives
The primary purpose of this thesis is to capture the Carrizo Formation in Tullos-
Urania Oilfield by processing the seismic data acquired during the summer of 2015.
This survey can serve as a precursor for distinct types of seismic imaging in topics such
as petroleum exploration, coalbed gas exploration, amplitude vs.offset analysis, and
safety risk investigations of potential vertical drilling projects.
“A seismic survey is a program used for geologically mapping structure by
observation of seismic waves. By specifically creating seismic waves with artificial
sources and observing the arrival time of the waves reflected from acoustic impedance
contrasts, or refracted through high-velocity members.” (Sheriff, 2002) An excellent
survey design cost-effectively achieves geophysical objectives, within the given
schedule. Some factors that control the effectiveness of survey design are typically not
constrainable, and therefore each survey will be useful for its primary goal. These
factors include the target depth and size, dip, noise and dominant frequency.
In general, it is difficult to distinguish a thin bed when its thickness is below the
resolution limit. The earth acts as a natural filter and readily removes higher frequencies
more efficiently than the lower frequencies. Because the earth naturally absorbs these
higher frequencies so efficiently, one could argue that we should increase the power of

5
our seismic acquisition sources to overcome high-frequency attenuation. However,
stronger sources tend to produce lower frequencies.
Strata must exceed the minimum tuning characteristics of the survey to image a
seismic cross-section of the Carrizo Sand. This tuning thickness refers to the vertical
and horizontal resolution of the seismic wave. The vertical resolution of a seismic wave
relies on a function of the thickness of the sedimentary bed, the dominant frequency
produced by the source, and the velocity in the sediment. However, it has been proven
by synthetic modeling that λ/4 or 1/4 of the wavelength is the limit of bed tuning
thickness, below which thinner beds cannot be resolved. (Widess, 1973)
λ is calculated as the average seismic velocity divided by the average seismic
frequency or λ= V/f. Therefore, assuming an average velocity of 2,057 m/s and an
dominant frequency of 51 Hz would theoretically allow us to image a sedimentary layer
with greater than 10 m in thickness (Table 1). It is conceivable to detect layers down to
λ/32 however; due to the forces of constructive and deconstructive interference, it
leaves a significant margin for error in phase polarity.
Velocity
2,057
m/s
6,749
ft/s
2,057
m/s
6,749
ft/s
Frequency
Dominant Frequency Maximum Frequency
51 Hz 51 Hz 72 Hz 72 Hz
M Ft. m Ft.
λ/4 10.1 33.1 7.14 23.4
λ/8 5.04 16.54 3.57 11.7
λ/16 2.52 8.27 1.79 5.86
λ/32 1.26 4.14 0.89 2.93
Table 1: Vertical seismic resolution

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Geological Setting
While the Wilcox Group has been extensively explored and developed, there is
still a significant amount of unrecovered reserves. Petroleum systems in the area
include many different structural and stratigraphic traps, and they range in reservoir
quality and potential charge. Much of the historical data on the Wilcox group focuses on
well log correlations and petroleum discoveries.
The up-dip fluvial Wilcox trend, which encompasses the Tullos-Urania oil field, is
a basin-influenced accumulation defined by oil and gas deposits in stratigraphic traps of
interbedded sandstone reservoirs and black shales (even coal beds). The focus of this
study are the productive strata in the Tullos-Urania oilfield concentrated in the in the
middle Wilcox Group, along with the Carrizo Formation.
In the Tullos-Urania oil field, there are three formations of interest (Figure 3). The
youngest of the three is the Sparta Sand. The Sparta Sand is dominantly sandy but has
some clay beds. The base of the Sparta Sand includes, in some areas, 30 feet of
continuous sand, which has yielded showings of oil and gas. The Sparta Sand has an
overall thickness of 400 feet in the field. (Schneider, 1929)

7
Figure 3: Stratigraphic Column of the critical Formations in the Tullos-Urania Oilfield,
the target interval of the survey the Carrizo Formation marked by the gold star (modified
after Schneider, 1929)
The next member is the Cane River Formation. The upper Cane River Formation
is a sandy clay rich in fossils. The Cane River Formation then grades downward to
further reductive fossiliferous marl holding localized deposits of pyrite. The top and
bottom of the Cane River Formation are the easiest distinguishable horizons to find on
resistivity curves. The top of the horizon, a brown clay, contrasts sharply with the
massive Sparta Sand overlying it. The basal horizon of the Cane River Formation
additionally displays a sharp contrast with the underlying Sand of the Wilcox Formation.
The Wilcox Formation is a thick package made up of lenticular beds of sands and
clays, some being lignitic. The lignite members dominate the upper section of the
Wilcox and are easily distinguishable on resistivity logs due to their sharp spikes. The

8
Wilcox Group, within onshore Louisiana, USA has created numerous prolific
hydrocarbon reservoirs in association with either positive structural features or where
paleo-depositional settings were favorable (Tye et al., 1991). The central focus of this
study is the strata within the middle Wilcox Group (Figure 4), as they are the
hydrocarbon-producing reservoirs for a majority of the 46 petroleum fields of LaSalle
Parish, Louisiana (SONRIS); along with the Carrizo Sandstone as it is the producing
formation within this study area, and within 5 of the neighboring fields.
Figure 4: Type log from well (WSN) 19, with local stratigraphic nomenclature. This well
had gas production associated within the Cruze Sandstone, shown by the red flag in the
resistivity log. (altered after Quick, 2018)
The Angelina-Caldwell flexure is a monoclinic structure trending at N. 45°E in the
proximity of the Tullos-Urania oil field. (Figure 5) An area of a marked increase in the
dip of the strata defines the general structure. The beds north of the flexure have a

9
gentle basinward slope, and these beds increase in the dip as much as 180 ft./mile. In
the Tullos-Urania field, the dips of these beds are not as drastic. However, this flexure is
responsible for the structural closure of 50 feet. The post-depositional uplift of these
beds has resulted in a change in the strike.

10
Figure 5: Geologic Structure of the Tullos-Urania field, depicting the contours on top of
the producing Carrizo Sand with CI: 10 feet (modified after Schneider, 1929). The red
line in the image is the outline of the seismic survey with the associated geographic
coordinates for the endpoints.

11
The timing of hydrocarbon migration is unknown, even though the Wilcox oil
reserves are believed to have migrated vertically. The minor structural closure is a result
of salt movement such as the LaSalle Arch. “The LaSalle Arch basement complex is
made up of relict Paleozoic continental crust which was presumably rifted apart during
the Triassic Period. Rifting preferentially occurred to the north of the nose of an eroded
Paleozoic thrust fault. Crustal extension occurred in a north-northeast to south-
southwest direction” (Lawless and Hart, 1990).

12
Chapter II: Method and Procedures
Acquisition Equipment and Parameters
The seismic survey took place between two wells (serial numbers), 119587 and
111872, using a nitrogen-accelerated, 200-pound weight drop mechanism (United
Service Alliance Model A-200). Figure 6 is a satellite image with georeferenced well
and seismic survey locations. The target frequency was 60 Hz, but we were able to
reach a spectrum of 8 Hz to 80 Hz. The survey was a 750-meter long 2D line with 144
geophones installed at 5-meter intervals for recording. Table 2 gives the seismic survey
acquisition parameters.
Figure 6: Location of seismic survey line with associated wells, imaged with ESRI
ArcGIS to display terrain and proximity to nearby oil wells.

13
Parameter Value
2D Line Direction Southeast → Northwest
Length of Profile ~715 m
Source United Service Alliance A-200 nitrogen
Accelerated Weight Drop
Source Type Linear
Source Spacing 5 m
Vertical Stacks Five impacts per shot
Number of Unique Shot points 134
Receiver spacing 5 m
Number of channels 144
Sampling interval 0.001 s
Record Length 4 s
Table 2: Summary of Seismic Acquisition Parameters
Some complications in the acquisition of the data included a low signal-noise-
ratio, constant pump jack noise, and issues with the recording trigger. These noise
complications were muted and filtered out in the processing sequence. Each shot was
recorded for 4 seconds, however; the data quality quickly dissipated below 1.5 seconds
due to the signal-noise ratio. Because of the rugged terrain and isolated location of this
study, the survey was not perfectly straight nor, could all shots be acquired. These
missed shots illustrate as step-outs in the shot and receiver fold geometry diagram.
The acquisition team was able to achieve both the higher frequency and the
impressive penetration depth, as illustrated in Figure 7, because of two factors: (1) The
near-surface sediments at the survey site were lithologically consistent and compact as
they cover most of the upland areas of central and northern Louisiana. Moreover, (2),
the team used a specially engineered nitrogen accelerated weight drop source from

14
United Service Alliance that supplied a sharp, single, high-frequency impact with
minimal “plate and piston bounce.”
Figure 7: Images from the field, depicting the acquisition hardware. The left image
illustrates the geophone setup with the recording direction perpendicular to the line. The
right image shows the shot source, which was a truck-mounted 200 lb. nitrogen
accelerated weight drop.
The results have shown that the survey design and source are capable of
imaging the subsurface in the survey area with better resolution than it was observed in
standard industry data (Figure 8). In perspective, this method should be employed
sooner rather than later because it ends the need for shot hole drilling and shooting.
When compared to conventional industry surface sources, the source used is capable
of more rapid shooting in tighter environments than most. Another benefit is that there is
a minimal environmental impact from the truck-mounted system. These advantages
contribute a significant cost benefit for seismic imaging in the Wilcox Group of central
and northern Louisiana.

15
(a)
(b)
Figure 8: Comparison of recent data (a) to existing nearby seismic data (b).
(a) Produced a much clearer, high-resolution image of the target zone.
Software Used
In processing and imaging the seismic data the following software programs were used:
Hampson Russell
Paradigm ECHOS
Petra
SONRIS
Seisee

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Chapter III: Seismic Wave Attenuation
Seismic Waves
Seismic waves are energy waves traveling through the earth after a given source
of energy is released. There are two distinct types of seismic waves: surface waves and
body waves.
Body waves are waves that penetrate deeply through the interior of the earth.
These waves stand for short pulses of propagating energy. They follow refracted ray
paths decided by the elastic moduli and densities of different regions of the earth's
interior. The two types of body waves generated from a seismic moment are pressure
and shear waves (Lay and Wallace, 1995).
Pressure waves are the fastest moving waves, and they are merely sound
waves. The pressure wave is a longitudinal wave made up of a series of compressions
and rarefactions. This type of wave propagates longitudinally. Therefore, particles in the
earth vibrate back and forth in the axis of propagation, parallel to the wave that is
traveling through it. The speed α at which a P-wave propagates is given by:
α = √ (Λ𝛌𝛌 +2μ)/ρ (3.1)
where α is the P wave velocity,
ρ is the mass density,
λ and μ are the Lame constants,
λ = K – 2/3μ where K is the bulk modulus.

17
Shear waves, unlike primary waves, are transverse waves, so motion is
perpendicular to the direction of wave propagation. These waves travel through the
earth, and the restoring force comes from shear effects. The S wave is a transverse
wave that is polarized in two perpendicular planes, the vertically polarized components,
SV, and the horizontally polarized components, SH. If an S-wave or a P-wave strikes an
interface at an angle other than 90°, a phenomenon known as mode conversion can
occur.
Ρδ2/δt2(δ x μ) = μδ2(δ x μ) (3.2)
The quantity that is propagating is Δµ when the components of this term are
considered. It can be shown that this amount represents a rotational disturbance without
a change in volume. The wave equation that denotes the S wave velocity:
β2 = µ/ρ (3.3)
where β is the shear-wave velocity
μ is the Lame constant,
moreover, ρ is the mass density.
In these equations, β is always smaller than α; this relationship implies S waves
always travel slower than the P waves. Since the shear modulus μ, equals zero in a
fluid, S waves cannot propagate through a fluid.
P and S waves propagate independently. Body waves travel perpendicular to
the wavefront. Seismic energy spreads in the form of small packets of energy pulsating
in the direction of these ray paths for P waves, and perpendicular to the direction of the
ray paths, as it travels through the Earth. The velocity of the wave changes as it
propagates and so the ray paths are bent according to Snell’s Law. (Snell's law - SEG
Wiki, n.d.) This alteration in velocity is because of sharp discontinuities, for example,
changes in lithology. These discontinuities act as interfaces that reflect and refract the
seismic waves like a mirror and a lens.

18
Chapter IV: Seismic Data Acquisition
Introduction
Seismic exploration is necessary to get as accurate an interpretation as possible
of some portions of the earth’s subsurface geologic structure graphically. Many
companies want to evaluate the potential of a petroleum reservoir with the highest
resolution and lowest cost. As a result, seismic data processors need to produce
seismic images according to these companies’ needs.
Due to the wild variations that exist in technology, lithology, and desired goals,
seismic imaging is not always easy. First, a source of controlled seismic energy needs
to be transmitted into the earth. When the seismic energy is reflected, refracted and
diffracted from geologic interfaces underground, it is recorded from the surface. These
reflected, refracted and diffracted waves can help illustrate the depth of interest.
In a perfect situation, seismic waves would travel downward, reflect off a layer of
rock in the subsurface, and return to the surface. However, it is not that simple. It
becomes problematic when seismic waves travel downward, refract along a lithologic
boundary, and return to the receiver, where it can be mistaken for a reflection. Another
example of a processing complication is in situations of diffractions. Diffractions can
occur at sharp discontinuities of a reflecting surface. When the wave front arrives at an
edge (discontinuity), a share of the energy propagates through the higher velocity
region, but much of the energy reflected in the form of a diffraction. With conventional
in-line recording, the migration process can collapse diffractions.
Once the seismic energy returns to the surface, seismometers physically coupled
to the Earth detect the signal and spread out along an array. When seismic data are

19
acquired on land, geophones act in place of seismometers. These geophones measure
the vertical part of the particle velocity (not the propagation velocity) of the returning and
vibrating seismic energy.
Energy Source
Transmitting energy into the earth is the next step in collecting data. There are
many different energy sources, including various explosives, gas or air guns, weight
drop mechanisms, vibrator systems, and even firearms. Different sources have different
applications, advantages, and disadvantages. With land data acquisition, vibrators tend
to be the best choice because of their ability to create a full frequency sweep. However,
they bring in significant expenses and are dangerous to nearby structures.

20
Chapter V: Data Processing
Seismic Data Analysis Workflow
The steps from raw seismic data to an entirely processed image of the
subsurface are outlined in this section. The processing flow included the following steps:
Figure 9: Processing workflow block diagram
Pre-processing. To reduce the cost of seismic exploration when dealing with
multifaceted subsurface conditions, the seismic data processor needs to develop a
processing sequence to reach the highest signal to noise ratio. The initial step is to
make sure the recorded seismic data are set up in the correct imaging sequence. This
imaging sequence requires pre-processing steps such as trace editing to get rid of
harmful, noisy, andor mono-frequent traces, as well as correcting traces with incorrect

21
polarities, trace sorting, assigning geometry and applying statics correction, and, finally,
noise attenuation.
Input raw data. After loading the field tapes (Figure 10) into the processing
computer, the priority is to conduct quality control measures to evaluate the quality and
characteristics of the data. The QC stage addresses several key challenges which may
occur while acquiring seismic data. Some of these challenges include the acquisition
problems and hardware failures which can result in positioning errors and incomplete
datasets, erroneous information in data headers timeliness of delivery of a dataset from
which drilling decisions can be made, as well as specific aspects of the data which may
help refine the processing steps. Seismic data quality checks at the first stage of a
processing project involve checking the survey geometry, data format, and consistency
between different portions of the dataset. These audits are completed by graphing the
survey geometry and shot gathers and calculating the number of bytes within the traces.
Carrizo
Sand
Ground Roll
(~300m/s)
Refracted waves
(~1200 m/s)
Reflections
Noise
Bad trace

22
Figure 10: Raw shot gather with several seismic arrivals interpreted, the target interval is
highlighted in yellow.
Geometry. Seismic data acquisition with multifold coverage is prepared in
source-receiver (s, r) coordinates. Common shot gathers are essential quality
assessment tools in field acquisition. When the traces of the gather originated from a
single shot and many receivers, it is called a shot gather. However, seismic data
processing is traditionally prepared with midpoint-offset (y, h) coordinates. This
transformation is accomplished by sorting the data into common midpoint gathers.
Common midpoint (CMP) gathers are the stereotypical gather: traces are sorted by
surface geometry to approximate a single reflection point in the earth. Based on known
values for the field geometry, each trace is assigned to a midpoint between the shot and
the receiver locations associated with that trace. Data resolution as displayed in Figure
11, is associated with the density of common midpoint traces at a given offset.
Figure 11: Fold and Shot – Receiver Geometry. The color scale below is illustrating
trace count density along the offset from low density in blue to high density in red.
Trace editing. After loading and sorting the traces into CDP gathers, the next
step is to edit faulty traces such as the two shown in Figure 12. In total, three traces,

23
#52, #79 and #97, had to be muted, a process where the actual trace values are
padded with zeroes. It had become clear at this stage that these traces were the result
of 3 separate channels. Because the x-coordinates of the faulty traces never changed, it
had to be due to a physical error in the acquisition stage; the explanation would be
insufficient coupling between the geophones and sediment.
Figure 12: The image on the left depicts the raw gather with the noisy channels (#52,
#79) producing constant noise, and the figure on the right depicts the edited gather
following the muting of the two traces.
Time-variant frequency filtering. The seismic spectrum, particularly the high-
frequency end, is subject to absorption along the propagation path due to the earth's
intrinsic attenuation. Consider the portion of the stacked section and its narrowly defined

24
filtered band-pass panels in Figure 13. A signal is present in 10-to-20, 20-to-40, 30-to-
60 and 40-to-80 Hz bands from beginning to end. Not much signal is noted below 1.0 s
in the 30-to-60 Hz band.
Figure 13: A raw field record and its band-pass filtered versions. Note that the larger
reflection amplitudes are confined to the shallower times at increasingly high-frequency
bands.

25
Figure 14: A raw field record with a time-varying frequency filter applied.
Nevertheless, the signal content appears to be preserved down to 1.0 s with the
40-to-80 Hz band. Finally, the 50-to-100 Hz band shows signal down to 0.75 s. Higher
frequency bands of the useful signal are confined to the shallow part of the section.
Thus, time resolution in the deeper section is drastically reduced. From a practical
perspective, the time-variant nature of the signal bandwidth requires a time-varying

26
application of frequency filters. This excludes the ambient noise that begins to dominate
the signal in late times and obtains a section with a higher signal-to-noise ratio.
Table 3 lists the time-variant filter (TVF) parameters selected from the panels in
Figure 13. In practice, filters are interwoven across adjacent time windows to develop a
smooth transition of passband regions.
Time, ms Filter Band, Hz
0-250 40–80, 320–500
300-800 30–60, 240–400
900-1200 20–40, 160–320
Table 3: Time-variant filter parameters for the data shown in Figure 13.
Surgical mute. Paradigm offers a simple surgical mute program within their
software platform. This program works by defining a fan using a series of offset
(specified by a channel id) vs. time (milliseconds) values which will eliminate (set
amplitude = 0) the traces within. The mutes also included traces recorded at near
offsets to the shots that were subjugated by the ground roll and guided waves. These
near offset kills might lower the amplitudes of shallow reflections after filtering, but they
can significantly decrease noise. Figure 15 shows a series of mute panels with growing
degrees of angle mutes.

27
Figure 15: The panels show the same raw gathers with different surgical mutes applied.
Qualitatively analyzing these panels lead to a 300 m/s mute being chosen to increase
the signal to noise ratio. This is necessary because the amplitude of the ground role is
~50 dB > than the reflected signal. Even mitigating most of the ground roll still swamps
the signal. This becomes very important later in the velocity analysis.
FK filter. An FK filter (where F is Frequency and K is wave number) was applied
to the data to deal with the refracted waves, ground roll, and prevailing noise. FK
filtering involves applying a filter to the events in an FK domain by designing a polygon
shaped fan to reject. Apparent velocity determines the angle of the dip event
propagating across the spread of receivers.

28
𝑉𝑉𝑎𝑎 =
𝑣𝑣
𝑠𝑠𝑠𝑠 𝑠𝑠 (𝛼𝛼)
5.1
Here Va is apparent velocity,
v - Seismic pulse traveling velocity;
α – The angle difference from 90° with which it propagates across
the spread.
Along the orientation of the spread, every single sinusoidal component of the
waveform has an apparent wave number Ka related to its frequency f.
Following these dimensional transforms, the plot of frequency and wave number is a
straight line along the apparent wave number and apparent velocity. Filtering in this
domain can split out a seismic event that is presented as a sloping linear trend of peaks
on the FK spectrum.
𝑉𝑉𝑎𝑎 =
𝑓𝑓
𝐾𝐾𝑎𝑎
5.2
The advantage of filtering in the FK domain is that signal and noise do not
overlap in two dimensions (frequency vs. wavenumber) while they do in t-x (time vs.
offset) domains making multiplicative filtering impossible. Straight dipping events in t-x
domain (time vs. offset) transform to linear events in FK domain. Therefore, events
having different dips between the two plots can be removed by multiplying the FK
transform of the data, with a transform that is zero, between the corresponding dips in
the FK domain and one elsewhere. The ground roll velocity in land acquisition data
have a distinct dip (Figure 16), and the refractions do as well, make them susceptible to
a dip, fan, or pie-slice filter (Hatton, 1986).

29
Figure 16: (a) Composite field record plotted in both the T-X domain and the f-k
domain, (modified from Yilmaz, 2001). A=ground roll, B- a backscattered component of
A, C= dispersive guided waves, D=primary reflection. (b) The f-k spectrum of this field
record. (c) The f-k spectrum of the field record after rejecting ground roll energy A.
Compare this with the f-k spectrum (b) of the original record. (d) Dip-filtered field record
whose f-k spectrum is shown in (c). Compare this with the original in (a).
The seismic event which travels across a spread in the direction from source to
the receiver will plot in the positive wavenumber, and the event traveling towards the
source will plot in the negative wavenumbers. The difference in apparent velocity allows
the unwanted noise to be isolated and removed with a defined reject fan, then inverse
transforming the data back to the t-x domain.
Below is an image of the raw gather (Fig.17a) and the FK filtered data (Fig.17b).
The problem with the FK filter on the data is that it does not entirely remove the ground
roll while smearing the noise across the section. Three different and substantial issues

30
cause the FK filter to fail: 1) the ground roll amplitude is ~2,000 times stronger than the
amplitude of the reflection data, (21 vs. 44,000 unitless magnitudes); 2) the ground roll
events are nonlinear, and 3) there is significant spatial aliasing in the ground roll due to
sparse horizontal trace sampling and its inherent dispersive nature (More off-end shots
would've helped avoid this issue, but it is too late for that).
(a) (b)
Figure 17: Application of noise attenuation through an iterative process of several FK-
filters. (a) displays the raw common midpoint gather, (b) displays the filtered data with
much of the ground roll noise removed
Ground roll’s main characteristics are high amplitudes, low temporal frequencies,
low velocities, and the variation of velocity with the frequency. By qualitatively observing
the results of the F-K filter, the best option moving forward in the processing sequence
was to abandon the F-K filter and proceed with both the surgically muted gathers and
the time-variant filtered gathers.
Velocity analysis.

31
Normal moveout analysis. The most robust and effective way to eliminate
multiples is to stack NMO (Normal Moveout) - corrected seismic gathers (Foster &
Mosher, 1992). After NMO correction multiples can have larger moveouts than
primaries, this is because they are undercorrected and, attenuated during stacking
(Yilmaz, 2001). When stacking is performed on NMO corrected common midpoint
gathers, the primaries are enhanced, because of the superposition of events at the
zero-offset travel time, while the multiples are spread over a range of time to produce
smaller amplitudes. The achievement depends on the moveout differences; they are
smaller at near offsets and larger at far offsets.
The travel time equation as a function of offset is: tx = �t0 +
x2
v2 (5.3)
where t0 is the two-way zero-offset travel time;
x distance (offset) between the source and receiver positions,
v is the velocity of the medium above the reflecting interface.
(Yilmaz, 2001)
From equation (5.3), we see that velocity can be computed when offset x and
two-way times t and t0 are known. Once the NMO velocity is estimated, the travel times
can be corrected to remove the effect of offset as shown in Figure 18. Traces in the
NMO- corrected gather are then summarized to obtain a stacked trace at the common
midpoint location.

32
Figure 18: NMO correction (equation 5.3) involves mapping nonzero-offset travel time t
onto zero-offset travel time t0. (a) Before and (b) after NMO correction. (Yilmaz, 1987)
Figure 19 demonstrates the numerical procedure of hyperbolic movement
correction. The key is finding the amplitude value of A’ on the NMO-corrected gather
from the amplitude value of A on the original common midpoint gather. Given quantities
t0, x, and vNMO calculate t from equation (5.2). The difference between t and t0 gives the
NMO correction:
𝛥𝛥𝛥𝛥𝑁𝑁𝑁𝑁𝑁𝑁 = 𝑡𝑡 − 𝑡𝑡0 (5.4)

33
Figure 19: Computational NMO correction depiction. For a given integer value for 𝒕𝒕𝟎𝟎,
and velocity v and offset x, use equation (5.3). The amplitude at the time t denoted by A
does not necessarily fall upon the integer sample location By using two samples on
each side of t (designed by solid dots), we can interpolate the amplitude value in t
between the four amplitude values. This amplitude value then is mapped onto output
integer sample t0 denoted by A′ at the corresponding offset. (Yilmaz, 1987)
Constant-velocity stacks. Velocity analyses can be made with constant velocity
stacks. By incrementally increasing the velocity model over a series of narrow windows
of data, the result allows the processor to pick the time at which this velocity most
suitably corrects the reflector. The result of this process is a time-variant velocity model
that works quite well.
Figure 20 illustrates this approach. Here, the entire line has been NMO-
corrected and stacked with a range of constant velocity values. The resulting line then
was displayed as a panel, where stacking velocity increases from left to right. Stacking
velocities were then picked directly from the CVS panel by selecting the speed that
results in the best stack response at a particular event time.

34
Figure 20: Constant velocity moveout corrections applied to all common midpoint
gathers (500 to 2000 m/s).
Semblance analysis. The CVS method is particularly useful in complex
structure areas. However, it does not fare well when handling data with a multiple
reflections problem such as ours. (Yilmaz, 1987) Therefore, it would be wise to use the
Carrizo
Sand

35
velocity spectrum method which is based on the cross-correlation of traces in a
common midpoint gather, and not on the lateral continuity of the stacked events.
Multiples and refractions do not travel as deep into the earth as primary waves.
Removing multiples from seismograms has been a long-standing problem for
exploration geophysics since they often destructively interfere with the primary
reflections of interest. In this case, multiples combined with prevalent noise affected the
semblance analysis. Therefore, the velocity picks on the semblance analysis diagram in
Figure 21 were chosen based on the highest peaks of coherence on the gated row plot
rather than the contour plot.
Figure 21: Semblance analysis using a normal moveout hyperbola along a common
offset “super gather” (summed CDP gathers 77-83). Velocity spectrum is shown on the
right.
Since velocity analysis implies a relationship between velocity and depth, interval
velocities can be determined from such analyses. The interval velocity Vi is the average

36
velocity over the interval between two reflecting interfaces. For parallel horizontal
reflectors and horizontally constant velocity surfaces, interval velocity is represented by
the Dix equation. (Nowroozi, 1989)
Vi = �
�VL
2tL−VU
2 tU�
(tL−tU )
(5.5)
VL is the stacking velocity to the Lth reflection,
which has the arrival time tL,
VU and tU are similar terms for a shallower Uth reflection.
Figure 22 below is a velocity field cross section created from the root-mean-
squared velocities chosen in the semblance analysis.
Figure 32: RMS Velocity field over the length of the seismic line under consideration.
This isovelocity contour was derived using the root-mean-squared (RMS) velocity picks
from the spectra in Figure 20. This figure conveys an unrefined structural trend in the
subsurface.

37
Stacking velocity determination often involves significant uncertainty. Interval
velocity calculations involve differences and therefore, have significant uncertainty,
especially if the interval is small.
The image below, Figure 23, illustrates a raw stacked cross-section created with
the velocity model built from the semblance analysis but without any of the noise
removal from the surgical mute or frequency filter.
Figure 23: The Figure above illustrates a raw CDP stack without any noise processing
for comparison to the proceeding two stacks.
Figure 24 illustrates both cleaned up, stacked cross-sections created with the
velocity model built from the semblance analysis. The stack built from the surgical mute
is on the right, and the stack built from the time-varying frequency filter is on the left.
The target interval was the Carrizo sand, which is labeled on the cross-section. Not only
Carrizo
Sand

38
it was imaged successfully, but also resolution allowed the survey to image sediments
down to the Cretaceous (~1220 ms).
Figure 24: Cleaned up, CDP stacks created with the velocity model built from the
semblance analysis. The surgical mute stack is on the right, and the time-varying
frequency filter stack is on the left.
Figure 25 is a zoomed in version of Figure 24 to illustrate the difference
between the two stacks, especially at the edges of the seismic line. The frequency
filtered CDP stack displays preservation of lower reflections on the left that get muted
out by the invasive mute process on the right.
Carrizo
Sand

39
Figure 25: Zoomed in cross-sections of the stack built from the surgical mute (right) and
the stack built from the time-varying frequency filter on the left. Note the preservation of
lower reflections on the left that get muted out by the invasive mute process on the right.
At this point, the time-varied frequency-filtered stack is the best available result
when compared to the raw CDP stack and the muted stack, so the decision was made
to move forward with the time-varying data throughout the post-stack processing.
Post-stack data processing.
Trim statics. Close examination of the velocity spectra shows that some
reflection events are more comfortable to pick than others. Therefore, to improve
stacking quality, residual statics corrections are performed on the moveout-corrected
common midpoint gathers. The material near the surface of the earth is highly variable
both in velocity and thickness and travel times may vary more because of near-surface
variations in the subsurface relief in which we are interested. (Sheriff, 1978)
The properties of this low-velocity near surface layer exert substantial effects on
seismic data. Velocities in this layer may be 300 to 750 m/sec as compared with

40
velocities of 1,500 to 2,500 m/sec below this layer. Therefore, the bottom of this layer is
an essential contact where a substantial change in velocity occurs. The measure of this
contrast is known as the reflection coefficient and is governed by the divergence in the
acoustic impedance of the two adjacent rock masses.
(Z = ρv) (5.6)
Where Z is Acoustic impedance,
ρ is density,
and v is the acoustic velocity of a given rock mass.
There are several methods available to apply static corrections. Some methods
involved in determining statics corrections include uphole data, refraction breaks, and
trial and error of reflection smoothing. The uphole method directly measures the near
surface effects of the weathering layer, but it requires wells drilled and logged both
within the low-velocity zone and beneath it. Since there were no sonic logs recorded
nearby to this survey, the uphole method would have been too unreliable for use in this
investigation. Besides it requires that the seismic source be down in the hole whose
bottom is below the target boundary. (Taner et al., 2007)
After normal moveout corrections (NMO), it will be easy to see any residual 'jitter'
between adjacent traces due to any remaining uncorrected statics errors, because the
NMO correction should make all the reflections horizontal. The remaining uncorrected
statics errors may be due to errors at the shot points and the geophone points.
Observation of this residual ‘jitter’ along the Carrizo horizon led to the idea of
using non-surface consistent trim statics to align the reflections. The module STATICT
computes these corrections by cross-correlating each seismic trace with a pilot trace
within a user-specified time gate. A fixed 500 ms wide gate starting at 400ms. The
program uses TIME as a structural alignment when STATICT generates the pilot traces

41
for cross-correlation, in this case, the target interval is the Carrizo sandstone which was
generally found at 550 ms.
The trim statics can be calculated on the fly within the central processing stream
and are written both to the seismic database for quality control and into the seismic
trace headers for later application with a call to STATIC.
The pilot trace for a CDP gather was constructed from the input data, by stacking
the traces, then smashing with some adjacently stacked gathers, after correcting for
structure, based on the time (in this case) at the center of the gate. The program
defaults to a SMASH of 7, but a value of 10 was used. LIMIT is the greatest allowable
static shift in ms, the value of LIMIT used was 4 ms. The process of arriving at the best
parameters for the line was very much trial and error. The main issue was the
elimination of cycle skips and sudden time shifts in the data.
Calculation and application of trim statics resulted in a significant improvement in
the stack (Figure 26).

42
(a) (b)
Figure 26: Two CDP stacks illustrate the effects of the statics scalar corrections upon
the reflections. The stack without residual statics corrections (b) shows the false
structure and poor coherence near CDPs 51-147, Both are eliminated by correcting for
residual statics (a).
Figure 27 depicts the results of the residual statics corrections; this approach,
the top of the figure illustrates the effects of the statics upon the Carrizo sand. This
graphic displays the scalar corrections needed to adjust the surface to a reasonable
result.

43
(a) (b)
Figure 27: Two CDP gathers illustrating the effects of the statics scalar corrections
upon the Mid-Carrizo peak event
Deconvolution. The common assumption that seismic data holds broadband -
zero phase wavelets is wrong. The majority of mistie problems between seismic data
and synthetic data, seismic data to seismic data collected at separate times, and many
of the misinterpretations based on modeling (AVO, lithology predictions, etc.) are the
result of mixed-phase wavelets remaining in fully processed seismic data.
Mid -
Carrizo

44
The seismic processing procedure designed to convert the field wavelet to the
desired minimum phase wavelet is wavelet deconvolution.
Wavelet deconvolution enhances the vertical resolution of seismic data by
collapsing the basic wavelet into a single spike. In addition to compressing reflections,
wavelet deconvolution can also be used to attenuate ghost arrivals, instrument effects,
reverberations, and multiple reflections. If deconvolution were utterly successful in
compressing the wavelet components and attenuating multiples, it would leave only the
reflectivity of the earth on the seismic trace, but it never is entirely successful in doing
this as it requires that the signal have a frequency spectrum from zero to infinity. In
doing so, the vertical resolution is increased, and earth impulse response or reflectivity
is recovered.
Since deconvolution was performed after stacking the data, an effort was made
to avoid tampering with the relative improved data (Gadallah, 1994). In cases when the
source signature is known, the wavelet deconvolution is considered as a deterministic
problem and it is possible to obtain the inverse filter for the wavelet. However, the
wavelet inside the seismograms, identifying each reflector, is ordinarily unknown. In that
case, the inverse filter is computed in a statistic way, using the Weiner-Levinson (WL)
method (Yilmaz 1987). The mathematical model customarily used to represent the
seismic amplitude, a(t), is referred as the convolutional model where the recorded
seismogram is the result of the convolution of the source signature, p(t). The variable
p(t) represents a seismic pulse or wavelet generated near to the surface, with the
impulse response of the earth, e(t), plus additive noise, n(t). The WL deconvolution
filter, therefore, varies with time owing to factors such as attenuation.

45
a(t) = p(t) ∗ e(t) + n(t). (5.7)
The WL deconvolution works well for wavelets with energy concentrated close to
its time origin (minimum-phase wavelet). WL deconvolution is a statistical filtering
method. It is usually applied to the seismograms to increase the time resolution of the
seismic sessions. The purpose of this method is to compress the form of the seismic
impulse. It is quite useful in showing the high-frequency components of the data, and in
reducing the time correlation and redundancy of the signal along the seismogram. The
WL method consists primarily of calculation of coefficients of the auto-correlation
function of the seismograms; attainment of the inverse filter by solving the normal
equations applying the Levinson recursion (Levinson, 1947), and the convolution of the
seismogram with the inverse WL filter, or merely seismic pulse deconvolution.
In Echos™ the modules DECONA (single channel), MCDECON (multichannel
deconvolution) and DECONQ (minimum entropy deconvolution) are available to provide
deconvolution. MCDECON and DECONA are useful for minimum phase/impulse
sources, and DECONQ is good for non-minimum phase sources. Therefore, the
necessary decision was to test both methods to compare the differences and figure out
how well our source stayed in minimum phase.
For both of optimum Weiner modules (DECONA and MCDECON), two important
variables must be provided. The length of the operator (n) and time lag (α). Time lag (α)
is the time where the first multiple occurs, and n is generously estimated, containing the
source wavelet. They can be computed with autocorrelation of the seismogram.

46
(a) (b) (c)
Figure 28: Comparison of CDP stacks with different methods of deconvolution applied.
(a) is the result of a minimum entropy spiking deconvolving algorithm; (b) is the result of
a multichannel predictive operator, and (c) is the result of a single channel predictive
deconvolution algorithm.
The DECONQ module designs a deconvolution filter from the seismic data using
a minimum entropy filter design algorithm to maximize the spikiness of the deconvolved
trace via iteration (five iterations in this case). The filter design is limited to a seismic
pass band specified by the user, 40 to 320 Hz for this survey. The length of the
deconvolution filter should be about 1.5 times the length of the wavelet, i.e., about 6 ms
for the data, but experimentally 40 ms was found to work better, overall.
Minimum entropy deconvolution proved to be the most successful in collapsing
the source wavelet and in enhancing latent high frequencies in the data.

47
(a) (b)
Figure 29: Comparison of stacked line with (b) and without a minimum entropy
deconvolution applied (a). The filter used assumes a minimum phase wavelet and auto-
correlates across all of the available traces. Note the compressed reflectors in the
traces in the left panel.
Figure 30 shows an example of successful deconvolution for the line. The
source signature has been collapsed, and significantly, higher frequencies (See Fig 30)
are present.
Carrizo
Sand

48
(a) (b)
Figure 30: Amplitude is defined by the grey scale bar beneath each cross section.
Zoomed in image of the minimum entropy deconvolution comparison to illustrate the
resolution differences between the two. The left image has a broader range of
frequencies and thus picks up smaller bed details such as the pinch out at 0.425 sec.

49
(a) (b)
Figure 31: Zoomed in the image of the frequency spectrum differences from before and
after deconvolution to illustrate the resolution differences between the two. The left
image, post deconvolution, has a broader range of frequencies than before. The
frequency spectrum was selected from the green window with a time range of 0.2 s to
1.0 s and from CDPs 64 to 194 in order to isolate the Carrizo Formation.
Migration and imaging. Migration moves dipping reflections to their correct
subsurface positions and collapses diffractions, consequently increasing spatial
resolution and yielding a seismic image of the subsurface. (Yilmaz, 1987) Regardless of
the method, all migration techniques incorporate the imaging principle. (Claerbout,

50
1971) explained the imaging principle as ‘‘reflectors exist at points in the ground where
the first arrival of the downgoing wave is time-coincident with an upgoing wave.’’
Post-stack 2D migration in this project used a finite difference algorithm. Some
significant advantages of finite difference techniques over the other migration methods
are its ability to better handle lateral velocity variations and the associated ray path
bendings at the interfaces. The maximum dip specified was 4 ms per trace. The
steepest dips of the stratigraphy are significantly lower than this. The migration velocity
was scaled to 85% of the calculated stacking (RMS) velocity. Migration significantly
improved the imaging of tight channels (see Figure 32b), where some of the finely
tuned stratigraphic and structural details are beginning to appear.
𝛅𝛅𝟐𝟐 𝐐𝐐
𝛅𝛅𝛅𝛅𝛅𝛅𝛅𝛅
= �
𝐯𝐯 𝟐𝟐
𝟖𝟖
�
𝛅𝛅𝟐𝟐 𝐐𝐐
𝛅𝛅𝐲𝐲 𝟐𝟐
(5.8)
Where Q is the retarded wavefield,
t is the input time,
𝛕𝛕 is the output time,
and y is the midpoint coordinate.
(a) (b)
Figure 32: a) CDP Stack, b) post-stack finite difference migration of the data

51
Depth conversion. The overall process of depth conversion can be defined in
simple terms as the conversion of a time quantity into some logical value of depth.
Figure 33 illustrates the depth-converted stacked section created with the model built
from the velocity analysis. Unfortunately, there were no sonic velocity or density logs
within a near enough vicinity to provide the velocity model some control values.
Figure 33: Depth-converted stacked seismic line created by stretching and squeezing
the cross-section to fit the picks from the velocity analysis. The y-axis is depth in
meters. The y-axis begins at ground level and represents true vertical depth below
ground level.

52
Chapter VI: Reservoir Imaging
Seismic Well Tie
Integration of wireline data and seismic data not only enriches confidence of
seismic interpretation but also can be crucial for seismic data acquisition and
processing. The Wilcox group yielded flat reflections ranging from 500 milliseconds to
1000 milliseconds. The horizon that is the shallowest of the Wilcox is the Carrizo sand,
and it produced a significant Trough-Peak response at 520-545 ms. However, many of
the oil-rich sands of the Wilcox occur in the middle to lower Wilcox at around 750
milliseconds.

53
(a)
(b)
Figure 34: (a) Illustrates the seismic to well tie with the two wells (SP logs plotted) that
lie along the 2d seismic section. (b) is a zoomed in plot of the seismic to well tie
demonstrating the success in fitting the seismic cross section to the measured depths of
the intervals of interest.

54
Seismic Attribute Analysis
Figure 35 illustrates a calculated post-stack seismic attribute with the two wells
tied into the cross-section. This attribute is a phase-independent representation of
amplitude. Amplitude envelope works by summing the magnitudes of all phases of a
trace within a given reflection. This attribute represents the acoustic impedance
contrast, hence reflectivity, which is particularly useful in seismic exploration. Some
examples of uses for the amplitude envelope include bright spots, sequence
boundaries, spatial correlation to porosity, and other lithological variations.
Figure 35: The cross-section represents a calculated amplitude envelope attribute. The
logs inserted here are SP logs to help identify stratigraphic changes.
Final Interpretation
Before the processing, the seismic gathers had concentrated sources of noise
energy, which required filtering. Time-variant frequency filters and deconvolution
decreased the effects of the refractions, ground roll, and random noise. Velocity

55
analysis and statics improved the coherency allowing the interpretations to be analyzed
easier.
Two well logs, strategically placed along the line, were most useful for showing
the formation locations found on the seismic line. These formations include the Sparta
Sand at 440 to 460 ms, the Carrizo at 520-545 ms, the base of the Big Shale at 720 to
740 ms and the Tew Lake at 740 to 760 ms. The line does not portray much structure
due to the minimal relief of the Tullos-Urania oilfield. The seismic image interpreted in
Figure 36 displays a significant amount of energy at 530 meters depth, which places it
in the Carrizo Sand near the Urania #02 well correlating with production of oil from this
interval.
Figure 36: Depth-converted cross-section and the measured depths of the two wells
along the line.

56
The interpretation in Figure 37 shows a slight change in dip, from the base of the
Big Shale downwards from Southeast to Northwest.
Figure 37: Close-up view of an interpreted version of the acquired seismic line, with
geologic strata interpreted throughout the seismic section.
The top of the Carrizo Sand aligns within the base of a ever-present peak event.
The Carrizo Sand seems to shift from a trough to a zero-crossing back to a tough even
along the line. With the geologic context that this area was deposited in a low energy
fluvial environment of the Holly springs deltaic body, it can be deduced that the Sand
body to the Southeast is a point bar or distributary channel buildup that pinches out in
the center of the survey with another sand buildup to the Northwest.
Due to the vertical resolution capabilities of this seismic survey, it is possible to
interpret the base of the Carrizo sandstone and where the Upper Wilcox
(undifferentiated) begins at ~540 ms.

57
The identification of the Base of Big Shale horizon (~710 ms), within the seismic
data, is an essential finding for this study; as it is now possible to identify where the
locally producing strata lie within the seismic section.
The interpretation of the strata within the middle Wilcox Group portrays the
appearance of a hidden structure (~730 ms), not previously correlated by the well logs.
Starting below the regional Tew Lake marker and continuing down, the seismic data
depicts a subtle positive structure with an isolated trough event with a good phase
change below within the central CDP’s of the survey. This is an important finding for this
report because it proves how vital the use of seismic data is while exploring for
petroleum. (Quick, 2018)

58
Chapter VII: Conclusions
The purpose of this investigation was to produce an image of the subsurface and
identify formations of interest for production of oil and gas by applying different
processing methods. With an optimal processing workflow and use of limited well log
control, an interpretation of the data was accomplished to provide to an oil company.
Low-resolution images (Figure 2) of the upper Wilcox group in the past have
been produced from surveys designed for deeper targets or lack of modern processing
technology. Because of the significantly low signal-noise ratio, the most critical
processing steps focused on the signal processing and the velocity analysis.
During the processing phase of this investigation, the author understood the
limitations of land acquisition of seismic data, however, produced an image that proved
to surpass the expectations of the initial goal. Not only was it possible to image the
Carrizo sand, but the data were able to image the entire Wilcox package which is rich in
economic deposits of oil and gas.
Well logs with enough depth and proximity to the investigation were used to pick
the tops of critical formations using knowledge of log mechanics and regional
stratigraphy. While there were two wells along the seismic line, they did not have sonic
velocity or density logs recorded. Therefore, the author worked conjunctively with
another graduate student, Nathan Quick, to use a converted p-wave velocity from the
resistivity values, using the Gaussian equation, to create a synthetic. The top of the Big
Shale, top of the Carrizo Sand and the bottom of the Wilcox group were accurately
picked from synthetics using wavelets extracted from the migrated line and wells.

59
This investigation employed the processing of seismic land data to find what
would be the best processing flow to obtain an interpretable stacked section. Further
research could be taken to include other possibilities for noise suppression, increasing
the accuracy of the well synthetics with the use of sonic logs, and increased attribute
analysis for reservoir characterization. If additional seismic acquisition is to take place in
the area with similar designs, it would be wise to design the field recording geometry to
avoid problems with ground roll.
An easy solution to circumvent the issues with ground roll would be to shoot the
survey in an off-end spread rather than split-spread. By modifying the recording
geometry and shooting off-end rather than split-spread, the ground roll energy a would
occur at much deeper times past a specific offset, and it would not affect the target
reflections. Nevertheless, this research has been more than successful and will serve
as a precursor to the seismic exploration of the Tullos-Urania oilfield in the future and
help design surveys and create interpretations of seismic data.

60
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63
Ghalayini, Zachary. Bachelor of Science, University of South Florida, Fall 2014;
Master of Business Administration Spring 2018; Master of Science, the
University of Louisiana at Lafayette, Fall 2018
Major: Geology
Title of Thesis: Geophysical Investigation of Carrizo Formation by Using Two-
Dimensional Seismic Surveys in the Tullos-Urania Oilfield in LaSalle Parish, LA
Thesis Director: Dr. Rui Zhang
Pages in Thesis: 77; Words in Abstract: 298
Abstract
The upper Wilcox group in the Tullos-Urania oilfield has not been imaged with
enough resolution for interpretation. Prior seismic data collected in the area was
designed for formations much deeper than the Wilcox Group. The purpose of this
investigation was to produce an image of the subsurface and identify formations of
interest for production of oil and gas by applying different processing methods. With an
optimal processing workflow and use of limited well logs, an interpretation of the data
was provided to the oil company.
The advantage of using an accelerated weight-drop source is the shallow
horizons, ranging from 1,500 to 3,000 feet in-depth, become distinct with higher
resolution. The acquisition achieved a dominant frequency averaging around 45-65 Hz
compared to a nearby pre-existing 3D survey volume with a dominant frequency range
of 15-35 Hz. Refracted waves dominated the unprocessed shot records from this data.
Consequently, the field records had a significantly low signal-noise ratio. Therefore, the
most critical processing steps focused on signal processing and velocity analysis.
Without enough ground roll and noise suppression, the velocity analysis would not have
been coherent. Some obstacles faced with processing the data included a sparse
horizontal sampling and a lack of velocity logs along the seismic line.

64
The results of this study included a set of stacked lines, velocity models, and an
optimal processing workflow for future high-frequency shallow seismic exploration
surveys in the vicinity of LaSalle, LA. These results have concluded seismic surveying
with an accelerated weight-drop source is a cost-effective method to produce a high-
resolution cross-section of the high and low-velocity sand and shale channels of the
fluvial Wilcox strata of Northern Louisiana. Further research should look to build on
these results and gather a 3D survey to image the structure of the Tullos-Urania oilfield
and identify hydrocarbons-in-place.

65
Biographical Sketch
Zachary Ghalayini was born March 20, 1993, in Sarasota, Florida. He graduated
from the University of South Florida in 2014 with a Bachelor of Science degree in
geology. Zachary entered the master’s program in geology at UL Lafayette that same
year. His research in that program has centered on geophysical exploration methods for
oil and natural gas deposits. He graduated in the fall of 2018 with a Master of Science
degree with a geology concentration.

Thesis: Geophysical Investigation of Carrizo Formation by Using Two- Dimensional Seismic Surveys in the Tullos-Urania Oilfield in LaSalle Parish, LA

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