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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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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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 3.17 – 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 3.22 – 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


.......................................................................................................... 90


.......................................................................................................... 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


(Vp/Vs=2.0).… ...............................................................................101


(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


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


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


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


3dB/octave, 4 sweeps......................................................................135


linear, 1 sweep ................................................................................136


linear, 3 sweeps...............................................................................137


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

Engin Masters Thesis-data Acquisition Techniques

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