Engin Masters Thesis-data Acquisition Techniques
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Copyright
by
Engin Alkan
2007
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MULTI-DIMENSIONAL LAND SEISMIC DATA-ACQUISITION TECHNIQUES
AND RANDOM SURVEY DESIGN
by
Engin Alkan, BSc.
Thesis
Presented to the Faculty of the Graduate School of
the University of Texas at Austin in Partial
Fulfillment of the Requirements for the Degree of
Master of Science in Geological Sciences
The University of Texas at Austin
August 2007
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MULTI-DIMENSIONAL LAND SEISMIC DATA-ACQUISITION TECHNIQUES
AND RANDOM SURVEY DESIGN
APPROVED BY
SUPERVISING COMMITTEE
____________________________________Bob Hardage
Chairperson of Supervisory Committee
____________________________________
Clark Wilson
____________________________________
Robert Tatham
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DEDICATION
To God and my family
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ACKNOWLEDGMENTS
This thesis report is part of ongoing work that many companies are undertaking to
reduce acquisition cost and risk in large land seismic acquisition programs. I would firstly
like to thank God for allowing me the privilege to be a part of the Jackson School of
Geosciences and the Exploration Geophysics Laboratory in the Bureau of Economic
Geology led by Dr. Bob Hardage.
To Dr. Bob Hardage who is really an excellent mentor and coach for giving me
the guidance to develop excellent project and making me feel a part of an active research
group. Special thanks also go out to Dr. Clark Wilson, Dr. Robert Tatham, and Paul
Murray of the Jackson School of Geosciences, and to Steve Jumper, Steve Forsdick, Tom
Thomas, Seth Conway, and all other Dawson Geophysical employees who have
contributed to the research described here.
Dr. Bob Hardage, my supervisor and mentor, ensured that my project was on
schedule and also ensured that I was on the right path to complete the project. Other
special thanks go to The National Oil & Natural Gas Company of Turkey whose financial
support made thesis work possible.
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MULTI-DIMENSIONAL LAND SEISMIC DATA-ACQUISITION TECHNIQUES
AND RANDOM SURVEY DESIGN
by Engin Alkan, MS. Geo. Sci.
The University of Texas at Austin, 2007
SUPERVISOR: Bob Hardage
This study analyzes different techniques and innovations of three-dimensional
seismic data acquisition and survey design. Multi-dimensional (both 2-D and 3-D) survey
design requires objective consideration of survey goals, the range of expected Earth
responses, crew and equipment accessibility, acquisition costs, instrument capabilities,
experimental field conditions, and logistic considerations.
Planning a 3-D survey combines operational and technical issues which, in turn,
depend on acquisition and design parameters. Because seismic source effort, crew and
equipment availability, and size and shape of the survey affect survey cost, it is necessary
to understand how all of these factors individually affect the overall data-acquisition
program. The main goals of this thesis are to analyze the effect of receiving station
coordinate randomness on different 3-D seismic data-acquisition and survey design
characteristics, both operationally and technically, and to ultimately optimize the cost and
data quality of seismic surveys.Many advances have been made in imaging subsurface structures at both shallow
and deep target locations through improved seismic data-acquisition and processing
techniques in the past two decades. Service companies and oil companies continue to
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develop new techniques to create better 3-D images with higher resolution and improved
signal-to-noise ratio.
A major problem that confronts onshore seismic exploration companies is the
effects of the acquisition geometry on recorded data. Receiver and source line spacings,
range of offsets, and azimuths, fold variations, and source-generated noise are all
important issues to consider, as are culture, topography, and surface conditions.
This thesis consists of two parts: (1) station randomness effects on acquisition and
survey parameters, and (2) experimental evaluation of Vibroseis sweep parameters. To
perform the latter analysis, field data acquired across Tohonadla field at Bluff, Utah were
processed, and data generated with various Vibroseis sweep parameters were compared.
These analyses allowed data-acquisition and survey design parameters to be related to the
cost of the survey and to data quality.
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Table of Contents
List of Figures ......................................................................................................... i
Chapter 1: 3D Seismic Data Acquisition and Survey Design Considerations ........1
1.1. Introduction and Background ..................................................................11.2. Why 3-D surveys .....................................................................................2
1.3. 3-D Seismic Survey Design and Special Considerations .......................2
1.3.1. Field Operations and Survey Considerations .............................3
1.3.1.1. Multi-channel Measurements ..........................................7
The Common Midpoint (CMP) Method ...................................9
Swath Shooting Method ............................................................9
1.3.1.2. Survey Considerations ..................................................10
(a) Seismic Resolution ..........................................................10
(b) Signal-to-Noise Ratio .....................................................12
(c) Sampling Rate .................................................................18
Sampling in the time domain ..........................................18
Spatial Sampling .............................................................19
(d) Migration Aperture ..........................................................22
1.4. Summary ................................................................................................26
Chapter 2: Survey Design Parameters and Randomization ..................................27
2.1. Introduction ............................................................................................27
2.2. 3-D Seismic Data Acquisition and Survey Design Parameters ..............27
2.3. Summary ................................................................................................42
Chapter 3: Randomness and Its Effects on Survey Design...................................43
3.1. Introduction ...........................................................................................43
3.2. Acquisition Geometry Effects on Seismic Records ..............................43
3.3. Random Models.....................................................................................48
3.3.1. Random Models Based on Orthogonal Geometry ....................48
3.3.1.1. Randomization Analysis for Template-Shooting ................53
3.3.1.2. Randomization Analysis for Full-Survey Shooting .............73
3.3.2. Randomness Analysis for Converted Waves ............................86
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3.3.2.1. Randomness Analysis for Template Shooting ....................86
3.3.2.2. Randomness Analysis For Full-Survey Shooting .............103
3.4. Error Analysis of Randomization Survey ...........................................114
3.5. Summary..............................................................................................114
Chapter 4: Tohonadla Noise Analysis – Source Effort Test ..............................116
4.1. Major Statistics of Oil Fields in Utah .................................................117
4.2. Geologic Setting of the Osage County Study Area ............................117
4.3. Source Effort Testing ..........................................................................121
Quality Control ................................................................................124
4.4. Summary..............................................................................................140
Chapter 5: Conclusions........................................................................................141
References ...........................................................................................................144
Vita ....................................................................................................................147
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List of Figures
Figure 1.1– The model of 3D survey design and seismic data acquisition used in this
analysis ...............................................................................................4
Figure 1.2 – The algorithm used to design sweep parameters and acquisition
parameters...........................................................................................5
Figure 1.3 – The algorithm of 3D survey design and geometrical parameters .......6
Figure 1.4 – Data sorting techniques and CMP method ........................................ 8
Figure 1.5 – A swath geometry and its internal shot station configuration ......... 10
Figure 1.6 – Horizontal resolution of seismic data based on aperture...................12
Figure 1.7 – Single reflector model for single shot and receiver array..................13
Figure 1.8 – Effect of number of phones on signal wavelength (top), the effect of
distance between phones on signal wavelength (bottom)................ 15
Figure 1.9 – Array response and attenuation ....................................................... 16
Figure 1.10 – Target spectrum of attenuation for receiver array...........................17
Figure 1.11 – Sampling theorem and anti-alias filter ............................................19
Figure 1.12 – Ray propagation model and sampling interval relationship........... 20
Figure 1.13 – Relation between receiver-station interval, geologic dip, velocity, and
frequency for straight ray paths and curved ray paths ..................... 22
Figure 1.14 – Relationship between recoverable dip angles with increasing depth and
aperture limit for straight lines..........................................................24
Figure 1.15 - Geologic dip, frequency, and migration aperture relationship for curved
ray path..............................................................................................25
Figure 2.1 – 3D orthogonal seismic data acquisition survey design sample ........ 31
Figure 2.2 – 3D acquisition and survey design terms........................................... 31
Figure 2.3 – Fold calculation analysis and fold distribution diagram....................32
Figure 2.4 – Comparison of two 3-D recording swaths and patch shooting geometries
...........................................................................................................33
Figure 2.5 – Comparison of stacking folds created by patch-A and patch-B....... 34
Figure 2.6 – 2D and 3D teepees for different amounts of fold distribution.......... 36
Figure 2.7 – A spread sheet used to design a 3-D survey......................................37
Figure 2.8 – Minimum and maximum offsets .......................................................38
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Figure 2.9 – Example of offset distribution plot....................................................40
Figure 2.10 – Example of spider plot showing azimuth distribution.................... 40
Figure 2.11 – Comparison of azimuth and offset distributions created by narrow and
wide-azimuth surveys ...................................................................... 41
Figure 3.1 – Modulation of bin-to-bin amplitude caused by the acquisition geometry
...........................................................................................................44
Figure 3.2 – Effect of surface geology, topography, and cultural constraints on the
survey design and on the fold distribution of the survey..................46
Figure 3.3 – Survey design example having an obstacle in the center of the survey
area................................................................................................... 47
Figure 3.4 – Model parameters and survey statistics............................................ 48
Figure 3.5 – Design of the basic orthogonal model...............................................49
Figure 3.6 – Fold distribution of the base orthogonal survey................................50
Figure 3.7 – Offset distribution of the base orthogonal survey ............................ 51
Figure 3.8 – Azimuth distribution and bin statistic of the base orthogonal survey 52
Figure 3.9 – Design of random model-1 (randomness within one-quarter of a station
interval) .............................................................................................53
Figure 3.10 – 2D and 3D fold distribution plots of random model-1....................54
Figure 3.11 – Offset distribution of random model-1........................................... 55
Figure 3.12 – Azimuth distribution of random model-1....................................... 56
Figure 3.13 – Offset-fold diagrams for (a) orthogonal (b) random model-1.........57
Figure 3.14 – Random model-2 (randomization within half-bin dimension)........58
Figure 3.15 – The 2D and 3D fold distribution plots of random model-2............ 59
Figure 3.16 – Offset distribution of random model-2........................................... 60
Figure 3.17 – Azimuth distribution (color spider plot) of random model-2..........61
Figure 3.18 – Offset-fold diagrams for (a) orthogonal (b) random model-1 ........62
Figure 3.19 – Design of random model-3 (randomization within one-half of a line
interval) ............................................................................................63
Figure 3.20 – 2D and 3D fold distribution plots of random model-3................... 64
Figure 3.21 – Offset distribution of random model-3........................................... 65
Figure 3.22 – Azimuth distribution of random model-3........................................66
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Figure 3.23 – Offset-fold diagrams for (a) orthogonal (b) random model-3.........67
Figure 3.24 – Design of random model-4 (randomization within one-half of a line
interval on receiver stations only).................................................... 68
Figure 3.25 – Acquisition footprints and fold map of random model-4............... 69
Figure 3.26 – Rose diagram of orthogonal model .................................................70
Figure 3.27 – Rose diagram of random model-1 ...................................................71
Figure 3.28 – Rose diagram of random model-2 .................................................. 71
Figure 3.29 – Rose diagram of random model-3 .................................................. 72
Figure 3.30 – Rose diagram of random model-4 ...................................................72
Figure 3.31 – Comparison of orthogonal and the first random designs for full-survey
shooting analysis...............................................................................74
Figure 3.32 – Fold map of orthogonal and the first random designs (randomness
within one-quarter of a station interval)........................................... 75
Figure 3.33 – Comparison of orthogonal and the second random designs for full-
survey shooting analysis .................................................................. 76
Figure 3.34 – Fold map of orthogonal and the second random designs (randomness
within one-half of a station interval).................................................77
Figure 3.35 – Comparison of orthogonal and the third random designs for full-survey
shooting analysis ..............................................................................78
Figure 3.36 – Fold map of orthogonal and the third random designs (randomness
within one-half of a line interval) .....................................................79
Figure 3.37 – Comparison of orthogonal and the fourth random designs for full-
survey shooting analysis .................................................................. 80
Figure 3.38 – Fold map of orthogonal and the fourth random designs (randomness
within one-line interval)................................................................... 81
Figure 3.39 – Comparison of orthogonal and the fifth random designs for full-survey
shooting analysis...............................................................................82
Figure 3.40 – Fold map of orthogonal and random designs ..................................83
Figure 3.41 – Fold map at the center of the survey area (the image area) for
orthogonal and random designs ....................................................... 84
Figure 3.42 – Orthogonal design used for converted-wave analysis.................... 86
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Figure 3.43 – Common midpoint and common-conversion-point imaging ..........87
Figure 3.44 – Fold map of the orthogonal design for converted waves (Vp/Vs=1.75).
...........................................................................................................89
Figure 3.45 – Fold map of the orthogonal design for converted waves (Vp/Vs=2.0).
.......................................................................................................... 90
Figure 3.46 – Fold map of the orthogonal design for converted waves (Vp/Vs=2.25).
.......................................................................................................... 91
Figure 3.47 – Random design used for converted-wave analysis..........................92
Figure 3.48 – Fold map of the random design for converted waves (Vp/Vs=1.75).....
...........................................................................................................94
Figure 3.49 – Fold map of the random design for converted waves (Vp/Vs=2.0)…...
.......................................................................................................... 95
Figure 3.50 – Fold map of the random design for converted waves (Vp/Vs=2.25)…..
.......................................................................................................... 96
Figure 3.51(a) – Bin statistics of orthogonal and random models for converted waves for
Vp/Vs = 1.75.....................................................................................97
Figure 3.51(b) – Bin statistics of orthogonal and random models for converted waves
for Vp/Vs = 2.0. ................................................................................98
Figure 3.51(c) – Bin statistics of orthogonal and random models for converted waves
for Vp/Vs = 2.25. ..............................................................................99
Figure 3.52 – Rose diagrams for orthogonal and random designs for converted waves.
(Vp/Vs=1.75) ..................................................................................100
Figure 3.53 – Rose diagrams for orthogonal and random designs for converted waves.
(Vp/Vs=2.0).… ...............................................................................101
Figure 3.54 – Rose diagrams for orthogonal and random designs for converted waves.
(Vp/Vs=2.25). .................................................................................102
Figure 3.55 – First random survey example for full-survey shooting geometry that
introduces the survey in which receivers and sources were located with a
station randomness of up to one-half of a station interval..............104
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Figure 3.56 – Comparison between fold maps of orthogonal and random (randomness
within one-half station interval) surveys for converted-waves study
(Vp/Vs ratio equal 2.0). ..................................................................105
Figure 3.57 – Effect of randomness on fold distribution at the center of the survey
(image area) area. (Vp/Vs =2.0). ....................................................106
Figure 3.58 – Comparison between fold maps of orthogonal and random (randomness
within one-half station interval) surveys for converted-waves study
(Vp/Vs ratio equal 1.7). ..................................................................107
Figure 3.59 – Effect of randomness on fold distribution at the center of the survey
(image area) area (Vp/Vs =1.7). .....................................................108
Figure 3.60 – First random survey example for full-survey shooting geometry that
introduces the survey in which receivers and sources were located with a
station randomness within one-half of a station interval ................109
Figure 3.61 – Comparison between fold maps of orthogonal and random (randomness
within one station interval) surveys for converted-waves study (Vp/Vs
ratio equal 2.0).. ..............................................................................110
Figure 3.62 – Effect of randomness (randomness within one station interval) on fold
distribution at the center of the survey (image area) area (Vp/Vs =2.0)
.........................................................................................................111
Figure 3.63 – Comparison between fold maps of orthogonal and random (randomness
within one station interval) surveys for converted-waves study (Vp/Vs
ratio equal 1.7).. ..............................................................................112
Figure 3.64 – Effect of randomness (randomness within one station interval) on fold
distribution at the center of the survey (image area) area (Vp/Vs =1.7)
.........................................................................................................113
Figure 4.1 – A view of study area........................................................................116
Figure 4.2a – Location of Tohonadla Oil Field in the U.S ..................................119
Figure 4.2b – Location of Tohonadla Oil Field in the region immediately surrounding
the oil field .....................................................................................119
Figure 4.3 – The area of Tohonadla Oil Field, Bluff, Utah. ................................120
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Figure 4.4 – One of the major oil-producing provinces of Utah and vicinity - Oil and
gas fields in the Paradox Basin of Utah and Colorado. ..................120
Figure 4.5 – 62,000-pound peak-force vibro-truck............................................. 123
Figure 4.6 – Radio telemetry (RSR) system. .......................................................123
Figure 4.7 – GPS device and base station............................................................124
Figure 4.8 – 8-120 Hz, linear sweep, 2 ms taper. ................................................126
Figure 4.9 – 6-120 Hz, nonlinear, 3dB/octave sweep, 2 ms taper. ......................127
Figure 4.10 – Parameter testing. The fifth receiver line was chosen for the noise
study. 144 stations were located along this line..............................128
Figure 4.11 – RAW# shot record: 8-120 Hz, linear, 4 sweeps, 2 vibes...............129
Figure 4.12 – Raw shot records: 8-120 Hz, linear, 4 sweeps, 3 vibes. ................130
Figure 4.13 – Raw shot record: 6-120 Hz, 3dB/octave, 4 sweeps, 2 vibes..........131
Figure 4.14 – Raw shot record: 6-120 Hz, 3dB/octave, 4 sweeps, 3 vibes..........132
Figure 4.15 – Final record sections (left) 2 vibes, linear, 4 sweeps (right) 2 vibes,
3dB/octave, 4 sweeps......................................................................134
Figure 4.16 – Final record sections (left) 3 vibes, linear, 4 sweeps (right) 3 vibes,
3dB/octave, 4 sweeps......................................................................135
Figure 4.17 – Final record sections (left) 3 vibes, linear, 2 sweeps (right) 3 vibes,
linear, 1 sweep ................................................................................136
Figure 4.18 – Final record sections (left) 3 vibes, linear, 2 sweeps (right) 3 vibes,
linear, 3 sweeps...............................................................................137
Figure 4.19 – Final record sections (left) 3 vibes, linear, 3 sweeps (right) 3 vibes,
linear, 4 sweeps...............................................................................138
Figure 4.20 – Migrated section of final record. Final record – 3 vibes linear 2
sweeps….........................................................................................139
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CHAPTER 1
3-D SEISMIC DATA ACQUISITION AND SURVEY DESIGN
CONSIDERATIONS
1.1. Introduction and Background
The history of seismic reflection and refraction survey design began over 80 years
ago, and many salt domes were found by 1930s using the seismic refraction method
(Telford, et al. 1990). Since 1930s, the seismic reflection surveying has been the most
widely used geophysical technique. Beginning in the 1970s, three-dimensional seismic
survey techniques have been applied increasingly in hydrocarbon exploration (Telford, et
al. 1990).
The main objective of the seismic reflection method is to image subsurface
structures by using well-designed data-acquisition geometries, efficient data-processing
techniques, and effective interpretation. It is obvious that the first and main step is to
design an effective survey that will facilitate acquisition of the data with a high signal-to-
noise ratio and with minimal discontinuity and noise contamination in the final image.
Although many advances have been made in the past two decades in seismic data
acquisition and processing techniques that image subsurface structures at a wide range of
depths, every oil company still plans to create new techniques to create even better 3-D
images with higher resolution and improved signal-to-noise ratio. Two-dimensional
seismic data are still used by many companies; two-dimensional data, however, do not
lead to the reduction in risk and exploration success that 3-D data do. Although 3-D
survey management and the cost of 3-D surveys require considerable expense and effort,
and the demands of 3-D data-acquisition and imaging increase day by day, significant
improvements in acquisition techniques still occur.
The main difference between 3-D and 2-D surveys is the detail and reliability of
their images of subsurface structures and the accuracy of their interpretations. Being able
to perform migration in three dimensions eliminates much of the misinterpreted
structures imaged by 2-D data and provides a complete 3-D image of a targeted
subsurface structure. As a result, 3-D surveys are performed more frequently than are 2-D
surveys today.
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Many contractors and small oil companies acquire small onshore 3-D data
surveys in addition to 2-D data. However, it is not uncommon to see 3-D surveys of
hundreds of square miles conducted onshore by some oil companies. As the demand for
oil and gas grows, more investments will be made in seismic exploration. This is the
main reason why it is important to study new three-dimensional seismic survey design
techniques for hydrocarbon exploration and development.
1.2. Why 3-D Surveys?
Profiles of 2-D seismic data may be beneficial and adequate when there is no need
to do detailed geological studies with high resolution. In addition, the time and equipment
requirements, and operational cost of 2-D surveys are less than 3-D surveys. Because 3-D
data provide a detailed 3-D volumetric image of the subsurface and a more accurate and
reliable interpretation of subsurface geology, one of the main goals of 3-D surveying is to
produce data that will result in an accurate 3-D migrated wavefield (Yilmaz, 1987).
Three-dimensional seismic data acquisition is the acquisition geometry that provides the
most accurate information about locations of faults and dipping subsurface structures.
1.3. 3-D Seismic Survey Design and Special Considerations
3-D surveys are more complex and difficult to design than 2-D surveys. When
considering the equipment that has to be deployed and the configuration and deployment
of this equipment, there is greater complexity in creating and designing a 3-D seismic
survey than just choosing sources and receivers and recording data. As stated, the main
objective of 3-D seismic survey designs is to implement a survey that satisfies all data-
processing and imaging needs and that produce a good image for interpretation in a cost-
effective manner. The data that are acquired must yield new drilling locations after
interpretation and define potential reservoirs, fluid contacts, and reservoir compartment
boundaries. Three-dimensional seismic data thus confront decisions that commonly
involve several millions of dollars in acquisition costs. Although it seems impossible to
manage 3-D seismic data-acquisition requirements with optimum cost, benefit there are
new technologies and innovations that allow one to create a design that ensures a desired
data quality with acceptable cost.
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3
The models and algorithms shown in Figure-1.1, Figure-1.2, and Figure-1.3
describe the relationship between geology, geophysical needs (processing requirements),
and the design and cost of acquisition surveys utilizing Vibroseis sources that will be
used in this analysis.
1.3.1. Field Operations and Survey Considerations
Data-acquisition service companies design their surveys according to the
objectives and expectations provided by their clients. Cost can be as important as data
quality and survey planning and crew management play important roles in planning
seismic surveys. Although the first consideration in acquiring seismic data is to record
data that have optimal quality, unrealistic survey designs may be planned and cause
undesirable results if unrealistic budget constraints are imposed. As a result, overall risk
management will be an important part of seismic survey design and execution.
Survey plans are generally dictated by two considerations: operational issues in
the field and technical requirements of the interpretation. Both constraints are considered
to be cost-related.
(1) Operational considerations consist of permitting surface access for the survey
and obtaining access to the prospect in light of environmental and cultural constraints,
weather and seasonal considerations, equipment availability, daily crew availability cost,
and similar concerns. These constraints and considerations establish a cost and budget
plan before beginning the survey. Planning the survey is mainly determined by the total
source effort (vibrator sweep parameters and number of source units), and survey
geometry parameters (Figure-1.1). The source effort (number of units) and sweep
parameters include the type of sweep (linear or nonlinear, up-going or down-going),
sweep frequency interval, listening time, total number of sweeps required for each
station, and so on (Figure-1.2). Survey geometry parameters to consider are the number
of receiver and source lines, number of receiver and source points on each line, in-line
and cross-line orientations, and position of sources relative to the receiver groups
(Figure-1.3).
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MODEL OF 3-D SEISMIC SURVEY DESIGN AND ACQUISITION
Geophysical
Considerations
Resolution Signal to
Noise (S/N)ratio
Sampling
1- Vertical Rate
2- Horizontal
-----------------------------------------------------------------------------------------------------------------------------------------------
Figure-1.1: The model of 3D survey design and seismic data acquisition used in this analysis.
Acquisition SOURCE EFFORT AND
VIBROSEIS SWEEP
PARAMETERS
Parameters
SURVEY GE
DESIGN P
--------------------------------------------------------------------------------------------------------------------------------------------
Design
Parameters
Type ofsweep
Bandwidth
of sweep
Length ofsweep
Listen
time
Geometry ofsets of sources
-----------------------------------------------------------------------------------------------------------------------------------------
Type ofgeometry
Numbin t
Receiver andsource station
intervals
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Figure-1.2: The algorithm used to design sweep parameters and acquisition parameters
Starting
Parameters
Signal to
Noise (S/N)ratio
Thickness of
target
Sweep
Parameters
----------------------------------------------------------------------------------------------------------------------------------------------------
----------------------------------------------------------------------------------------------------------------------------------------------------
Cost Parameters
Type of
sweep
Bandwidth
of sweep
Length of
sweep
Geometry of sets
of sources
----------------------------------------------------------------------------------------------------------------------------------------------------
Number of stations require
complete survey
SOURCE EFFORT AND VIBROSEIS SWEEP PARAMETERS
D
Number
of sweepsArray
Total time required to complete
acquisition at each source stationSecond set of
parameters
Total time required to complete
all source stations
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6
----------------------------------------------------------------------------------------------------------------------------------------------------
----------------------------------------------------------------------------------------------------------------------------------------------------
----------------------------------------------------------------------------------------------------------------------------------------------------
Figure-1.3: The algorithm of 3D survey design and geometrical parameters (Modified from Hardage, 19
Starting
Parameters
Depth of the
shallowesttarget
Width of the
narrowest targetto image
First set of
Parameters
Cost parameters
Source and
receiver station
intervals
The number ofreceivers and
sources required
Total number of people in crew required
to complete entire survey
Inline and cross-linefold
The dip
geolotarge
Size of the patch
Second set of parameters
Total time required to lay down
all equipment
SURVEY GEOMETRY PARAMETERS
Source and
receiver line
intervals
The size of
the area to besurveyed
Type of the geometry
and gather (CMP or
CDP)
Minimum
offset
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Parameters used in 3-D surveys should be specified precisely in advance because
it is not operationally efficient to change parameters and redefine the survey after
acquisition begins. Therefore, any area which may require surface preparation and special
regulations should be thoroughly examined, and all cultural constraints and zones that
have not been permitted should be carefully considered before making the final decision
about how to proceed with data acquisition. Every single day that elapses without
production will affect cost. All of the operational constraints mentioned above should be
considered, and a survey should be planned to accommodate these constraints.
1.3.1.1. Multi-channel Measurements
Today’s seismic exploration involves many data channels that record the seismic
signal created at a number of source stations. Multi-channel acquisition techniques are
based on systems that use several source stations and a great number of receiver stations
that are simultaneously active. In these systems, several spread geometry descriptions can
be created to orient the sources and receivers. In land seismic situations, more geophone
stations are typically deployed in the field than the data-acquisition system can
simultaneously record (Kereks et al. 2001). In this way the proper number of active
channels must be quickly connected when needed and there is no need to alter the total
field layout. To simultaneously record a reflected wavefield by a large numbers of
receivers, different procedures have been developed. Two methods are preferred to
handle the areal boundary conditions of the survey: “roll-on, roll-off” and “with-tail”
(Thomas et al. 2004b). The “roll-on, roll-off” technique, also referred to as “no-tail”
geometry, does not include survey areas of source stations without receiver channels, or
receiver channels without source stations (Thomas et al. 2004b).
Regardless of the acquisition and design technique, all 3-D seismic acquisition
designs are made by deciding the geometry and the number of stations and lines for both
receivers and sources. Deploying the required equipment in the field is always the first
step in the acquisition. Once all the stations and lines are in place, shooting and recording
can commence. The seismic signal generated at each source station is recorded by many
hundreds of receivers planted in different directions in a 3-D geometry. Thousands to
millions of seismic traces are collected in typical 3-D seismic surveys.
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There are different techniques to sort and gather these seismic traces; however,
most methods are defined in terms of as common-midpoint (CMP) gathers (Evans, 1997).
Among the techniques for sorting the data are:
• Common-midpoint gather (CMP) – In this gather, all traces that belong to the same
source-receiver midpoint will be gathered.
• Common-shot gather - In this record, all traces that belong to the same shot will be
gathered. This is how data are typically recorded.
• Common-receiver gather- In this gather, all traces recorded with the same geophone
will be gathered.
• Common-offset gather- In this gather, all traces with the same offset between the
receiver and shot will be gathered.
receiver arrasource
Figure-1.4: Data sorting techniques and CMP method (Modified from Evans, 1997).
earth
surface
reflector
Common receiver gather
Common shot gather
Shot number
Common offset gather
Common midpoint gather
Position on the line
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Some of these different data sorting techniques are illustrated in a “stack-chart”
format in Figure-1.4.
The Common Midpoint (CMP) Method
The common-midpoint method (also referred to as Common Depth Point (CDP)
and Common Reflection Point (CRP) is a very common approach for imaging subsurface
structures in reflection seismic surveying (Evans, 1997). For a single reflection event, the
location that is midway between the source and the receiver is called the midpoint . For
groups of receivers and sources, traces can be gathered from reflection events that
originate at a common midpoint, and this collection of traces is called a CMP gather
(Evans, 1997). The main advantage to collecting traces into CMP gathers is that these
gathers are optimum for many data-processing steps such as stacks, velocity analyses,
multiple suppression, and signal enhancement. CMP stacking and velocity analysis are
particularly important steps in seismic signal processing and imaging (Shin, 1999).
Swath Shooting Method
Swath shooting is an acquisition geometry that involves several parallel receiver
lines, and in which the source stations move back and forth along these fixed receiver
lines. Regardless of the location of sources and receivers, all midpoints are halfway
between a shot location and receiver station. A swath method that includes source lines
that extend across several parallel receiver lines is a common configuration used to shoot
a 3-D land survey (Evans, 1997). Figure-1.5 illustrates a simple example of the geometry
of how sources and receivers could be placed in the swath.
Once a survey geometry is selected, the survey designer creates a shooting
template and allows all sources required for the survey to fire into their respective
receiver template. From the shots recorded by these receivers, it is possible to calculate
expected data-acquisition statistics. This process is then repeated for a number of survey
geometries that need to be considered. The more survey options that are designed, the
better are the chances of finding an optimum acquisition geometry. Before defining the
acquisition parameters, it is necessary to discuss the following geophysical considerations
that affect the acquisition geometry and influence a survey design.
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*Receiver lines
Figure-1.5: A swath geometry and its internal shot station configuration (Modified from
Evans, 1997).
1.3.1.2. Survey Considerations
This section focuses on technical considerations that constrain a survey and
specifies the design optimization programs that are used for exploration targets. In this
section, seismic resolution, signal-to-noise ratio, temporal sampling rate, spatial sampling
and aliasing, evaluation of noise and array designs, and migration aperture concepts are
discussed, and their effects are compared and related to each other.
There are many practical considerations in a field design which must be
understood before starting the design process. As shown in the outline illustrated in
Figure-1.3, the first step in analyzing data-acquisition and design is to understand the
targets that have to be imaged (that is, data-processing and imaging requirements). A
designer should be aware of which survey requirements affect the imaging of subsurface
structures. Key survey considerations are data resolution, signal-to-noise ratio, samplingrate, and spatial aliasing.
(a) Seismic Resolution
The resolution that is required in both the vertical and horizontal directions is one
of the key design considerations. The ability to identify the top and bottom boundaries of
Inline swath geometry*
**
**
*
Source stations acrosseach receiver-line
interval
Receiver line
Receiver line
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a geological target is defined by vertical resolution, while the ability to image small-scale
features such as meandering channels, small reefs, and channel-like structures is called
horizontal resolution (Evans, 1997). As stated in the model in Figure-1.3, vertical
resolution is controlled by the frequency bandwidth of the reflected signal. Because the
frequency content of the reflected signal limits vertical resolution, selection of bandwidth
is very important. Higher frequencies can be obtained with wider signal bandwidth
(Evans, 1997). Any increase in the dominant frequency of the data increases vertical
resolution (Yilmaz, 1987). The dominant wavelength of a reflected signal is defined by
the following equation (Yilmaz, 1987);
λdom = V /f , (1.1)
Where, V refers to the propagation velocity of the seismic wave at the reflector depth,
and f stands for the dominant frequency of the propagating wavelet.
Horizontal resolution is improved by migration. Lateral (horizontal) resolution
also depends on velocity and is limited by the Fresnel Zone (Yilmaz, 1987). Horizontal
resolution is represented by (Yilmaz, 1987);
r = V/2 . (t/f)1/2
. (1.2)
Where, r is the radius of the (First) Fresnel Zone for a dominant frequency f and velocity
combinations at various reflection times t (related tV depths) depths (t=2z/V) (Yilmaz,
1987). Effective 3-D migration improves the resolution by reducing the size of the
Fresnel zone. More complex structures may require higher vertical resolution, which
depends on frequency, temporal sampling and lateral resolution as well as receiver and
source line and station spacings (Evans, 1997). If the area that is to be imaged has steeply
dipping reflections, the acquisition aperture (total length of the array of receivers)
becomes important, and aperture width may vary from one direction to another. As stated
by Evans (1997), aperture width in the dip direction should be larger than the aperture
width in the strike direction (Figure-1.6) In many reflection surveys, seismic lines are
located in a direction parallel to the dip direction if possible (Evans, 1997).
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Dip Direction
Strike Aperture
Dip Aperture S t r i k e D i r e c t i o n
Figure-1.6: Horizontal resolution of seismic data based on aperture width.
(b) Signal-to-Noise (S/N) ratio
Signal-to-noise (S/N) ratio is also an important consideration used to define the
quality of seismic data. Data quality can be improved by many ways: adding Vibroseis
sweeps together (vertical stacking), increasing the sweep length, and designing efficient
source and receiver arrays.
Noise attenuation and array design
The main objective of source and receiver arrays (groups of geophones at each
receiver position) is to enhance signal and attenuate noise (Stone, 1994). Repeatable and
consistent (coherent) noise can be canceled by using arrays, or group, of receivers
(Evans, 1997). Ground roll is a coherent noise that commonly overrides onshore data and
is characterized by circular to elliptical particle motion as it travels along the Earth
surface (Evans, 1997).
Vermeer (1990) suggested that the solution to a better suppression of side-
scattered ground roll is to apply the criteria of symmetric sampling, which requires equal
shot and receiver intervals and equal shot and receiver arrays. Symmetric sampling
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means there must be an even, uniform succession of not only geophones, but also of
sources, across the entire seismic line. “Symmetric sampling is based on the principle of
reciprocity that asserts properties of the wave field in the common-receiver domain are
identical to the properties in the common-shot domain”, (Vermeer, 1990).
Using multiple receivers can also attenuate random (incoherent) noise that occurs
in unrepeatable patterns. The way to attenuate random noise is to space elements of a
receiver array far enough apart so that there is no correlation between the noise detected
on those elements (Evans, 1997).
Noise testing is an efficient way to identify coherent-noise and to test array
effects on signal quality. In this technique, geophone and source groups are deployed, and
the signal generated by different configurations of sources is recorded by geophone
arrays that are deployed at different intervals and in various numbers (Personal
communication, Tom Thomas, Dawson Geophysical, 2006). Based on the layered Earth
model illustrated in Figure-1.7, an array can be considered as a spatial filter that
attenuates coherent ground roll as well as random noise. An example of one noise test
and array configuration is shown in Figure- 1.8 and Figure-1.9.
Figure-1.7: Single reflector model for single shot and receiver array.
Source
Receiver arrayGround Roll
Reflection events
Reflector
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Properly designed geophone arrays effectively amplify the desired signal and
suppress unwanted noise. The signals coming from the separate geophones of a receiver
array are summed into one signal for all of the receivers in the geophone group. If the
geophones are a linear array, the directional response of the array is defined by the
relationship between the apparent wavelength (λ ) of a wave traveling horizontally in the
direction of the array, the total number of receivers in the array (N), and the interval
between receivers (∆x).
The main goal of array design is to identify the dominant wavelength of the
horizontally propagating ground roll package. The array length should then be the same
as this dominant ground roll wavelength. After identifying the wavelength of the ground
roll package, an array may be designed that cancels the noise for any known noise
wavelength (Figure-1.8 and Figure-1.9).
The response of an array is defined by following equation (Evans, 1997);
R(k) = sin(k x N.π.dx/2)2 / (k x.N.π.dx/2)
2 , (1.3)
where N is the number of phones deployed at each station, k x is wavenumber of the
signal, and dx is the spacing between geophones in the array. Based on this equation, an
array of geophones attenuates waves that have a wavenumber greater than k x (Evans,1997). The more geophones that are used (N is large), the larger is the range (R) in which
the noise is removed. The attenuation of noise can be represented by the amplitude
response formula defined by Evans, (1997) as;
Amplitude Response (dB) = 20 log [sin(k x N.π.dx) / (k x.N.π.dx)] (1.4)
The 1/N factor causes attenuation to increase as array length increases (in other words as
the number of receivers increases). But long arrays also might cause high-frequency
reflections to be attenuated (Evans, 1997). One array design option is illustrated in
Figure-1.10. In order to preserve shallow reflections that are characterized by high
frequencies and more oblique approach angles, the length of the array should not be long.
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Figure-1.8: Effect of number of phones on signal wavelength (top), and the effect of
distance between phones on signal wavelength (bottom).
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Wavenumber-k (1/m) 0 0.01 0.03 0.05 0.07
Figure-1.9: Array response and attenuation.
A t t e n u a t i o n ( d B )
-10
-20
-30
-40
-
-
-
-
20 log (1/N)
Lobe Envelope
Lobes
k=(1/dx)k=(3/Ndx) k=(5/Ndx)
k=(1/Ndx)
Maximum attenuation points:
Notches = k=[(1/Ndx), (2/Ndx), (3/Ndx), . . ., (N/Ndx)
Amplitude Response (dB) = 20 log [sin(k x N.π.dx) / (k x.N.π.dx)]
Lobe Envelope
Wavenumber (1/m)
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The array shown in Figure-1.9 is defined by the number of phones (N) in the
array and the spacing of the phones. Its response will have: (1) maximum attenuation
points at wavenumbers (1/Ndx), (2/Ndx), … ,(N/Ndx) (Alias), (2) side lobes between
maximum attenuation points, (3) the first alias peak at wavenumber is k=1/dx, and (4) a
lobe at the center of the attenuation band will have a relative amplitude of (1/N).
Figure-1.10: Target spectrum of attenuation for receiver array.
Wavenumber (1/m)
The center of the wavenumber spectrum is at k x=0.035. Therefore, the maximum
attenuation for this array should be k x=1/2dx = 0.035, which implies that the first alias
peak will be at k=1/dx = 0.07. Figure-1.10 shows the response of the array, and the
targeted wavenumber spectrum is represented by rectangular box.
Another way to increase the signal-to-noise ratio is to design stack arrays in CMPgathers. Anstey (1986) stated that ground roll can be attenuated with stacking arrays in
different shot-receiver configurations, especially in split-spread shooting technique.
Because it provides more continuous array coverage and has signals from both near and
far offsets, split-spread shooting geometry is an efficient way to increase signal-to-noise
ratio in CMP data.
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Configuring sources into arrays also reduces the noise level in data because
source arrays increase the output energy level and their spatial extent attenuates
organized source-generated noise. Evans (1997) suggested that designing both receiver
and source arrays have a significant influence on suppressing noise. Combined array
responses have a better rejection of coherent horizontal wave noise than does either a
receiver array response alone or a source array response alone.
(c) Sampling Rate
Sampling rate and frequency aliasing are important issues that need to be
considered before designing a survey. Sampling theory has interested geophysicists since
the beginning of the geophysical industry (Stone, 1994). Two types of sampling, spatial
sampling and time sampling, are used in the seismic industry. Time sampling is done by
the acquisition recording (digitization) system for all data channels, while spatial
sampling is performed by using geophone arrays on the receiver stations, and by
choosing different receiver group intervals and line spacings (Stone, 1994).
Sampling in the time domain
Because it is possible to resolve Earth layering with proper vertical sampling,
acquisition survey designs should consider time sampling as a main concern. A time
sampling interval should be chosen that is small enough that the reconstructed signal is an
accurate representation of the original bandwidth of the signal (Yilmaz, 1987). To ensure
that, the highest frequency in the data should be less than one-half of the Nyquist
frequency associated with the digitization process. Any frequencies higher than Nyquist
frequency will be aliased and will appear in some part of the lower range of the frequency
spectrum that has signal that needs to be preserved without contamination (Stone, 1994).
Figure 1-11 illustrates the concept of the Nyquist limit and aliasing criteria. Anti-alias
filters in the recording system are used to eliminate frequencies that might cause aliasing
(Stone, 1994). The time sampling (digitization) intervals used in seismic data acquisition
are usually 2-ms and 4-ms. A sampling interval of 2-ms sampling will require an anti-
alias filter that eliminates frequencies higher than 62.5 Hz, while a 4-ms sampling needs a
125 Hz anti-alias filter in order to avoid aliasing problem (Stone, 1994).
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Power spectrum
Overlap = aliasing
Frequency (Hz)
- Anti-alias filter for 4-ms sampling intervalPower spectrum
Cut-off Cut-off
Figure-1.11: Sampling theorem and anti-alias filter: (Modified from Stone, 1994).
Spatial Sampling
Spatial sampling is important in seismic data acquisition because it affects
frequency content, velocity analysis, and the migration of dipping reflection events. The
distance between receiver stations (geophone group interval) should be small enough to
inhibit aliasing and to achieve proper sampling (Evans, 1997). Because frequency-
wavenumber (f-k) plots do not distinguish noise from actual reflection signal when
spatial aliasing exists, spatial aliasing is one reason for misinterpretations of dipping
structures in the subsurface (Evans, 1997). To avoid spatial aliasing, station spacing
should be chosen according to the following equation;
∆X= V / [4. (f max). (sin Qdip)] , (1.5)
where V is the velocity, ∆X is the receiver-station interval, f max is the maximum
frequency in the data, and Q is the structural (geologic) dip of the reflector (Evans, 1997).
Figure 1-12 illustrates the Earth model. When the velocity increases linearly with depth,
the ray paths change from straight lines to curved ray paths as shown in the figure.
Frequency (Hz)125 Hz62.5 Hz 250 Hz
- Anti-alias filter for 2-ms sampling interval
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∆X
Earth surface
Curved rayDepth (Z)
Straight ray
Figure-1.12: Ray propagation model and sampling interval relationship.
As seen on the Figure-1.12, the receiver-station interval required for proper
sampling interval can calculated by using equations for either straight or curved rays. For
straight-ray propagation, the required receiver interval is calculated using the following
equation (Evans, 1997);
∆X= V / [4. (f max). (sin Qdip)] , (1.6)
The relationship between receiver interval, frequency, velocity, and geologic dip
for the case of curved rays propagation is given by the following equation (Evans, 1997);
∆X= [Vo+ C. Z] / [4. (f max). (sin Qdip)] , (1.7)
Propagation velocity increases linearly with depth, as described by equation-1.7. In this
equation, In this equation, Vo is a constant velocity for the model in which depth is
variable, Q is geologic dip, Z is the depth of reflector, C is a constant, and f max is the
maximum frequency in the data.
reflector(Qdip
X= [Vo+ C. Z ]/ [4. (f max). (sin Qdip)]
(The equation for curved rays)
X= V / [4. (f max). (sin Qdip)]
(The equation for straight rays)
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In Figure-1.13, it is seen how receiver-station interval is chosen based on
frequency, velocity, and structural (geologic) dip. It is obvious that the spatial sampling
interval requirement requires a smaller receiver spacing with increasing frequency and
with increasing dip angle. It is also possible to conclude that the receiver spacing
increases with velocity if everything else is held constant.
The relationship between receiver interval and spatial sampling can be expressed
in a second way. Sampling theory requires that a recorded wavelength should contain at
least two samples per wavelength to prevent aliasing (Evans, 1997). This means that
spatial frequencies that will not be aliased have wavenumbers given by (Evans, 1997);
k x < 1/ (2.∆X) (1.8)
In this equation, k x is the wavenumber, and ∆X is receiver-station spacing.
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Figure-1.13: Relationship between receiver-station interval, geologic dip, velocity, and
frequency for straight ray paths (top) and curved ray paths (bottom). Small geophone
group intervals are required to avoid aliasing of high frequencies (above 30 Hz).
(d) Migration aperture
Seismic migration is a data-processing, imaging technique that improves lateral
resolution, preserves amplitudes, maps dipping events to their true geological locations,
and collapses diffractions to their discontinuity origination points (Yilmaz, 1987).
Migration is an essential tool for interpretation of geologic structures on seismic sections.
In order to interpret geologic features such as structural dips and faults, a migration
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aperture should be chosen that is as large as possible; however, a large aperture also
increases acquisition and processing cost (Shankar, 2006).
Better migration is achieved with proper choice of the size of the migration
aperture. A small migration aperture width increases random noise and mis-positions
dipping events. On the other hand, a large aperture reduces the migration quality across
shallow regions if there is a low signal-to-noise ratio. Consequently, the optimal
migration aperture width is controlled by the amount of noise existing in the data, among
other factors.
After defining the source-receiver station interval, the migration aperture should
be calculated to define the size of the acquisition area that is needed at each depth to
recover a certain dip angle. Migration aperture is described with the following equation
(Evans, 1997);
A= Z. tan (Qdip) , (1.9)
Where, A is the total Aperture, Z is the reflector depth and Qdip is the dip of the reflector
(see fig. 1-12). In the constant-velocity case, the recoverable dip decreases with depth
because the fixed migration aperture is less able to capture steeper dip at increasing
depth. When there is a linear increase in velocity with depth, the dip recovered by a fixed
migration aperture is larger, because rays are refracted and create a smaller aperture at the
Earth surface (Figure-1.12). As depth increases, a velocity increase causes the dip limit to
increase while a fixed migration aperture causes the dip limit to decrease.
If possible, acquisition parameters should be adjusted so that the maximum
recoverable dip for both aliasing and aperture corresponds to the dip at the target depth.
Figure-1.14 illustrates the relationship between dip angles with increasing depth. As an
example, to image a target with a 40o degree dip at a depth of 10,000 m, a designer
should increase aperture size and decrease receiver interval. This recoverable dip can be
properly imaged only if frequency, aperture size, and receiver interval are adjusted so that
migration is done correctly for the desired target depth.
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Figure-1.14: Relationship between recoverable dip angles with increasing depth and
aperture limit (for straight lines dip angle; Q = asin ([V] / [4*f*∆X])). In the first example
(top), the aperture limit is 5,000 m and the receiver-station interval is 25 m. In the second
example (bottom), the aperture limit is 10,000 m and the receiver-station interval is 15 m.
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If the migration aperture is restricted to 5,000 m and the receiver-station interval
is 25 m (Figure-1.14, top curve), it will not be possible to image structures having dip
angles more than 30o at the target depth of 10,000 m.
Geologic dip varying with depth for curved ray
The relationship between geologic dip angle,
aperture limit, and receiver-station interval
(for curved ray paths)
Figure-1.15: Geologic dip, frequency, and migration aperture relationship for curved ray
path (dip angle; Q = asin ([V + C*Z] / [4*f* ∆X])). In the model illustrated by the red
line, the receiver-station interval is 50 m. In the model illustrated by the blue line, the
receiver-station interval is 25 m.
The relationship between frequency, geologic dip, and the migration aperture
when velocity increases linearly with depth is illustrated in Figure-1.15. Here, a target
located at 2500 m with a 40o dipping angle can be imaged by decreasing the distance
between receiver stations from 50 m to 25 m.
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1.4. Summary
There are many techniques for planning and designing a 3-D survey and for
implementing this design in the field. Regardless of the technique used to create a 3D
acquisition design, there are several practical considerations that affect the acquisition
geometry and survey characteristics. Survey requirements such as resolution, signal-to-
noise ratio, sampling rate, and migration aperture will have significant impacts on the
survey and must be considered before starting the survey.
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CHAPTER 2
SURVEY DESIGN PARAMETERS AND RANDOMIZATION
2.1. Introduction
Once essential survey imaging requirements are considered, 3D seismic data-
acquisition field parameters can be defined that optimize acquisition programs for
exploration targets. In this section, each acquisition parameter is defined and its impact
on the success of the survey design is examined. An appreciation of the impact of various
field techniques on multi-dimensional (2-D and 3-D) seismic data quality will be
emphasized. The parameters that will be discussed in this section are target depth,
configuration of receiver and source positions in the field, fold, offset considerations,
source and receiver line spacing, bin size, source-receiver azimuth and offset
distributions, recording channel requirements, advanced 3D techniques, and model
studies.
Acquisition geometry has a significant effect on seismic imaging. For example
acquisition artifacts, or footprints, cause many problems on records during data
processing and interpretation. Therefore, it is also important to design a survey in which
geometry effects have minimal influences on the records. Randomization of source and
receiver station coordinates is one of the character solutions for removing geometry
effects that will be introduced in this section. Several random models designed with a
MATLAB code developed in this research study will be illustrated in the next chapter,
where fold, offset, and azimuth distributions generated by these models will be presented.
2.2. 3-D Seismic Data Acquisition and Survey Design Parameters
Several recent techniques have focused on optimization of acquisition cost and
data quality. Multi-component acquisition, High Fidelity Vibroseis Seismic (HFVS), and
other techniques have been developed to improve data quality while minimizing
acquisition cost.
When designing a 3D seismic survey, the first step is to define the type of
acquisition geometry. Some options that can be used in surveying are: orthogonal
geometry (the alignment of source and receiver lines are orthogonal to each other),
parallel ,or swath, geometry (source and receivers lines are parallel to each other), brick
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geometry (the form of the source and receiver lines looks like a brick pattern), zigzag
geometry (source lines have angles of 45º and 135º with the receiver lines), slant
geometry (receiver and source lines are non-orthogonal), and so on (Vermeer, 2004). In
each of these geometries, the design objection is to create a regular offset distribution in
each CMP bin and a concentration of midpoints in the centers of the bins (Vermeer,
2004).
The 3D symmetric sampling approach suggested by Vermeer (1990) minimizes
the number of spatial discontinuities that cause migration artifacts. To ensure correct
spatial sampling, equal parameters are chosen for in-line and cross-line directions so that
migration artifacts are reduced (Vermeer, 2004). In this logic, the cross-line direction
becomes as important as the in-line direction in the imaging process. Therefore, an
orthogonal geometry with its source-receiver azimuth diversity is one of the best design
options to ensure correct spatial sampling is achieved (Vermeer, 2004).
Because each intersection of source and receiver lines defines the center of a
cross-spread, the data acquired with an orthogonal geometry can be considered as a
collection of cross-spreads (Vermeer, 2002). As stated by Vermeer (2005), the spatial
continuity of the midpoint area of a cross-spread geometry provides great potential for
pre-stack processing steps, ambient noise removal, and interpolation of missing shots or
receivers.
Another comparison between two common acquisition geometries: parallel and
orthogonal geometries, is described by Vermeer (2005) as, “The properties of these two
types of geometry are different. Parallel geometry is an intrinsically narrow-azimuth
geometry, whereas orthogonal geometry is suitable to acquire wide-azimuth data. For
parallel geometry data it is possible to create low-fold regular coverage by selecting a
small range of absolute offsets, whereas in orthogonal geometry such a selection would
provide highly irregular fold-of-coverage.”
As a result, each of the geometries listed previously has advantages and
disadvantages when compared to another. Once survey geometry is selected, the
acquisition and survey design parameters can be selected by conventional methods such
as discussed by Stone (1994), Hardage (1997), Cordsen et al. (2000), Vermeer (2004),
and other authors.
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No matter which technique is used, all acquisition and survey designs are planned
with respect to the following parameters: target depth, fold, and azimuth and offset
distributions.
Target depth is considered as the first parameter of the acquisition. The type and
areal extent of the survey geometry is determined by the target depth. There are various
approaches for defining the areal extent of the survey for various target depths. Some
approaches prefer to define the areal extent of the survey in both dimensions as 80% of
the maximum target depths, while some prefer that the dimension be the same as the
depth of the target. No matter which preference is used, it is accepted by most designers
that target depth determines the areal extent of a survey. Hardage (1997) stated that larger
source-to-receiver offsets are important for data processing and imaging, and he
emphasized that maximum offset of a recording swath is determined by target depth.
Figure-2.1 illustrates an acquisition geometry and survey layout system based on
orthogonal geometry. Orthogonal geometry is commonly used for onshore surveys
because it constitutes the ideal situation in which the acquisition parameter can be the
same in both the in-line and cross-line directions (Vermeer, 2003b). Moreover as
indicated by Vermeer (2003b), an orthogonal geometry leads to proper spatial sampling,
and continuous wavefields can be fully constructed from the sampled wavefield. Because
it is easy to implement, an orthogonal geometry is also considered as one of the best
survey geometries (Thomas, 2004a). In an orthogonal geometry, receiver cables are
oriented along in-line directions and are perpendicular to source lines that are oriented as
cross-lines. This geometry is an advantage when calculating CMP bins and bin statistics.
Bin size is an important parameter because it describes the spatial sampling and the trace
spacing in the processed volumetric image. A smaller bin spacing usually creates a better
image of the subsurface. The receiver-station spacing on the surface can be described as
twice the subsurface bin dimensions. On Figure-2.1, receiver groups are shown by blue
stars while source groups are shown by red stars. For this example, receiver and source
stations are deployed at 220-ft spacings along the lines while receiver and source lines are
1320-ft apart.
Figure-2.2 illustrates how receivers and sources are placed in the field. In this
figure, 3D survey layout terms such as in-line, cross-line, receiver and source stations,
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SLI (source-line interval), RLI (receiver-line interval), bin, midpoint, and recording patch
(template) are illustrated. The distance between adjacent receiver stations is described as
the receiver-station interval (RSI) while the distance between adjacent source points is
defined as the source-station interval (SSI). These parameters are selected by considering
the narrowest horizontal dimension of the geology that needs to be imaged (Hardage,
1997), with the requirement being that a minimum of 2 receiver stations should span the
narrowest dimension of the target.
As stated above, receiver and source-station intervals determine the horizontal
sampling of the data. The Bin dimension is described as the small rectangular area that
has dimensions of (SSI/2) x (RSI/2) (one-half of the source-station interval along source
lines and one-half of the receiver-station interval along the receiver lines), and all
midpoints that fall inside a bin are considered to belong to the same common midpoint
(Cordsen, 2000). When calculating the stacking fold of each bin, all traces belonging to
each bin will be CMP (summed) and contribute to that bin’s fold (Cordsen, 2000).
Stacking fold is thus described as the number of traces that are summed to construct the
single trace placed at the center of this particular CMP bin, and is given as the following
equation (Hardage, 1997);
FStacking
= FIL
x FXL
, (2.1)
In this equation, FIL is the fold in the in-line direction, and FXL is the fold in the cross-line
direction. The in-line fold is described as follows (Hardage, 1997);
FIL = [1/2 x (number of receiver channels) x [(RLI) / (SSI)] (2.2)
FXL = [1/2 x (number of receiver lines in recording swath)] (2.3)
In equations (2.2) and (2.3), RLI is the spacing between receiver lines, while SSI
is the spacing between sources.
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Figure-2.1: 3-D orthogonal seismic data acquisition survey design sample.
Receiver
stations
Source
stations
PATCH -
TEMPLATE
RLI
SLI
MIDPOINTS
In-line
Cross-line
Figure-2.2: 3-D acquisition and survey design terms.
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Figure-2.3: Fold calculation analysis and fold distribution diagram.
The last step in the design process is the comparison of nominal fold with the
desired fold. Before starting a design, a designer must decide how to choose the desired
fold for the survey (Hardage, 1997). “If only 2-D data are available in the area of interest
and these 2-D data adequately image the subsurface geology, a commonly used design
guideline is” (Hardage, 1997);
3-D stacking fold = ½ (2-D stacking fold) (2.4)
This states that 3-D stacking fold needs to be only one-half the value of 2-D
stacking fold to yield 3-D data of equivalent signal quality (Hardage, 1997). The number
of receiver lines and the spacing between source lines have a significant impact on fold
calculation. When designing a survey, the recording swath, or patch, must include an
even number of receiver lines (Figure-2.4) and an odd number of source lines to
minimize oscillations on the fold values (Figure-2.5) in adjacent bins across the entire 3-
D grid (Hardage, 1997).
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(a) 17x96 Patch A shooting (patch has odd number of receiver lines).
(b) 18x96 Patch B shooting (patch has even number of receiver lines).
Figure-2.4: Comparison of two 3-D recording swaths and patch shooting geometries.
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Figure-2.5: Comparison of stacking folds created by patch-A and patch-B
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For example, the fold distribution of survey design B given in Figure-2.4 is
illustrated in Figure-2.5. The template for this survey consists of 18 receiver lines, and
each receiver line has 96 stations. In this template, the receiver-station spacing is 220 ft,
and there is a source station every 1320 ft along each receiver line. Based on these
parameters, the maximum in-line and cross-line fold are calculated as given below:
FIL = [1/2 x (96) x [(220) / (1320)] = 8.
FXL = [1/2 x (18)] = 9.
and, the 3-D fold is;
F = FIL x FXL = 8 x 9 = 72.
Based on source and receiver locations and the offset, the amount of fold will
vary from one bin to another. The area of maximum fold is generally chosen as the
optimal image area (Cordsen, 2000).
Another way to describe an acquisition geometry is the Teepee Technology
introduced by Thomas (2004a,b). If the shape of the subsurface coverage is defined by a
teepee, and all acquisition parameters such as length of spread, source-to-receiver offset,
and fold are determined by the shape and size of that teepee. Thomas (2004b) defines the
spread length as the half-size of the distance between teepee centers, if each shot gather is
determined by one teepee. After identifying survey shape and size depending on the
target depth, fold is defined by the overlaps between teepees (Thomas, 2004b). Based on
these definitions, a survey design is created that conforms to the available equipment and
crew. By changing teepee parameters, the required field supplies are determined by the
survey design and are optimized for cost and data quality. Figure-2.6 illustrates the 2D
and 3D teepees associated with different shot and receiver configurations.
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Figure-2.6: 2-D and 3-D teepees for different amounts of fold distribution (Modified
from Thomas, 2004a,b).
Station and line intervals, patch parameters (size and the shape of the template),
and the number of source and receiver points necessary to obtain a desired fold define a
survey design. Companies have different criteria and methods for selecting these
parameters, depending on their objectives. One way to examine and compare these
options is to create a parameter table.
In this study, a spread sheet was prepared to describe 3D survey design and is
given as Figure-2.7. Depending on the availability of equipment and crew, the fold
occurring across a target area can be related to the size and shape of the patch and to the
intervals between receiver and source stations and lines. By using this table, the
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approximate cost of a survey can be predicted because the size of the patch will show the
minimum and maximum live receiver channels needed to record data from each
particular shot point in the survey area. Different survey options can be produced by
using this table, and various acquisition geometries can be considered so that companies
are able to make a cost analysis of a survey based on their objectives.
Figure-2.7: A spread sheet used to design a 3-D survey.
Besides fold, offset and azimuth distributions are important parameters for
effective imaging and hence survey designs. Once the geometry of the survey
(orthogonal, brick, zigzag, and so for) is specified, minimum and maximum offsets are
determined. Due to the feasibility and ease of the modeling that is required, an orthogonal
survey geometry is the most popular type used by designers. As previously stated,
receiver and source lines are located orthogonal to each other in this geometry. An ideal
case is to use square boxes in an orthogonal design, which will minimize the minimum
offset, described by the following equation (Cordsen, 2000);
Xmin= ((Receiver line interval)2 + (Source line interval)
2)1/2
, (2.5)
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Minimum offset should be small enough to properly sample shallow reflectors
that might be used for various processing or interpretation purposes. The depth of the
shallowest target that has to be imaged defines the proper minimum offset for the survey.
This shallowest target is that stratigraphic unit that has to be imaged to aid processing and
interpretation steps such as static corrections and shallow velocity analysis. The depth of
this unit controls much of the acquisition design (Hardage, 1997). The relationship
between minimum and maximum offsets and the depths of shallow and deep targets is
illustrated in Figure-2.8.
Figure-2.8: Minimum and maximum offsets (Modified from Hardage, 1997).
A designer must choose the source-line and receiver-line intervals to be less than,
or the same as, the depth of the shallowest target (ZSH) to ensure that a minimum offset
exists that will allow that unit to be imaged. As stated previously, the maximum offset
depends on the depth of the deepest target (primary target) that must be imaged (Hardage,1997).
Fold analysis by itself is not enough to describe a good survey design. Other
important parameters are the offset and azimuth distributions needed for good velocity
analysis. Bins which do not have a continuous range of offsets are useless because there
may not be enough moveout on traces to determine velocity and no chance to apply AVO
XMIN
XMAX
ZSH
ZTAR
ZSH= The depth of the shallowest target
ZTAR= The depth of the deepest target
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without the far traces (Stone, 1994). Thus, bins should have near, middle, and far traces
to provide optimal velocity analysis. Offset distribution is mainly controlled by the
amount of fold. Better designs create even distributions from near to far offsets to provide
better velocity analysis and stacking response. Aliasing of dipping signal and noise can
be caused by uneven offset distributions (Cordsen, 2000). An example of offset
distribution is illustrated by Figure-2.9.
Azimuth distribution is another important parameter and it is mainly affected by
fold and offset distributions. Azimuth is less important if the layers in the Earth are flat
and unfractured. If there is significant variability of the dip of layers, or there are some
directional structures such as salt domes, faults, and fractures then velocity becomes
direction-dependent and is related to azimuth (Stone, 1994). Because the Earth has
direction and azimuth-dependent structures, azimuth distribution graphs (spider plots)
provide important information for processing seismic data. Bad azimuth distributions
may cause problems such as biased variations in velocity caused by anisotropy or/and
dip, while a proper azimuth distribution provides information from all angles in the
stacking bin (Cordsen, 2000). The ratio of the cross-line dimension of the patch to the in-
line dimension is defined as the survey aspect ratio, which is a parameter that controls
azimuth distribution. Aspect ratio values between 0.6 and 1 give better azimuth
distributions while values less than 0.5 cause poor azimuth distributions (Cordsen, 2000).
Figure-2.10 illustrates one example of a spider plot or azimuth graph. Both azimuth and
offset distribution must be considered in an attribute analy
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