UNIVERSITY OF OKLAHOMA

GRADUATE COLLEGE

REPROCESSING 3D SEISMIC DATA FOR QUANTITATIVE INTERPRETATION,

FORT WORTH BASIN, JEAN, TEXAS

A THESIS

SUBMITTED TO THE GRADUATE FACULTY

in partial fulfillment of the requirements for the

Degree of

MASTER OF SCIENCE

By

MARCUS CAHOJ

Norman, Oklahoma

2015

REPROCESSING 3D SEISMIC DATA FOR QUANTITATIVE INTERPRETATION,

FORT WORTH BASIN, JEAN, TEXAS

A THESIS APPROVED FOR THE

CONOCOPHILLIPS SCHOOL OF GEOLOGY AND GEOPHYSICS

BY

______________________________

Dr. Kurt Marfurt, Chair

______________________________

Dr. John Pigott

______________________________

Dr. Oswaldo Davugustto

© Copyright by MARCUS CAHOJ 2015

All Rights Reserved.

iv

Acknowledgements

Completing this thesis would not have been possible without the help of many

people. First, I would like to thank the ConocoPhillips School of Geology and

Geophysics at the University of Oklahoma for providing me the chance to attain a

Master’s degree in Geophysics. Dr. Kurt J. Marfurt has been fundamental in my

development as a scientist and person. Dr. John Pigott and Dr. Oswaldo Davogustto

have both been crucial in providing me with geophysical and geological insight. Next, I

would like to express gratitude to Mike Burnett of TameCat llc for providing a license

to the data that I used in this thesis. Many students, current and former, friends, and

staff at the university have also aided in my research and completion of my thesis.

Those individuals include Thang Ha, Sumit Verma, Tengfei Lin, Jie Qi, Fangyu Li, Tao

Zhao, Gabriel Machado, Bryce Hutchinson, Joseph Snyder, Oluwatobi Olorunsola,

Lanre Aboaba, Abdulmohsen AlAli, Dr. Shiguang Guo, Dr. Bradley Wallet and many

others. I cannot thank each and every one of you enough for the knowledge and

companionship you have shared with me. I would also like to thank Schlumberger for

the use of VISTA and Petrel as well as CGG for the use of HampsonRussell. Without

the availability of this software at the University of Oklahoma I could not have

completed this thesis. Lastly, I would like to thank my family for their continuous

support during the pursuit of my Master’s degree.

v

Table of Contents

Acknowledgements ......................................................................................................... iv

List of Tables .................................................................................................................. vii

List of Figures ................................................................................................................ viii

Abstract ......................................................................................................................... xvii

Chapter 1: Introduction ..................................................................................................... 1

Objectives ................................................................................................................... 3

Location ...................................................................................................................... 4

Chapter 2: Geological Background .................................................................................. 7

Regional Geology ....................................................................................................... 7

Local Geology ............................................................................................................ 8

Chapter 3: 3D Prestack Seismic Processing ................................................................... 17

Data Acquisition ....................................................................................................... 19

Data Loading and Geometry .................................................................................... 21

Trace Editing ............................................................................................................ 24

Elevation and Refraction Statics .............................................................................. 25

Deconvolution .......................................................................................................... 28

Velocity Analysis and Residual Statics .................................................................... 32

Noise Suppression .................................................................................................... 37

Prestack Time Migration .......................................................................................... 48

Matching Pursuit Normal Moveout .......................................................................... 53

Prestack Structure Oriented Filtering ....................................................................... 55

vi

Chapter 4: Comparison to Legacy 1999 Vendor Volume .............................................. 57

Data Comparison ...................................................................................................... 57

Chapter 5: Interpretation, Inversion and AVAz ............................................................. 67

Structural and Stratigraphic Interpretation ............................................................... 67

Geometric Attributes ................................................................................................ 68

Post stack Acoustic Impedance Inversion ................................................................ 69

Amplitude Variations with Azimuth ........................................................................ 70

Geobody Extraction .................................................................................................. 70

Chapter 6: Conclusion .................................................................................................... 89

References ...................................................................................................................... 90

Appendix A: Understanding processing pitfalls with seismic modeling ....................... 93

vii

List of Tables

Table 1: Survey parameters ............................................................................................ 19

Table 2: Parameters for F-K filtering ............................................................................. 38

Table 3: Prestack Kirchhoff migration parameters ........................................................ 51

viii

List of Figures

Figure 1: County map of Texas. Young County is located in north central Texas (blue

arrow) ............................................................................................................................... 5

Figure 2: Satellite image of part of North America. Texas is outlined by the blue

polygon and the location of the seismic survey is denoted by the red star. ..................... 6

Figure 3: Cross section of Late Paleozoic trough through the Texas Craton and Ouachita

foldbelt. (Walper, 1982). .................................................................................................. 9

Figure 4: Map of Late Paleozoic’s structural elements in Texas and Oklahoma. (Walper,

1982). .............................................................................................................................. 10

Figure 5: Paleogeographic reconstruction of the Fort Worth Basin and Bend Arch (top)

during the Late Cambrian and (bottom) during the Devonian (Walper, 1982). ............. 11

Figure 6: Paleogeographic reconstruction of the Fort Worth Basin and Bend Arch (top)

during the Mississippian and (bottom) during the Pennsylvanian (Walper, 1982). ....... 12

Figure 7: Stratigraphic column of the Bend Arch. Stratigraphic formations of interest

for this study are the Early Pennsylvanian Caddo Limestone (Pollastro, 2007). ........... 13

Figure 8: Paleographic map of North America during the Early Pennsylvanian. The red

star represents the approximate location of the study area (Blakey, 2011). ................... 14

Figure 9 : (top) Structural provinces of Oklahoma and Texas. (bottom) Paleographical

map of the Fort Worth Basin (Thomas 2002). ............................................................... 15

Figure 10: East to West stratigraphic cross-section of the Fort Worth Basin and Bend

Arch (Bowker, 2007). The Caddo limestone lies within the Strawn group. ................. 16

Figure 11: Flow used for seismic processing and interpretation. ................................... 18

ix

Figure 12: Fold map of the Jean survey with source receiver array overlain. The

nominal fold is 17 with a maximum fold of 36. The survey has a rectilinear brick

pattern with a few irregularities due to surface obstacles. .............................................. 20

Figure 13: Raw seismic data sorted in the shot domain. There are 10 receiver lines with

the shot occurring between line 5 and line 6. Green arrows showcase reflectors and red

arrows highlight groundroll. ........................................................................................... 22

Figure 14: Raw seismic data sorted shot verses offset domain. The green line shows the

first break picks, used for refraction statics. The orange arrows highlight coherent

seismic noise, such as groundroll and headwaves. The blue arrow shows a seismic

reflection. ........................................................................................................................ 23

Figure 15: Representative gather with no sort order. The red arrow points to a traces

that needs to be killed. The green arrow points to a once bad traces that has been killed

by the processor. ............................................................................................................. 24

Figure 16: (From left to right) Map of the source receiver elevation, Layer 1 refraction

velocity, Layer 2 refraction velocities, picked from first break picks and long

wavelength refraction corrections. ................................................................................. 26

Figure 17: A representative NMO corrected gather (left) before and (right) after

refraction and elevation statics. Arrows indicate events that are better aligned resulting

in better frequency content. ............................................................................................ 27

Figure 18: (Left) Raw seismic data before correction for spherical divergence. (Right)

Seismic data after t-power gain correcting for spherical divergence. Deeper reflectors

are now more visible; however, groundroll is consequently enhanced. ......................... 30

x

Figure 19: Shot gather of the seismic data (top) before and (bottom) after spiking

deconvolution. The frequency spectrum is flattened increasing the contribution of both

low and high frequencies. ............................................................................................... 31

Figure 20: Semblance panel used for the second iteration of velocity analysis. (Left)

Semblance panel with picks used for NMO of the seismic data. (Right) NMO corrected

seismic gathers. ............................................................................................................... 34

Figure 21: Inline of the velocity field picked after the second iteration of velocity

analysis. The velocities range from 5500 ft/s to 16,000 ft/s. The velocity field is

laterally smooth such that prestack Kirchhoff time migration will provide accurate

results. ............................................................................................................................. 35

Figure 22: Brute stacked seismic data after the first round of velocity analysis. Orange

arrows indicate groundroll while green arrows indicate reflectors. (Middle) Brute stack

after second round of velocity analysis, deconvolution and F-K filtering. (Bottom) Brute

stack after velocity analysis performed on migrated gathers. ........................................ 36

Figure 23: Seismic data sorted shot verses channel. An (middle) Ormsby bandpass

filter shows most of the noise falls below 20 Hz. (right) By removing the bottom 20 Hz

most of the groundroll is suppressed. However, significant signal is also rejected

making an inversion and quantitative measures more difficult. ..................................... 39

Figure 24: (Left) A common receiver gather sorted by field station number versus

absolute offset and (Right) the corresponding F-K spectrum. Green arrows indicate

reflectors, yellow headwaves and orange groundroll. .................................................... 40

xi

Figure 25: Seismic data sorted by receiver versus absolute offset. It is necessary to have

the data sorted in this domain to perform F-K filtering. A strong presence of ground

roll is easily identifiable as are headwaves and reverberations. ..................................... 41

Figure 26: Seismic data sorted by field station number verses absolute offset with

polygons drawn around each mode of noise that was removed independently. Because

F-K filtering operates under the assumption of linear moveout and to avoid aliasing, the

noise to be removed needed to be flattened to be about wavenumber zero. .................. 42

Figure 27: (Top) Seismic data sorted by field station number versus absolute offset.

(Middle) Muted noise to be modeled in the F-K domain. The blue pick is defined as t =

𝒓𝑽𝑳𝑴𝑶 where r is the absolute offset and VLMO is the LMO velocity (blue). (Bottom)

LMO flattened noise. ...................................................................................................... 43

Figure 28: F-K spectrums of the segmented and bandpassed groundroll. ..................... 44

Figure 29: First mode of groudnroll modeled using F-K filtering. The frequency

content is between 0-25 Hz. The bottom figure is the same as above but with inversed

linear moveout applied. This is to be subtracted from the raw data to remove this mode

of groundroll. .................................................................................................................. 45

Figure 30: Seismic data sorted field station number verses absolute offset. The top

image is after the first mode has been subtracted from the original seismic. The bottom

figure is after all four F-K modeled modes of noise defined in Table 2 have been

subtracted. ....................................................................................................................... 46

Figure 31: (left) Seismic data sorted shot versus channel. Before any noise suppression

has been undergone. The (right) same gather after all F-K modeled modes of noise

have been subtracted. The orange arrows indicate zones where groundroll and coherent

xii

noise was overbearing the reflectors. The green arrows indicate zones where the

reflectors are now more visible. ..................................................................................... 47

Figure 32: Seismic data (left) after migration and (right) after migration with far offset

stretching muted. ............................................................................................................ 49

Figure 33: Stacked seismic data (A to A’) after migration and the muting of the

stretched far offsets ......................................................................................................... 50

Figure 34: Result after reverse normal moveout (RNMO). The data needed to be

RNMO’d for velocity analysis on the migrated data and matching pursuit normal

moveout. ......................................................................................................................... 51

Figure 35: Semblance panel used for the third iteration of velocity analysis after

migration. (Left) Semblance panel with picks used for NMO of the seismic data. (Right)

NMO corrected seismic gathers. The NMO corrected gather has greater resolution than

the NMO corrected gather during the second iteration of velocity analysis. ................. 52

Figure 36: (Left) Results after the second pass of migration. (Right) Results after

matching pursuit normal moveout. Notice the far offsets are better preserved leading to

better frequency content when stacked. .......................................................................... 54

Figure 37: (top) Rejected noise after prestack structural-oriented filtering using

Principle Components. (Bottom) Reflectors after Principal Component PSOF. After

PSOF incoherent noise has been removed from the migrated gather. ........................... 56

Figure 38: Vendor processed Jean seismic survey. The amplitudes in the shallow

section have not been properly balanced. Furthermore, the seismic data has been blued

up to 225 Hz causing a ringing around reflectors. The basement at 1100 ms is not easily

visible. ............................................................................................................................ 60

xiii

Figure 39: Reprocessed Jean survey. The amplitudes have been properly balanced in

the shallow section resulting in more identifiable reflectors. Furthermore, the frequency

spectrum has been whitened to 140 Hz resulting in a more geological result. The

basement at 1100 ms is easily identifiable. .................................................................... 61

Figure 40: Shallow horizon tracked to show the improved performance of autotracking

after reprocessing. (Left) Picks performed manually by the seismic interpreter. (Middle)

Autotracked horizon using the vendor processed Jean survey with a quality factor of

0.7. (Right) Autotracked horizon using the reprocessed Jean survey with a quality

factor of 0.7. ................................................................................................................... 62

Figure 41: Deeper horizon tracked to show the improved performance of autotracking

after reprocessing. (Left) Picks performed manually by the seismic interpreter. (Middle)

Autotracked horizon using the vendor processed Jean survey with a quality factor of

0.55. (Right) Autotracked horizon using the reprocessed Jean survey with a quality

factor of 0.55. ................................................................................................................. 63

Figure 42: Timeslice through seismic amplitude at t = 540 ms. (left) Vendor processed

seismic data. (right) Reprocessed seismic data. The reprocessed seismic data has

greater continuity due to the lack of high frequencies cross cutting the timeslices. ...... 64

Figure 43: Timeslice at t = 846 ms through a coherency volume. (Upper left) Vendor

processed coherence volume. (Upper right) Reprocessed data coherence volume. ...... 65

Figure 44: Timeslice through most negative curvature at t = 840 ms. (left) Vendor

processed most negative curvature. (right) Reprocessed most negative curvature. The

reprocessed seismic data has greater continuity due to the lack of high frequencies cross

cutting the timeslices. ..................................................................................................... 66

xiv

Figure 45: Line C-C’ showing key horizons. ................................................................. 71

Figure 46: Line from A - A’ showing key horizons. ...................................................... 72

Figure 47: (left) Time structure map of the Palo Pinto stratigraphic horizon tracked

through the reprocessed Jean survey. White arrows indicate areas of the horizon

contaminated by acquisition footprint. The causes of this footprint will be investigated

further in Appendix A. (right) Time structure map of the KMA stratigraphic horizon

tracked through the reprocessed Jean survey. ................................................................ 73

Figure 48: (left) Time structure map of the Upper Caddo stratigraphic horizon tracked

through the reprocessed Jean survey. The white arrow indicates areas of the horizon

containing draping over a carbonate reef. Reefs and draping due to deposition on top of

reefs are common oil and gas exploration targets (right). Time structure map of the

Basement horizon tracked through the reprocessed Jean survey. .................................. 74

Figure 49: (left) Time structure map of the Mississippian stratigraphic horizon tracked

through the reprocessed Jean survey. (Right) Time structure map of the Middle Caddo

stratigraphic horizon tracked through the reprocessed Jean survey. The white arrow

indicates a structurally low area in the horizon that is a potential channel cross cutting

the carbonate Strawn formation. ..................................................................................... 75

Figure 50: (left) Thickness map constructed by subtracting the Strawn Unconformity

stratigraphic horizon from the Upper Caddo stratigraphic horizon. The unconformity

thickens as we traverse to the northeast, coinciding with the trend of the Bend Arch.

(Right) Thickness map constructed from subtracting the Upper Caddo from the

Basement stratigraphic horizon. ..................................................................................... 76

xv

Figure 51: (left) Timeslice through most negative curvature at t = 420 ms. (Right)

Timelesice through most positive curvature at t = 420 ms. The curvature anomalies

indicated by the white arrows are interpreted to be acquisition footprint overprinting

geology. In Appendix A, I investigate the cause of footprint in the Jean survey using

seismic modeling. ........................................................................................................... 77

Figure 52: (left) Co-rendered K1 and K2 curvature extracted along the Middle Caddo

stratigraphic horizon. (Right) Co-rendered K1 and K2 curvature and energy ratio

similarity coherence attribute extracted along the Middle Caddo stratigraphic horizon.

The white arrow indicates a potential incised channel. .................................................. 78

Figure 53: Well tie and synthetic seismogram for Well #1 in the Jean survey. For the

best correlation between the seismic and the well logs, the statistical wavelet was

rotated to -170 degrees. With no density log available, one was estimated using the

Gardner’s equation (1974). ............................................................................................. 79

Figure 54: Well tie and synthetic seismogram for Well #2 in the Jean survey. For the

best correlation between the seismic and the well logs, the statistical wavelet was

rotated to -170 degrees. With no density log available, one was estimated using the

Gardner’s equation (1974). ............................................................................................. 80

Figure 55: Vertical section through the acoustic impedance inversion volume. The

section bisects Well #1 which has the acoustic impedance from the well logs projected

upon it. The impedance from the well to the impedance generated from the seismic

have a good correlation. ................................................................................................. 81

Figure 56: (left) Acoustic impedance extracted along the Upper Caddo horizon. The

Upper Caddo is a limestone resulting in high acoustic impedances. (Right) Acoustic

xvi

impedance co-rendered with the Upper Caddo time structure map. The majority of the

low impedance areas correlate to low structural relief zones. ........................................ 82

Figure 57: (left) Acoustic impedance extracted along the Middle Caddo horizon. The

Upper Caddo is a limestone resulting in high acoustic impedances. (Right) Acoustic

impedance co-rendered with the Middle Caddo time structure map. Low impedance

appears to be found along the channel feature seen in this horizon and the carbonate reef

in the southeast corner of the survey. ............................................................................. 83

Figure 58: (left) Acoustic impedance co-rendered with K1 and K2 curvature extracted

along the Upper Caddo horizon. (Right) Acoustic impedance co-rendered with K1 and

K2 curvature extracted along the Middle Caddo horizon............................................... 84

Figure 59: (left) Timeslice at t = 750 ms through the anisotropy azimuth volume.

(Right) Co-rendered anisotropy azimuth and density. .................................................. 85

Figure 60: Timeslice at t = 960 ms through the anisotropy azimuth volume. (Right) Co-

rendered anisotropy azimuth and density. ...................................................................... 86

Figure 61: Chair display from A to A’. Vertical section is seismic amplitude and

timeslice is K2 curvature. The arrow indicates a potential channel in the Middle Caddo

that is extracted using geobodies. ................................................................................... 87

Figure 62: Chair display showing geobody extraction of channel after Wallet (2014).

The vertical sections are seismic amplitude and the horizontal slice is the Middle Caddo

time structure map. The channel geobody extracted by thresholding K2 curvature is

shown in red. .................................................................................................................. 88

xvii

Abstract

The purpose of this study is to use state-of-the-art 3D seismic processing and

interpretation tools to bring a modern perspective to legacy data. Having access to a

relatively small, 10 mi.2, 3D seismic survey acquired in the early 1990’s and a number

of wells within the adjacent area, I reprocess the raw shot gathers with a more modern

seismic processing workflow. With careful attention to velocity analysis, techniques

for preserving the frequency content while mitigating noise, a newly developed prestack

Kirchhoff migration algorithm coupled with prestack structural-oriented filtering and an

abundance of time, I am able to improve the seismic image quality thus boosting the

interpretability of the data. The primary reflectors of interest are representative of

Pennsylvanian fluvial-deltaic sandstone and conglomerate and the Strawn formation

carbonate reefs. Given the relatively shallow depth of these reflectors and the expected

compaction, poststack inversion, AVAz analysis and direct hydrocarbon indicators

could prove to be valuable tools for the interpreter. Geometric attributes are used to

assist in the development of a stratigraphic and structural framework. Geobody

extraction is utilized for 3D mapping and visualization of fluvial-deltaic channels,

potential bypassed pay. I use poststack inversion to interpolate porosity and fluid

saturation measured at over 50 wells within the survey. With the renewed interest in

shallower targets in the Fort Worth Basin improved imaging techniques and the use of

modern interpretation tools will have great importance.

1

Chapter 1: Introduction

The Bend Arch-Fort Worth Basin Province is located in southwestern Oklahoma

and north-central Texas (Figure 1). Although a gray oil field, much light has been shed

on it by hydrocarbon exploration companies in recent years, with the resurgence of

drilling activity attributed to horizontal drilling and hydraulic fracturing. The basin has

a number of potential stacked plays ranging from the Ordovician Ellenburger to the late

Pennsylvanian Cisco group. With a seismic survey atop of the Bend Arch, in my thesis,

I reprocess this legacy data with the intent of increasing signal to noise ratio and also

preserving higher frequencies to allow for better imaging capabilities. Because of more

robust processing workflows and advancements in imaging algorithms, reprocessing of

legacy seismic data has proven an invaluable technique for re-investigating hydrocarbon

plays (Aisenberg, 2013). Upon completion of the reprocessing I compute seismic

attribute and poststack inversion volumes in order to identify and better map features

within the hydrocarbon bearing Strawn formation. Considerable work has been

performed over the attribute expression of the Barnett Shale and Ellenburger dolomite

in the Bend-Arch Fort Worth basin province identifying prospective locations and

geohazard recognition (Fernandez, 2013). However, little has been reported on the

Strawn and Cisco groups. Legacy data and reprocessed data attribute analysis and

interpretations are compared to ensure that significant improvements have been made.

This thesis begins with Chapter 2 where I provide an overview of the geology in

the area surrounding the Jean seismic survey. I begin by providing various maps and

satellite images allowing the reader to determine the survey’s approximate location.

Next, I move on to the regional geology and local geology, specific to the producing

2

intervals of the survey. Chapter 3 provides a general overview of the processing steps

and parameters used through 3D prestack Kirchhoff time migration of the seismic

survey. Chapter 4 includes a comparison of my results to the 3D seismic data processed

by a commercial vendor. Chapter 5 shows the results of a poststack inversion and

AVAz. I also provide an interpretation of the structural and stratigraphic framework

using geometric attributes. I conclude in, Chapter 6 summarizing the critical points

found within each chapter.

3

Objectives

The primary objective of this study is to reprocess a legacy 3D seismic data set

and bring it up to modern 3D seismic data quality. Upon completion of reprocessing,

seismic attributes, inversion and AVAz are used to map and identify sweet spots in the

Pennsylvanian units in the Jean survey of Young County, Texas.

- Processing Objectives:

Human Intensive Objectives

o Careful attention to trace editing

o Proper static corrections

o Detailed removal of linear noise

o Diligent velocity analysis

Computationally Intensive Objectives

o Prestack Kirchhoff time migration

o Prestack structure oriented filtering

o Matching pursuit normal moveout

- Interpretation Objectives:

Map key reflectors corresponding to producing lithological units

Use geometric attributes to understand morphology of structure and stratigraphy

o Dip and azimuth

o Coherency

o Curvature

Compute a poststack inversion volume to locate porosity and fluid saturation

Use amplitude variations with azimuth (AVAz) to identify anisotropic zones

Extract geobodies for 3D visualization of channels

4

Location

The data license used for this project was provided by Mike Burnett of

TameCAT llc., an independent oil and gas company in Norman, OK. The data

relinquished for this project included 2- 8 mm 5GB tapes containing poststack seismic

data each with different processing procedures applied by a commercial vendor, 2- 8

mm 5GB tapes including raw prestack seismic data to be reprocessed, 4 floppy disks

containing observer and field notes, 5 LAS logs and 60+ paper well logs from multiple

wells drilled within and adjacent to the seismic survey. The 5 LAS files contained p-

wave sonic logs while the paper logs, being older wells, only contained SP, gamma ray

and resistivity. There are no shear sonic logs in the area of the seismic survey.

The seismic survey, approximately 10 sq. miles in size, was acquired in Young

County, Texas. Figure 1 is a county map of Texas, Young County can be found in

north central Texas. Figure 2 is the location of the seismic survey on a satellite image.

5

Figure 1: County map of Texas. Young County is located in north central Texas

(blue arrow)

6

Figure 2: Satellite image of part of North America. Texas is outlined by the blue

polygon and the location of the seismic survey is denoted by the red star.

7

Chapter 2: Geological Background

Regional Geology

The seismic survey in this study lies on the eastern edge of Young County,

Texas. It is in a transition zone between the Fort Worth Basin and the Bend Arch,

located in North Central Texas and Southern Oklahoma. The surrounding regional

geological features around the Fort Worth Basin and Bend Arch consist of the Muenster

Arch, Ouachita Structural Belt and Llano Uplift . Figure 4 shows the structural

elements in the area around the Fort Worth Basin during the Late Paleozoic. These

structures were created during the collision of North Africa and North America during

the early to middle Paleozoic (Flippin, 1982).

The Fort Worth Basin is an asymmetrical peripheral wedge-shaped foreland

basin (Dickenson, 1976) and is approximately 15,000 mi2 in area (Montgomery, 2005).

The basin was created due to the advancing Ouachita structural belt during the

formation of Pangaea in the Late Paleozoic, when North Africa collided with North

America. In Figure 3 we see a cross-section through the Fort Worth Basin area during

the Late Paleozoic. In Figures 5 and 6 we see the evolution of the Fort Worth Basin and

surrounding structural elements as North Africa approaches and collides with North

America.

The Ouachita Structural Belt formed during the Late Paleozoic; however, it is

currently only found in the subsurface with the exception of southeastern Oklahoma,

northwest Arkansas and west Texas. This orogenic system is believed to be formed

similarly to the Appalachian Mountains in western North America, during the creation

of Pangaea.

8

The Bend Arch is a northward plunging broad anticlinal structure extending to

the north from the Llano Uplift and terminating in the regional syncline south of the

Red River Uplift (Evanoff, 1976). The Bend Arch was a relatively stable area during

the formation of the Fort Worth Basin to the east and the Permian Basin to the south

west.

Local Geology

The stratigraphy of the area around the survey entails rock units from Cambrian

to Permian age. Figure 7 shows the generalized stratigraphy for the Fort Worth Basin

and Bend Arch. The primary stratigraphic units of petroleum exploration are the

Desmoinesian Caddo limestone. In Figure 8, the approximate location of the survey is

marked by a red star on a Blakey reconstruction of the time of deposition of the Caddo

limestone. Reefing is common in the Caddo limestone along a North-South trend line

from Archer to Brown County. The Caddo limestone ranges from 100 ft to 800 ft and

can be correlated as far as the Permian Basin. Oil and gas are not only found in reefs

but also formations above the reefs due to the draping effect capturing upward

migrating hydrocarbons (Evanoff, 1976).

Sessions of upwarp and downwarp during the Ouachita Orogeny lead to several

erosional unconformities in the Bend Arch bedding. The most notable unconformity,

found at the top of the Ordovician Ellenburger, has Mississippian beds deposited on top

of it (Walper, 1982). Figure 9 shows the paleogeographic features of the Late

Paleozoic. Figure 10 shows a representative east to west geological cross section across

the Fort Worth Basin and Bend Arch; the red star represents the approximate location of

the seismic survey.

9

Figure 3: Cross section of Late Paleozoic trough through the Texas Craton and

Ouachita foldbelt. (Walper, 1982).

10

Figure 4: Map of Late Paleozoic’s structural elements in Texas and Oklahoma.

(Walper, 1982).

11

Figure 5: Paleogeographic reconstruction of the Fort Worth Basin and Bend Arch

(top) during the Late Cambrian and (bottom) during the Devonian (Walper,

1982).

12

Figure 6: Paleogeographic reconstruction of the Fort Worth Basin and Bend Arch

(top) during the Mississippian and (bottom) during the Pennsylvanian (Walper,

1982).

13

Figure 7: Stratigraphic column of the Bend Arch. Stratigraphic formations of

interest for this study are the Early Pennsylvanian Caddo Limestone

(Pollastro, 2007).

14

Figure 8: Paleographic map of North America during the Early Pennsylvanian. The

red star represents the approximate location of the study area (Blakey, 2011).

15

Figure 9 : (top) Structural provinces of Oklahoma and Texas. (bottom)

Paleographical map of the Fort Worth Basin (Thomas 2002).

16

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17

Chapter 3: 3D Prestack Seismic Processing

The goal in reprocessing the Jean survey, provided by Mike Burnett of

TameCAT LLC, was to use modern processing workflows to improve the data fidelity

beyond that provided by the commercial vendor in 1999. The original processing

focused on improving seismic resolution using well based data bluing factors. My goal

is to apply an amplitude friendly workflow that preconditions the data for more

quantitative interpretations, i.e. seismic inversion, than the commercial vendor.

Such improvement was accomplished with more time dedicated to statics,

velocity analysis, frequency preservation and noise removal and also having a more

geologically centered processing workflow in mind. Because of the amount of time

spent reprocessing this data set and the amount learned in doing so, this chapter of my

thesis is very dear (both affectionately and costly) to me and forms the bulk of my

effort.

The reprocessing workflow can be outlined as follows:

-Loading seismic data,

-Defining geometry and trace editing,

-Performing refraction and elevation statics,

-Suppressing linear noise,

-Picking velocities and calculating residual statics,

-Applying prestack time migration,

-Applying prestack structure oriented filtering and,

-Stacking the prestack time migrated gathers.

Figure 11 summarizes the above steps in the format of a workflow.

18

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ure

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.

19

Data Acquisition

The Jean survey was acquired and originally processed in 1999 by Western

Geophysical. Table 1 shows a detailed outline of the survey parameters. The source

was 5 kg of dynamite buried at 80 ft. The sources and receiver line spacing was 2200 ft

and the source and receiver interval was 220 ft. The acquisition follows a standard

rectilinear seismic survey array with the exception of small deviations due to surface

obstacles. Figure 12 shows the source and receiver deployment with shot points in red

and receiver stations in blue and green. The shot indicated by the red star is recorded by

the green receivers, defining the live receiver patch. For any shot the maximum

number of receiver lines turned on was 10. The signal was recorder for 3001 ms with a

1 ms sampling time leading to a Nyquist frequency of 500 Hz. The targets of interest

are relatively shallow, with the producing formations around 700 to 800 ms and

basement reflection at 1100 ms.

Survey Name: Jean 3D

Receiver Spacing: 220 ft

Receiver Line Spacing 1320 ft

Source Spacing: 220 ft

Source Line Spacing: 1320 ft

Source Type: Dynamite buried 80 ft.

Trace Length: 3000 ms

Sample Rate: 1 ms

Bin Size: 110 X 110 ft

Azimuth: 1 degree East of North

Number of Total Traces: 390,403

Nominal Fold: 17

Table 1: Survey parameters

20

Figure 12: Fold map of the Jean survey with source receiver array overlain. The

nominal fold is 17 with a maximum fold of 36. The survey has a rectilinear

brick pattern with a few irregularities due to surface obstacles.

21

Data Loading and Geometry

The first step to processing is loading the seismic data properly and checking the

geometry. Geometry is quality controlled by picking first breaks (later be used for

refraction statics) and defining the binning, azimuth and inline/crossline spacing. I

began with 2- 8mm 5 GB tapes containing raw prestack seismic data. The azimuth of

the survey was 1 degree east of north and the binning used was the natural binning, of

110 ft. After binning, the fold, azimuth of traces and offset can be computed (Figure

12). The nominal fold of the Jean survey is 17 with the maximum of 36. This relatively

low fold made improving the signal to noise ratio a priority if more quantitative

measurements were to be attempted. The seismic survey has relatively wide azimuth,

making AVAz a likely quantitative measurement. The offsets of the survey range from

0 to 14,000 ft. Figure 13 is representative of a typical patch in the shot domain. In the

patch, ten receiver lines can be seen with split spread, meaning the shot occurred near

the middle of the receiver line. The inset schematic in Figure 13 represents the

approximate location of the shot with respect to the receiver lines. Seismic reflectors

are shown by the green arrows where coherent noises, such as groundroll, are shown by

red arrows. With no noise removal or amplitude balancing, the reflectors are very

difficult to identify at this point in the processing flow.

Figure 14 is a representative shot sorted by offset with the first breaks in light

green. These first breaks are used for refraction statics, an essential procedure for

aligning reflectors and improving the frequency content after stacking the seismic data.

22

Fig

ure

13

: R

aw s

eism

ic d

ata

sort

ed i

n t

he

shot

do

mai

n.

Ther

e ar

e 10 r

ecei

ver

lin

es w

ith

th

e sh

ot

occ

urr

ing

bet

wee

n l

ine

5 a

nd l

ine

6. G

reen

arr

ow

s sh

ow

case

ref

lect

ors

and r

ed a

rro

ws

hig

hli

ght

gro

undro

ll.

23

Fig

ure

14

: R

aw s

eism

ic d

ata

sort

ed s

hot

ver

ses

off

set

dom

ain. T

he

gre

en l

ine

show

s th

e fi

rst

bre

ak p

icks,

use

d f

or

refr

acti

on s

tati

cs. T

he

ora

nge

arro

ws

hig

hli

ght

coher

ent

seis

mic

nois

e, s

uch

as

gro

undro

ll a

nd

hea

dw

aves

. T

he

blu

e ar

row

show

s a

seis

mic

ref

lect

ion.

24

Trace Editing

Trace editing was a very time consuming but crucial part of the processing for

this data set. With a nominal fold of 17, suppression of coherent and incoherent noise is

crucial. I could not rely on the power of the stack to attenuate noise from bad coupling

or broken geophones. Therefore, killing such bad traces before proceeding to other

processing steps is critical. To do so I had to manually inspect all trace in three

different sort domains, shot, receiver and not sort order. Figure 15 shows a

representative gather with no sort order, highlighting the typical removal of ‘bad’ traces.

Figure 15: Representative gather with no sort order. The red arrow points to a traces

that needs to be killed. The green arrow points to a once bad traces that has been killed

by the processor.

25

Elevation and Refraction Statics

Both elevation and refraction statics were computed and applied as part of the

processing procedure. The goal of elevation statics is to project the seismic data onto a

fixed datum, typically below the lowest part of the weathering zone. The aim of

refraction statics is to determine and then correct for the velocity and thickness of the

shallowest two layers in the substrate and to correct for the weathering zone (Russell

and Russell, 1989). Figure 16 shows a map of the source and receiver elevation the

elevation static corrections and the velocity for the first and second layer used in the

weathering zone correction. Figure 17 shows a gather sorted by common midpoint.

From this figure we can see the importance of proper statics corrections. Green arrows

indicate events that are better aligned after statics and consequently have greater

frequency content after stacking (Stein et al., 2009). Elevation statics and refraction

statics are important to correct for large scale undulations on a seismic survey array,

whereas residual statics are important for trace by trace corrections.

26

Fig

ure

16

: (F

rom

lef

t to

rig

ht)

Map

of

the

sourc

e re

ceiv

er e

levat

ion,

Layer

1 r

efra

ctio

n v

eloci

ty,

Layer

2

refr

acti

on v

eloci

ties

, pic

ked

fro

m f

irst

bre

ak p

icks

and l

ong w

avel

ength

ref

ract

ion

co

rrec

tio

ns.

27

Figure 17: A representative NMO corrected gather (left) before and (right) after

refraction and elevation statics. Arrows indicate events that are better aligned resulting

in better frequency content.

28

Deconvolution

The mathematical basis for deconvolution is well documented and routinely

used by most geophysicists (Cary, 2001). In my application, I use deconvolution to

compress the source wavelet to more accurately approximate the reflectivity spike

series. Shallow multiples form the weathering zone form part of the effective wavelet

and are also suppressed. The spectrum is flattened such that low frequency groundroll

is somewhat attenuated (Sheriff, 2004). Deconvolution can be time consuming, with

multiple parameters to test, including window length and area, operator length and

alternative pre-whitening factors.

Before deconvolution could be applied the data had to be scaled for geometric or

spherical divergence energy losses with depth. For the Jean survey a t-powered

exponential gain of 1.8 worked well. From my experimentation I found it important to

apply a t-gain before deconvolution before an additional surface consistent gain, and not

the other way around. Figure 18 shows the data before and after t-gain. Note that

reflectors in the deeper section that were originally not seen are now visible. However,

the amplitudes of groundroll are also more pronounced after correcting for spherical

divergence.

Upon completion of applying geometry, trace editing, refraction and elevation

statics and amplitude scaling for geometric attenuation I performed an initial

deconvolution. Because the data were acquired with dynamite as opposed to Vibroseis,

no phase conversion or matching needed to be performed because the data were already

minimum phase. This initial deconvolution involved a channel deconvolution as

opposed to a surface consistent deconvolution, the reason for this being that the

29

overbearing presence of ground roll had too great an affect while shaping the

deconvolution operator. Therefore, surface consistent deconvolution was not applied to

the data. The initial channel deconvolution was a spiking deconvolution, had an

operator length of 120 ms, a pre-whitening factor of 5% and was applied to the entire

trace. Figure 19 show the results before and after the initial channel deconvolution.

Take notice to the increase in the frequency content (Figure 19 inset image). The

resulting frequency spectrum is also flatter; therefore, spectral whitening or bluing was

not performed.

30

0

1500

Tim

e (m

s)

350

1100

Before Amplitude Gain After Amplitude Gain

Figure 18: (Left) Raw seismic data before correction for spherical divergence. (Right)

Seismic data after t-power gain correcting for spherical divergence. Deeper reflectors

are now more visible; however, groundroll is consequently enhanced.

31

Figure 19: Shot gather of the seismic data (top) before and (bottom) after spiking

deconvolution. The frequency spectrum is flattened increasing the contribution of

both low and high frequencies.

32

Velocity Analysis and Residual Statics

Velocity analysis is the most human intensive part of the processing workflow.

Figure 20 shows a representative semblance panel and NMO corrected CMP gather

from running the Interactive Velocity Analysis flow. Before I began velocity analysis I

first ran some initial conditioning. Since semblance represents reflectors by hyperbola,

velocity analysis is applied after gaining the data, spiking deconvolution, elevation

statics, refraction statics, and bandpass filtering. The bandpass applied for velocity

analysis was an Ormsby filter with corner frequencies of 17-22-80-95 Hz. I determined

that with this Ormsby I could eliminate large portions of noise and only keep reflectors

for velocity analysis. Subsequent processing will use a broader spectrum of the data.

Velocity analysis is an iterative process, having to be applied on finer and finer

grids until the residual statics converge, ideally to 0 ms. Residual static shifts that are

incorrect decrease the power of the CMP stack (Ronen and Claerbout, 1985). Having a

decreased CMP stack power is synonymous with lower resolution and destruction of

high frequency signals. The order for velocity analysis is as follows:

Compute semblance on a user defined grid,

Pick semblance panel that flatten seismic primary reflectors,

NMO correct seismic data with velocity field from picked semblance panel,

Compute residual statics, and

Stack NMO corrected gathers with the residual statics applied.

For my first pass of velocity analysis I used a 20x20 inline-crossline grid. I also

used a 10x10 super-gather, which essentially boosts the number of traces in an

33

ensemble while picking semblances by combining 100 CMP’s together. Supergathers

improve the signal to noise ratio of the semblance calculation but laterally smear

geology. Using this velocity field I flattened my seismic gathers using an NMO

correction and computed residual statics. After the first pass of velocity analysis the

residuals had converged to an RMS value of 3 ms, indicating that I needed to perform

additional iterations of velocity analysis. On the second pass of velocity analysis I used

a 10x10 inline-crossline grid. Upon completion of residual statics after this pass the

residuals had converged to less than an RMS value of 1 ms, which is acceptable for my

sampling increment of 1 ms. Figure 21 shows an inline of the velocity field after the

second iteration of velocity analysis; note that the velocity field is very smooth laterally.

Figure 22 shows a NMO corrected and stacked inline after the first pass of velocity

analysis. This stack was performed before deconvolution and noise suppression. The

effects of groundroll can be seen by the orange arrows. Figure 22 (middle) shows a

stack after deconvolution, F-K filtering and the second iteration of velocity analysis.

We can see greater frequency content and continuity of reflectors.

Upon completion of the prestack Kirchhoff migration, velocity analysis was run

again on the reverse normal moveout migrated gathers. Figure 22 (Bottom) shows a

stack after velocity analysis on migrated gathers. This is discussed later in more detail

under the Prestack Time Migration section of this thesis.

34

Figure 20: Semblance panel used for the second iteration of velocity

analysis. (Left) Semblance panel with picks used for NMO of the seismic

data. (Right) NMO corrected seismic gathers.

200

400

1000

Tim

e (m

s)

800

Velocity Offset

35

2500

2000

3000

18000

12000

6000 Velocity (ft/s)

1000

500

0 Crossline 0 90 30 60

Tim

e (m

s)

Figure 21: Inline of the velocity field picked after the second iteration of velocity

analysis. The velocities range from 5500 ft/s to 16,000 ft/s. The velocity field is

laterally smooth such that prestack Kirchhoff time migration will provide accurate

results.

36

Figure 22: Brute stacked seismic data after the first round of velocity analysis.

Orange arrows indicate groundroll while green arrows indicate reflectors. (Middle)

Brute stack after second round of velocity analysis, deconvolution and F-K filtering.

(Bottom) Brute stack after velocity analysis performed on migrated gathers.

Amplitude

37

Noise Suppression

Figure 23 shows the data and noise under a suite of frequency ranges. It is

evident from Figure 23 (right) that most of the Ground roll is below 20 Hz; however,

using a bandpass filter to reject this noise removal would significantly degrade

subsequent post stack inversion. Therefore, other options for linear noise removal were

examined. The first methodology was the AASPI coherent noise suppression purposed

by VERMA (2014). However, from my findings and those of Ha (2014) on a Texas

panhandle data volume, Verma’s AASPI coherent noise suppression workflow works

best for broad band noise. In my case, with the ground roll below 20 Hz, I decided to

implement the F-K filter for noise suppression. F-K filtering separates various types of

noise from signal based on their different moveout (Alvarez de la Hoz, 1995). Figure

24 shows the F-K spectrum of my data. Before an F-K transform is performed it is

quintessential that the lateral and vertical amplitudes of the seismic data be roughly

balanced. In my case I used exponential t-powered gain and surface consistent scaling.

F-K filtering can often be a very tedious process having to be applied for each type of

noise individually and for both the source and receiver domains (Vermeer, 2012).

The general steps for F-K noise removal include:

-Sorting the data in source versus absolute offset (Figure 25),

-Determining the spatial range of the noise and isolating it with a mute,

-Finding the frequency range of the noise within the muted area (Figure 26),

-Flattening the gathers with respect to moveout to avoid aliased noise and signal,

-Modeling the isolated area in the F-K domain (Figure 28),

38

-Unflattening F-K modeled noise (Figure 29),

-Subtracting the F-K modeled noise from the original data (Figure 30),

-Looping for all modes of noise (groundroll, headwaves and reverberations) and

-Repeating the flow for the data sorted by receiver verse absolute offset.

Table 2 shows the parameters used for F-K modeling the different types of noise:

Polygon Color Frequency Linear Moveout

Blue 0 - 40 Hz 10,480 ft/s

Yellow 0 - 22 Hz 8,755 ft/s

Green 0 - 25 Hz 6075 ft/s

Red 0 - 25 Hz No LMO Used Table 2: Parameters for F-K filtering

Figure 31 shows the seismic data sorted by shot (left) before F-K filtering and

(right) after F-K filtering. We can see that most of the groundroll is suppressed and

reflectors are more identifiable.

39

Fig

ure

23

: S

eism

ic d

ata

sort

ed s

hot

ver

ses

chan

nel

. A

n (

mid

dle

) O

rmsb

y b

andpas

s fi

lter

show

s m

ost

of

the

nois

e fa

lls

bel

ow

20 H

z. (r

ight)

By r

emovin

g t

he

bott

om

20 H

z m

ost

of

the

gro

undro

ll i

s su

ppre

ssed

. H

ow

ever

, si

gnif

ican

t si

gn

al

is a

lso r

ejec

ted m

akin

g a

n i

nver

sion a

nd q

uan

tita

tive

mea

sure

s m

ore

dif

ficu

lt.

Time (ms)

50

0

15

00

Sig

nal

Nois

e

Nois

e

40

Fig

ure

24

: (L

eft)

A c

om

mon r

ecei

ver

gat

her

sort

ed b

y f

ield

sta

tion n

um

ber

ver

sus

abso

lute

off

set

and (

Rig

ht)

the

corr

espondin

g F

-K s

pec

trum

. G

reen

arr

ow

s in

dic

ate

refl

ecto

rs,

yel

low

hea

dw

aves

and o

ran

ge

gro

undro

ll.

41

Fig

ure

25

: S

eism

ic d

ata

sort

ed b

y r

ecei

ver

ver

sus

abso

lute

off

set.

It

is

nec

essa

ry t

o h

ave

the

dat

a so

rted

in t

his

dom

ain

to p

erfo

rm F

-K f

ilte

rin

g.

A s

trong p

rese

nce

of

gro

und r

oll

is

easi

ly i

den

tifi

able

as

are

hea

dw

aves

an

d r

ever

ber

atio

ns.

Nois

e

Nois

e

42

Fig

ure

26

: S

eism

ic d

ata

sort

ed b

y f

ield

sta

tion n

um

ber

ver

ses

abso

lute

off

set

wit

h p

oly

go

ns

dra

wn

aro

un

d e

ach

mod

e of

nois

e th

at w

as r

emo

ved

ind

epen

den

tly. B

ecau

se F

-K f

ilte

ring o

per

ates

und

er t

he

assu

mpti

on o

f li

nea

r

mov

eout

and t

o a

vo

id a

lias

ing, th

e nois

e to

be

rem

oved

nee

ded

to b

e fl

atte

ned

to b

e ab

out

wav

enum

ber

zer

o.

43

Figure 27: (Top) Seismic data sorted by field station number versus absolute offset.

(Middle) Muted noise to be modeled in the F-K domain. The blue pick is defined as

t = 𝒓

𝑽𝑳𝑴𝑶 where r is the absolute offset and VLMO is the LMO velocity (blue).

(Bottom) LMO flattened noise.

44

Fig

ure

28

: F

-K s

pec

trum

s of

the

segm

ente

d a

nd b

and

pas

sed

gro

un

dro

ll.

45

Fig

ure

29

: F

irst

mode

of

gro

udn

roll

model

ed u

sing F

-K f

ilte

ring. T

he

freq

uen

cy c

onte

nt

is b

etw

een

0-2

5 H

z. T

he

bott

om

fig

ure

is

the

sam

e as

above

but

wit

h i

nver

sed l

inea

r m

oveo

ut

appli

ed. T

his

is

to b

e su

btr

acte

d f

rom

the

raw

dat

a to

rem

ove

this

mode

of

gro

undro

ll.

46

Fig

ure

30

: S

eism

ic d

ata

sort

ed f

ield

sta

tion n

um

ber

ver

ses

abso

lute

off

set.

T

he

top

im

age

is a

fter

the

firs

t m

ode

has

bee

n s

ubtr

acte

d f

rom

the

ori

gin

al s

eism

ic. T

he

bott

om

fig

ure

is

afte

r al

l fo

ur

F-K

model

ed m

odes

of

nois

e

def

ined

in T

able

2 h

ave

bee

n s

ubtr

acte

d.

Am

pli

tud

e

47

Figure 31: (left) Seismic data sorted shot versus channel. Before any noise

suppression has been undergone. The (right) same gather after all F-K modeled

modes of noise have been subtracted. The orange arrows indicate zones where

groundroll and coherent noise was overbearing the reflectors. The green arrows

indicate zones where the reflectors are now more visible.

48

Prestack Time Migration

Land seismic surveys often have irregularities in their acquisition. Because of this fact

and the relatively smooth velocity field, I performed a prestack Kirchhoff migration

described by Perez and Marfurt (2008). Seismic migration moves dipping events to

their correct locations and focuses the energy from diffractions. A good migration will

provide a higher resolution image and move out of plane reflections to their proper

location.

After suppression of coherent noise described in the previous section I ran my

first pass of migration. This migration used the velocity field that was created after the

second round of velocity analysis, on a 10x10 inline-crossline grid. Figure 32 shows

the results after migration (left) without a mute and (right) with a mute to remove any

reflectors that were stretched at far offsets. Figure 33 shows a stack of the migrated

data after muting the stretched offsets.

The next step was to reverse normal moveout (RNMO) the migrated data using

the velocity field used to migrate the seismic data. Upon completion of RNMO I

repicked the velocity field on the migrated data following the Deregowski loop (1990).

Figure 34 shows the reverse normal moveout gathers. Repicking velocities on the

migrated data led to greater clarity of events and better resolution of the now flattened

events. Figure 35 shows a semblance panel and normal moveout corrected seismic data

on the migrated data. Table 3 contains the parameters for the prestack Kirchhoff time

migration.

49

Am

pli

tud

e

Fig

ure

32

: S

eism

ic d

ata

(lef

t) a

fter

mig

rati

on a

nd (

right)

aft

er m

igra

tion w

ith f

ar o

ffse

t st

retc

hin

g m

ute

d.

50

Figure 33: Stacked seismic data (A to A’) after migration and the muting of

the stretched far offsets

Amplitude

51

Min.

Frequency

Max.

Frequency

No.

Offset

Bins

Max.

Offset

No.

Azimuths

Migration

Aperture

0 Hz 140 Hz 60 14000 ft. 8 7000 ft.

Table 3: Prestack Kirchhoff migration parameters

Figure 34: Result after reverse normal moveout (RNMO). The data needed to be

RNMO’d for velocity analysis on the migrated data and matching pursuit normal

moveout.

Offset 0 60 45 15

Tim

e (m

s)

0

1500

500

1000

Amplitude

52

Figure 35: Semblance panel used for the third iteration of velocity analysis

after migration. (Left) Semblance panel with picks used for NMO of the

seismic data. (Right) NMO corrected seismic gathers. The NMO corrected

gather has greater resolution than the NMO corrected gather during the

second iteration of velocity analysis.

53

Matching Pursuit Normal Moveout

Using a new velocity field picked on the migrated data we recomputed a

prestack Kirchhoff migration, using the same input seismic data as the first time. In

order to preserve the fidelity of far offset I performed matching pursuit normal moveout

(MPNMO). The purpose of MPNMO is to minimize the stretching and subsequent

decrease of high frequencies that occurs at far offsets during migration (Zhang, 2014).

Figure 36 (left) shows the output after MPNMOing the data. We can see when

compared to the results after migration (Figure 36 (right)) that the far offsets, especially

in the shallow section have been preserved. The result contains better frequency

resolution and gives the interpreter the ability to perform more quantitative measures,

such as AVO and inversion. Even after MPNMO some of the farthest offsets are still

not preserved. Because of this we still have to apply a mute to the far offset before we

can stack the seismic data.

54

Fig

ure

36

: (L

eft)

Res

ult

s af

ter

the

seco

nd p

ass

of

mig

rati

on. (

Rig

ht)

Res

ult

s af

ter

mat

chin

g p

urs

uit

norm

al m

oveo

ut.

N

oti

ce t

he

far

off

sets

are

bet

ter

pre

serv

ed l

eadin

g t

o b

ette

r fr

equ

ency c

onte

nt

wh

en s

tack

ed.

Am

pli

tud

e

55

Prestack Structure Oriented Filtering

The purpose of prestack structural-oriented filtering (PSOF) is to remove

incoherent noise that may be cross cutting reflectors (Davogustto, 2011). To do so we

must first stack the seismic data and compute dip, azimuth and similarity. I chose to

compute energy ratio similarity because the results are dependent on both the amplitude

and waveform of the traces in the analysis window. The next step is to use the prestack

structural oriented filter. The inputs for this program are the prestack migrated volume,

the inline and crossline dip and the similarity attribute. Within the AASPI software

there are four methods for computing prestack structural oriented filtering. After

running all of them I decided the Principle-component-filter gave the best results.

Principle-component filtering works by generating a covariance matrix based on

coherency values within a structural dip oriented analysis window. From this

covariance matrix the principle-components can be determined. With the first

principle-component being representative of most coherent part of the data, the seismic

reflectors, and the later component being representative of the least coherent parts of the

data, the noise. The first principle component is then kept while the other components

are rejected, resulting in the suppression of cross cutting incoherent noise.

Figure 37 (top) shows the rejected noise and (bottom) the results of prestack

structure oriented filtering.

56

Tim

e (m

s)

0

1500

500

1000

Offset 0 60 45 15

Offset 0 60 45 15

Tim

e (m

s)

0

1500

500

1000

Figure 37: (top) Rejected noise after prestack structural-oriented filtering using

Principle Components. (Bottom) Reflectors after Principal Component PSOF.

After PSOF incoherent noise has been removed from the migrated gather.

Amplitude

57

Chapter 4: Comparison to Legacy 1999 Vendor Volume

In order to ensure that significant improvements have been made by

reprocessing the Jean survey a seismic attribute comparison was made. Seismic

processing has not only made improvements in seismic image quality and attribute

image quality but also improved utility in common interpretation functions, such as

horizon autotracking.

The major advantage I possessed while reprocessing the Jean survey was an

understanding of the geology from the previous vendor processed seismic data.

Knowing the time of reflectors of interest allowed me to concentrate velocity analysis

and migration to that particular area, thus, giving an advantage over the commercial

vendor. Other advantages over the commercial vendor are recent technological

advancements, such as prestack structure-oriented filtering to remove incoherent noise

and matching pursuit non-stretch normal moveout to preserve far offsets. Furthermore,

understanding how previous processing steps hindered the subsequent attribute

interpretation, I attempted to bypass such steps to increase the utility of seismic

attributes and quantitative interpretation measures.

Data Comparison

The first step in comparing the vendor processed to the reprocessed is to check

the frequency spectrum. Figures 38 and 39 show a representative vertical slice through

the vendor processed and reprocessed data volumes, respectively. From Figure 38 it is

evident that the commercial vendor blued the seismic data up to 225 Hz during

processing, resulting in a ringing around the reflectors that does not appear geological.

The amplitudes have not been vertically balanced resulting in artifacts in the shallow

58

section. Furthermore, many key reflectors, such as the Strawn Unconformity at t = 800

ms and the basement at t = 1100 ms, are not easily identifiable. In Figure 39, through

the reprocessed data, the key reflectors are more easily identifiable. The frequency

spectrum has been whitened to 140 Hz resulting in a more geological image. In the

reprocessed data, the amplitudes were balanced vertically before migration resulting in

reflectors in the shallow section. Furthermore, the Strawn Unconformity and basement

are continuous and can be tracked with more ease.

Associated with a better seismic image, the performance of key seismic

interpretation tools such as the autotracker has been improved. Figures 40 and 41 show

the improvements on tracking reflectors in the shallow and deep portion of the survey,

respectively. In Figure 40 (left) we see the manual picks by the interpreter used for

autotracking in the shallow section. In the middle figure, we see the result of using the

autotracker in the vendor processed data with a quality factor of 0.7. Due to the high

frequencies added during bluing of the spectrum, areas of autotracking failure are

evident as holes in the horizon. In the right figure, we see the result of the autotracker

on the reprocessed seismic data with the same quality factor of 0.7. Using the

reprocessed data we see fewer holes in the horizon, thereby, improving the utility of the

autotracker. Figure 41 shows the improved utility of the autotracker on a deeper

seismic reflector, requiring more picks by the interpreter. The results are similar to the

shallower horizon, having fewer holes in the tracked horizon while using the

reprocessed seismic survey.

Figure 42 shows a timeslice at t = 540 ms through the seismic amplitude data

volumes processed by the vendor and reprocessed data. Due to the high frequency

59

noise created by bluing the spectrum, the (left) timeslice through the data processed by

the venodor possesses very incoherent or choppy amplitudes. In contrast, the (right)

timeslice through the reprocessed data is much more continuous because artificial high

frequencies were not generated during moderate spectral whitening.

Reprocessing of the Jean survey also resulted in greater utility from the

subsequent attribute interpretation. In Figure 43 we see a timeslice at t = 846 ms

through a coherence volume. Figure 44 shows a timeslice at t = 840 ms through the

curvature attribute of the vendor processed and reprocessed data. Coherency and

curvature, for both the vendor processed and reprocessed, were computed with the same

parameters. In the vendor processed (left) little geological detail can be gathered from

either attribute. However, on the reprocessed (right) data set we see both a coherency

anomaly and a most negative curvature anomaly that appears to be geological i.e. an

incised channel.

60

Figure 38: Vendor processed Jean seismic survey. The amplitudes in the shallow

section have not been properly balanced. Furthermore, the seismic data has been blued

up to 225 Hz causing a ringing around reflectors. The basement at 1100 ms is not easily

visible.

61

Figure 39: Reprocessed Jean survey. The amplitudes have been properly balanced in

the shallow section resulting in more identifiable reflectors. Furthermore, the frequency

spectrum has been whitened to 140 Hz resulting in a more geological result. The

basement at 1100 ms is easily identifiable.

62

Fig

ure

40

: S

hal

low

hori

zon t

rack

ed t

o s

how

the

impro

ved

per

form

ance

of

auto

trac

kin

g a

fter

rep

roce

ssin

g. (L

eft)

Pic

ks

per

form

ed m

anual

ly b

y t

he

seis

mic

inte

rpre

ter.

(M

idd

le)

Auto

trac

ked

ho

rizo

n u

sin

g t

he

ven

do

r pro

cess

ed J

ean s

urv

ey

wit

h a

qual

ity f

acto

r of

0.7

. (

Rig

ht)

Auto

trac

ked

hori

zon u

sing t

he

repro

cess

ed J

ean s

urv

ey w

ith a

qual

ity f

acto

r of

0.7

.

63

Fig

ure

41

: D

eeper

ho

rizo

n t

rack

ed t

o s

how

the

impro

ved

per

form

ance

of

auto

trac

kin

g a

fter

rep

roce

ssin

g. (L

eft)

Pic

ks

per

form

ed m

anual

ly b

y t

he

seis

mic

inte

rpre

ter.

(M

idd

le)

Auto

trac

ked

ho

rizo

n u

sing t

he

ven

dor

pro

cess

ed J

ean s

urv

ey

wit

h a

qual

ity f

acto

r of

0.5

5. (

Rig

ht)

Au

totr

acked

hori

zon u

sing t

he

repro

cess

ed J

ean s

urv

ey w

ith a

qual

ity f

acto

r of

0.5

5.

64

Figure 42: Timeslice through seismic amplitude at t = 540 ms. (left) Vendor processed

seismic data. (right) Reprocessed seismic data. The reprocessed seismic data has

greater continuity due to the lack of high frequencies cross cutting the timeslices.

A A’

Amp.

Amplitude

65

Figure 43: Timeslice at t = 846 ms through a coherency volume. (Upper left) Vendor

processed coherence volume. (Upper right) Reprocessed data coherence volume.

A A’

Amplitude

Coh.

66

Figure 44: Timeslice through most negative curvature at t = 840 ms. (left) Vendor

processed most negative curvature. (right) Reprocessed most negative curvature. The

reprocessed seismic data has greater continuity due to the lack of high frequencies cross

cutting the timeslices.

A A’

Curv.

Amplitude

67

Chapter 5: Interpretation, Inversion and AVAz

Upon completion of an iteration of the Deregowski loop, prestack Kirchhoff

migration into eight azimuthally migrated volumes, reverse normal moveout, matching

pursuit non-stretch normal moveout and prestack structure oriented filtering the data

were stacked for attribute, inversion and AVAz interpretation.

Structural and Stratigraphic Interpretation

With the reprocessed Jean survey stacked I loaded the seismic data set into a

commercial software package to generate a synthetic seismogram and tie the seismic

data to the well logs. Wells ties are essential in order to know which seismic reflectors

correlate to a lithological unit. Using the well ties I was able to track key stratigraphic

horizons across the Jean survey. Figure 45 is an E-W vertical section through the

reprocessed Jean survey with interpreted horizons. The Upper Caddo occurs at

approximately t = 800 ms and the basement reflection occurs at approximately t = 1100

ms. Figure is a N-S vertical section through the reprocessed Jean survey.

Figures 47, 48 and 49 show the time structure maps of the horizons shown in

Figure 45 and 46. In Figure 47 (left) we see the Palo Pinto horizon. It is heavily

contaminated with acquisition footprint, which could be misinterpreted as geology. In

Appendix A the source of the acquisition footprint in the Jean survey is examined via

seismic modeling. In Figure 48 (left) we see the Upper Caddo. A structural high can be

found in the southeast corner (white arrow). This structural high is interpreted to be a

reef or draping caused by reefing in a lower formation. Figure 49 (right) is a time

structure map of the Middle Caddo horizon. In this figure a narrow, sinuous structural

68

low can be seen (white arrow). This is interpreted to be a potential channel cross

cutting the limestone Caddo formation (Figure 61).

Figure 50 (left) shows the thickness of the Strawn Unconformity generated

subtracting the top of the Strawn Unconformity from the top of the Upper Caddo. The

sediment of the unconformity thickens to the northeast, following the overall trend of

the Bend Arch. Figure 50 (right) shows the thickness of the formations from the Upper

Caddo to the Basement. From this figure it is evident that these units thicken in the

southeast corner of the seismic survey, around the interpreted location of the Caddo

reef.

Geometric Attributes

With the stacked reprocessed Jean survey a suite of geometric attributes was

generated. These attributes include dip and azimuth, most positive, most negative

curvature, k1 and k2 curvature, Sobel filter similarity and energy ratio similarity.

Curvature was the most useful attribute during interpretation, allowing for the extraction

of subtle morphologies along horizons and timeslices (Roberts, 2001). Using these

attributes I was able to better understand the expression and further strengthen my

interpretation of key stratigraphic features. Figure 51 shows a timeslice at t = 420 ms

through the (left) most negative curvature and (right) most positive curvature.

Undulations can be seen in the curvature responses that are attributed to acquisition

footprint and not geology. Appendix A shows the source of the acquisition footprint in

the Jean survey to be due to residual groundroll not properly removed by f-k filtering.

Figure 52 (left) shows co-rendered, k1 and k2 curvature and (right) co-rendered k1 and

69

k2 curvature and Sobel filter similarity extracted on the Middle Caddo formation. A

white arrow indicates a potential channel incised through this horizon.

Post stack Acoustic Impedance Inversion

Due to the absence of a shear sonic log within the survey or in a near offset well

only a post stack inversion could be computed, instead of a prestack inversion. To

perform the post stack inversion I generated synthetic seismograms and well ties for 5

wells within the survey that had P-sonic well logs. Because no density logs were

available, density was estimated from Gardner’s equation (1974). Figures 53 and 54

show synthetic seismograms and well ties for two wells within the Jean survey. A

correlation coefficient of 0.669 and 0.637 was achieved for the two wells displayed.

After the 5 wells were tied a model based post stack acoustic impedance

inversion was ran. Figure 55 shows a representative vertical section through Well #1.

With the acoustic impedance from the well logs projected on the wellbore, the inversion

through the seismic data appears to match. Figure 56 (left) shows the acoustic

impedance inversion extracted on the Upper Caddo and (right) low acoustic impedance

co-rendered on the time structure map. From this figure it appears that low impedance

values correlate to structural lows on the Upper Caddo horizon. Figure 57 (left) shows

the acoustic impedance extracted along the Middle Caddo and (right) low acoustic

impedance co-rendered on the time structure map. Figure 58 shows k1 and k2 curvature

co-rendered with low acoustic impedance on the (left) Upper Caddo and (right) Middle

Caddo.

70

Amplitude Variations with Azimuth

In order to detect any anisotropy I computed amplitude variations with azimuth

or AVAz. In order to perform AVAz the seismic data has to be azimuthally migrated. I

chose to migrate the Jean survey into 8 azimuths. After migration the seismic data

could then be stacked, each azimuthal volume individually, and flattened upon a

reference horizon. Next AVAz can be computed by cross-correlating the same trace

from each stacked azimuthally migrated volume.

Figure 59 shows a timeslice at t = 750 ms through (left) azimuth of the AVAz

program and (right) azimuth co-rendered with anisotropy density. There is strong

anisotropy at 90 degrees in the southern portion of the survey. Figure 60 shows a

timeslice at t = 960 ms through (left) azimuth of the AVAz program and (right) azimuth

co-rendered with anisotropy density.

Geobody Extraction

For better visualization of the potential channel in the Middle Caddo indicated

by curvature and coherence I used the method proposed by Wallet (2014). Using this

method allows for three dimensional interpretation of the channel by thresholding k2

curvature and then extracting adjacent voxels with similar values.

Figure 61shows a 3D display with the vertical section being seismic amplitude

and the timeslice at t = 860 ms being K2 curvature through the Middle Caddo. The

channel in the Middle Caddo is indicated by the arrow. Figure 62 shows a 3D chair

display of the channel geobody extracted by thresholding k2 curvature co-rendered with

the Middle Caddo time structure map.

71

Fig

ure

45

: L

ine

C-C

’ sh

ow

ing k

ey h

ori

zons.

A-A’

72

Fig

ure

46:

Lin

e fr

om

A -

A’

show

ing k

ey h

ori

zons.

C-C’

73

Fig

ure

47

: (l

eft)

Tim

e st

ruct

ure

map

of

the

Pal

o P

into

str

atig

raphic

ho

rizo

n t

rack

ed t

hro

ugh

the

repro

cess

ed J

ean s

urv

ey. W

hit

e ar

row

s in

dic

ate

area

s of

the

ho

rizo

n c

onta

min

ated

by

acquis

itio

n f

ootp

rint.

T

he

cause

s o

f th

is f

ootp

rint

wil

l be

inves

tigat

ed f

urt

her

in A

ppen

dix

A.

(rig

ht)

Tim

e st

ruct

ure

map

of

the

KM

A s

trat

igra

ph

ic h

ori

zon t

rack

ed t

hro

ugh t

he

repro

cess

ed

Jean

surv

ey.

74

Fig

ure

48

: (l

eft)

Tim

e st

ruct

ure

map

of

the

Upper

Cad

do s

trat

igra

phic

hori

zon t

rack

ed t

hro

ugh

the

repro

cess

ed J

ean

surv

ey. T

he

whit

e ar

row

indic

ates

are

as o

f th

e hori

zon c

onta

inin

g

dra

pin

g o

ver

a c

arbonat

e re

ef. R

eefs

and d

rapin

g d

ue

to d

eposi

tion o

n t

op o

f re

efs

are

com

mon

com

mon o

il a

nd g

as e

xplo

rati

on t

arget

s (r

ight)

. T

ime

stru

cture

map

of

the

Bas

emen

t h

ori

zon

trac

ked

thro

ugh t

he

rep

roce

ssed

Jea

n s

urv

ey.

75

Fig

ure

49

: (l

eft)

Tim

e st

ruct

ure

map

of

the

Mis

siss

ippia

n s

trat

igra

phic

hori

zon t

rack

ed

thro

ugh t

he

repro

cess

ed J

ean s

urv

ey.

(R

ight)

Tim

e st

ruct

ure

map

of

the

Mid

dle

Cad

do

stra

tigra

phic

hori

zon t

rack

ed t

hro

ugh t

he

rep

roce

ssed

Jea

n s

urv

ey. T

he

wh

ite

arro

w

indic

ates

a s

truct

ura

lly l

ow

are

a in

the

hori

zon t

hat

is

a pote

nti

al c

han

nel

cro

ss c

utt

ing t

he

carb

onat

e S

traw

n f

orm

atio

n.

76

Fig

ure

50

: (l

eft)

Thic

knes

s m

ap c

onst

ruct

ed b

y s

ubtr

acti

ng t

he

Str

awn

Un

con

form

ity s

trat

igra

ph

ic

hori

zon f

rom

the

Upper

Cad

do s

trat

igra

phic

hori

zon.

The

unco

nfo

rmit

y t

hic

ken

s as

we

trav

erse

to

the

nort

hea

st,

coin

cidin

g w

ith t

he

tren

d o

f th

e B

end A

rch.

(R

ight)

Thic

knes

s m

ap c

onst

ruct

ed f

rom

subtr

acti

ng t

he

Upper

Cad

do f

rom

the

Bas

emen

t st

rati

gra

phic

hori

zon.

77

Fig

ure

51

: (l

eft)

Tim

esli

ce t

hro

ugh m

ost

neg

ativ

e cu

rvat

ure

at

t =

420 m

s. (

Rig

ht)

Tim

eles

ice

thro

ugh m

ost

posi

tive

curv

ature

at

t =

420 m

s. T

he

curv

ature

anom

alie

s in

dic

ated

by t

he

whit

e ar

row

s ar

e in

terp

rete

d t

o b

e

acquis

itio

n f

ootp

rint

over

pri

nti

ng g

eolo

gy. I

n A

pp

endix

A,

I in

ves

tigat

e th

e ca

use

of

footp

rint

in t

he

Jean

surv

ey

usi

ng s

eism

ic m

odel

ing.

78

Fig

ure

52

: (l

eft)

Co

-ren

der

ed K

1 a

nd K

2 c

urv

ature

ex

trac

ted a

lon

g t

he

Mid

dle

Cad

do s

trat

igra

phic

hori

zon. (

Rig

ht)

Co

-

rend

ered

K1 a

nd

K2 c

urv

ature

and e

ner

gy r

atio

sim

ilar

ity c

oher

ence

att

rib

ute

ex

trac

ted a

lon

g t

he

Mid

dle

Cad

do

stra

tigra

phic

hori

zon. T

he

whit

e ar

row

indic

ates

a p

ote

nti

al i

nci

sed c

han

nel

.

79

Fig

ure

53

: W

ell

tie

and s

yn

thet

ic s

eism

ogra

m f

or

Wel

l #1 i

n t

he

Jean

surv

ey.

For

the

bes

t co

rrel

atio

n b

etw

een

the

seis

mic

and t

he

wel

l lo

gs,

the

stat

isti

cal

wav

elet

was

rota

ted t

o -

17

0 d

egre

es. W

ith n

o d

ensi

ty l

og a

vai

lable

,

one

was

est

imat

ed u

sin

g t

he

Gar

dner

’s e

qu

atio

n (

1974).

80

Fig

ure

54

: W

ell

tie

and s

yn

thet

ic s

eism

ogra

m f

or

Wel

l #2 i

n t

he

Jean

su

rvey

. F

or

the

bes

t co

rrel

atio

n

bet

wee

n t

he

seis

mic

and t

he

wel

l lo

gs,

the

stat

isti

cal

wav

elet

was

ro

tate

d t

o -

17

0 d

egre

es. W

ith

no

den

sity

lo

g a

vai

lable

, one

was

est

imat

ed u

sing t

he

Gar

dner

’s e

qu

atio

n (

197

4).

81

Fig

ure

55

: V

erti

cal

sect

ion t

hro

ugh t

he

acoust

ic i

mped

ance

inv

ersi

on v

olu

me.

T

he

sect

ion b

isec

ts

Wel

l #1 w

hic

h h

as t

he

acoust

ic i

mped

ance

fro

m t

he

wel

l lo

gs

pro

ject

ed u

po

n i

t. T

he

imped

ance

from

the

wel

l to

the

imped

ance

gen

erat

ed f

rom

the

seis

mic

hav

e a

go

od

co

rrel

atio

n.

82

Fig

ure

56

: (l

eft)

Aco

ust

ic i

mped

ance

ex

trac

ted a

long t

he

Upp

er C

addo h

ori

zon. T

he

Upper

Cad

do i

s a

lim

esto

ne

resu

ltin

g i

n h

igh a

coust

ic i

mped

ance

s. (R

ight)

A

coust

ic i

mp

edan

ce c

o-r

end

ered

wit

h t

he

Upper

Cad

do t

ime

stru

cture

map

. T

he

maj

ori

ty o

f th

e lo

w i

mped

ance

are

as c

orr

elat

e to

low

str

uct

ura

l re

lief

zones

.

83

Fig

ure

57

: (l

eft)

Aco

ust

ic i

mped

ance

ex

trac

ted a

long t

he

Mid

dle

Cad

do h

ori

zon. T

he

Upper

Cad

do i

s

a li

mes

tone

resu

ltin

g i

n h

igh a

coust

ic i

mped

ance

s. (R

ight)

A

coust

ic i

mped

ance

co

-ren

der

ed w

ith t

he

Mid

dle

Cad

do t

ime

stru

cture

map

. L

ow

im

ped

ance

app

ears

to b

e fo

und a

long t

he

chan

nel

fea

ture

see

n

in t

his

hori

zon a

nd t

he

carb

onat

e re

ef i

n t

he

south

east

corn

er o

f th

e su

rvey.

84

Fig

ure

58

: (l

eft)

Aco

ust

ic i

mped

ance

co

-ren

der

ed w

ith K

1 a

nd K

2 c

urv

atu

re e

xtr

acte

d a

lon

g

the

Upper

Cad

do h

ori

zon. (

Rig

ht)

A

coust

ic i

mped

ance

co

-ren

der

ed w

ith K

1 a

nd K

2 c

urv

ature

extr

acte

d a

lon

g t

he

Mid

dle

Cad

do h

ori

zon.

85

Fig

ure

59

: (l

eft)

Tim

esli

ce a

t t

= 7

50 m

s th

rou

gh t

he

anis

otr

op

y a

zim

uth

volu

me.

(R

ight)

Co-r

ender

ed a

nis

otr

op

y a

zim

uth

and d

ensi

ty.

86

Fig

ure

60

: T

imes

lice

at

t =

960 m

s th

rough t

he

anis

otr

op

y a

zim

uth

volu

me.

(R

ight)

C

o-r

ender

ed

anis

otr

op

y a

zim

uth

and d

ensi

ty.

87

Fig

ure

61

: C

hai

r dis

pla

y f

rom

A t

o A

’.

Ver

tica

l se

ctio

n i

s se

ism

ic a

mp

litu

de

and

tim

esli

ce i

s K

2

curv

ature

. T

he

arro

w i

nd

icat

es a

pote

nti

al c

han

nel

in t

he

Mid

dle

Cad

do t

hat

is

extr

acte

d u

sing g

eobo

die

s.

88

Fig

ure

62

: C

hai

r dis

pla

y s

how

ing g

eobod

y e

xtr

acti

on o

f ch

annel

aft

er W

alle

t (2

014).

T

he

ver

tica

l se

ctio

ns

are

seis

mic

am

pli

tude

and t

he

hori

zonta

l sl

ice

is t

he

Mid

dle

Cad

do t

ime

stru

ctu

re m

ap. T

he

chan

nel

geo

bod

y e

xtr

acte

d b

y t

hre

shold

ing K

2 c

urv

atu

re i

s sh

ow

n i

n r

ed.

89

Chapter 6: Conclusion

Reprocessing the legacy Jean 3D seismic surveys in Young County, Texas can

provide new insight into the geology within the survey. Utilizing modern processing

workflows, along with careful attention to trace editing, statics and velocity analysis and

advancements in processing and imaging technology such as prestack Kirchhoff

migration, non-stretch NMO, and prestack structure oriented filtering can improve the

subsequent interpretation. Furthermore, reprocessing with a focus on preserving the

data for quantitative interpretation will increase the amount of information that can be

gained from the 3D seismic data volume.

I have shown that by not bluing the frequency spectrum, as the commercial

vendor had done, we preserve the frequency content of the data allowing for subsequent

attribute and quantitative interpretation. Curvature and coherence attributes show

geology after reprocessing where as previously the only showed high frequency noise.

Key reflectors, such as the Caddo limestone and the basement, are easier to interpret

and quantitative measures, such as AVAz and acoustic impedance inversion can be

utilized.

With the quantitative interpretation of the reprocessed data, we are able to

identify a previously unseen channel in the Caddo formation. We are also able to detect

low impedance zones trending with the channel, interpreted to be channel sands.

90

References

Aisenberg, M., 2013, the value of reprocessing legacy data: A case study of Bois D’Arc

a Mississippi play in northeastern Oklahoma: Master’s Thesis, University of Oklahoma,

Norman, Oklahoma.

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93

Appendix A: Understanding processing pitfalls with seismic modeling

The following appendix is part of two expanded abstracts submitted for the 85th Annual

SEG meeting. My co-authors on these abstracts are Sumit Verma and Bryce

Hutchinson.

Pitfalls in seismic processing: part 1 groundroll sourced acquisition footprint

Summary

Whether it is in reference to the limitations of interpretation or associated with

seismic processing, usage of the phrase acquisition footprint is never in a positive

context. Footprint contaminates both time structure map and impedance inversion.

Although common, footprint is often poorly understood. Footprint is more common in

older, lower fold surveys. Part of this mystery is due to the division of labor in most

exploratory companies. Processing is usually conducted by specialists in a service

company, while attribute analysis is conducted by interpreters (often geologists) in an

oil company. Often, younger interpreters have never processed 3D seismic data, while

younger processors have never analyzed attributes. As a part of a reprocessing effort

for quantitative interpretation analysis, Cahoj (2015) encountered severe footprint

masking his shallow exploration target. We attempt to modify his processing workflow

to ameliorate the footprint lead to an effort to understand its cause, at least for this

survey. Upon completion of seismic processing we are left with a stacked version of our

synthetic data in which we can compute seismic attributes. We show that the

subsequent attribute interpretation is greatly affected by footprint caused by residual

groundroll. Lastly, we show an attribute interpretation corresponding to real 3D seismic

dataset and conclude that many artifacts seen in the dataset, often labeled under the

94

broad category of acquisition footprint, are actually residual groundroll not properly

removed during the processing flow. Because out of plane groundroll can have

hyperbolic moveout common noise removal techniques, such as F-K filtering, that

operate under the assumption of modeling noise with different linear moveouts, fail.

Introduction

Acquisition footprint refers to the imprint of acquisition geometry seen on

seismic amplitude timeslices and horizons. Acquisition footprint can obstruct not only

classical seismic interpretation but also affect interpretation based on seismic attributes

(Marfurt and Alves 2015, Marfurt et al., 1998). Seismic attributes, especially coherence

and curvature, often exacerbate the effect of footprint making their utility diminish

(Marfurt and Alves 2015; Verma et al., 2014).

With footprint being such a common problem its occurrence and formation are often

poorly understood (Chopra and Larsen, 2000). Although many methodologies have

been developed to remove linear coherent noise and acquisition footprint (Cvetkovic et

al., 2008 and Marfurt et al., 1998), little has been done in the way of illustrating its

occurrence via modeling. Hill et al. (1999) investigated acquisition footprint is caused

by inaccurately picked NMO velocity. Although groundroll is one of the prime causes

of acquisition footprint, the footprint pattern caused by the presence of groundroll has

not been modeled and documented.

One of the main causes of seismic acquisition footprint is sparse spatial sampling. It is

particularly challenging to remove aliased groundroll. Because of this the residual

groundroll’s occurrence on the stacked seismic data can be strong enough to influence

the interpretation. We study a low fold legacy seismic survey of North Central Texas

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and observed acquisition footprint with the North-South lineaments (Figure 1a) aligned

with the receiver lines. We investigate what can cause such footprint to be present in

our dataset; in this paper we present the findings.

Motivation

We observed north–south acquisition footprint present on the curvature attribute

shown in Figure 1a. The presence of this acquisition footprint hindered our attribute

assisted interpretation. Because of this we had an incentive to understand its origin. We

hypothesis that this acquisition footprint could have three potential sources:

1) Inadequate removal of groundroll,

2) NMO far offset stretch, and

3) Improper velocity analysis

In this paper we decide to investigate the effect of inadequately removed groundroll.

In Part 2 (Cahoj et al., 2015) of this abstract we will try to understand the effect of

NMO stretching and incorrect velocity analysis on our seismic interpretation. Equipped

with an actual seismic dataset with acquisition footprint, we are able to construct a

synthetic analogue.

Methodology

Seismic modeling

The objective of this model is to see the effect of residual groundroll on stacked

seismic data after processing and its relation with reflectors.

To do so we created a simple 3D flat layer seismic model with four layers. The

acquisition geometry is shown in Figure 2, with 6 receiver lines and 9 shot lines. Each

receiver line contains 60 receiver groups totaling 360 geophones, and each shot line

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contains 18 sources totaling 162 shots. The model has a strong presence of broad

bandwidth (0-50Hz) dispersive groundroll. We generated two separate models, one for

groundroll using an elastic modeling approach with only the weathering layers and a

second model with four layers using an acoustic modeling approach. We added these

two models to simulate the final 3D acquisition geometry for our study.

Seismic processing

The seismic processing can be broken into 7 steps.

1) Importing the synthetic seismic data

2) Defining the geometry

3) Sorting the data by absolute offset

4) Identifying the noise corridor with a mute and finding its respective linear

moveout velocity

5) Model the noise in the F-K domain

6) Inverse linear moveout and subtraction

7) NMO correction and stacking the synthetic data

Figure 3a shows a common shot the synthetic sorted by absolute offset. It is easy to

identify the lower velocity groundroll crosscutting and overbearing the reflectors.

Figure 3b shows the groundroll modeled by a standard F-K noise filtering procedure

and Figure 3c shows the results after the modeled groundroll is subtracted from the

input model. In this figure we see that most of the high amplitude groundroll has been

removed and the reflectors, once overprinted, are now visible. Upon completion of

groundroll removal the synthetic data were NMO corrected and stacked (Figure 4a).

Attribute interpretation

97

We computed a suite of seismic attributes using a commercial software package

on both the modeled synthetic seismic data and the actual seismic data. Such attributes

included dip and azimuth, energy ratio similarity and curvature. With these attributes

we were able to determine footprint’s response from improperly removed groundroll.

Using the modeled seismic data we were able to make an analogue to actual seismic

data to compare groundroll’s response and effect on interpretation.

Results

Figure 4a shows the inline of the stacked synthetic seismic data. The

undulations in the shallow section are the responses of constructively and destructively

interfering groundroll not properly removed by F-K filtering. Figure 4b shows the

corresponding inline through the actual seismic data. It is evident that similar

undulations exist in the shallow section of the real seismic data.

Figure 5a is a timeslice at t=1.320s through the most negative curvature

response of the stacked synthetic seismic data. We find that the response of curvature,

an attribute commonly used to map folds, flexures and deformation about faults, is

greatly contaminated by the inadequately removed groundroll. Figure 1a shows the

corresponding timeslice at t=0.410s through the most negative curvature of the real

seismic data; containing a similar footprint expression.

Figure 6a shows a horizon tracked through the 2nd layer in the synthetic dataset.

Because the layers were modeled to be horizontal we expect a uniform surface at a

constant depth. However, we can see rectilinear features, particularly strong in the

East-West direction. These features can also be seen in Figure 6b, the real seismic data.

Conclusions

98

Our analysis indicates that the undulations caused by residual groundroll will be

present on the seismic, having strongest amplitude near the surface and attenuating with

depth.

We conclude that inadequately removing groundroll can result in erroneous and

more difficult interpretations. Furthermore, seismic attributes, often used by less

experienced interpreters to accelerate there interpretations, are not immune to

acquisition footprint caused by groundroll. In many cases, seismic attributes exacerbate

the effects of this noise.

99

4000ft

4000ft

Figure A-2: Timeslice at t=0.41s through coherence

volume from real seismic dataset. The North-South

lineaments are aligned with the receiver lines. These

artifacts are weaker at depth but overprint the

objective at t=1.0s.

Figure A-1: Timeslice at t=0.41s through most

negative curvature volume from real seismic dataset.

The North-South lineaments are aligned with the

receiver lines. These artifacts contaminate attribute

volumes.

100

0 2000 12000

6000

10000 East-West (ft.)

No

rth

-So

uth

(ft.

)

1000

Figure A-3: The synthetic model’s geometry. Sources are in red and

receivers are in green. The geometry is perfectly rectilinear which is

not the case with actual seismic data due to surface obstructions.

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Figure 3. Shot vs absolute offset sorted (a) modeled seismic data

with four reflectors and groundroll with a large bandwidth (0-50Hz).

b) F-K modeled groundroll to be removed from the modeled seismic

data (a). (c) Result of subtracting F-K modeled groundroll (b) from

modeled seismic (a). Notice large amounts and high amplitude

groundroll is removed, but residual remains.

Positive

Negative

Figure A-4:

102

0.5

0.0

1.0

1.5

2.0

Time (s)

0.45

0.1

0.25

a)

b)

Figure A-5: (a) Inline through the synthetic seismic data.

(green horizon is displayed in Figure 6a) ( b) Inline of

real seismic data (yellow horizon is displayed in Figure

6b) . Notice the undulation anomalies caused by

inadequately removed groundroll in Figure 6a and similar

undulation features can be seen in Figure 6b most likely

caused by groundroll.

103

a)

b)

Figure A-6: (a) Timeslice at t=1.320s through most negative curvature of

the synthetic seismic data. Notice the undulation anomalies caused by

inadequately removed groundroll. (b) Coherence at t=1.320s of the

synthetic seismic data. Similar undulation features can be seen causing

lateral discontinuity in the reflectors.

104

4000ft

a)

b)

Figure A-7: Horizons tracked through (a) synthetic data, displayed on

Figure 4a as green horizon. (b) real seismic data, displayed on Figure 4a

as yellow horizon. The linear striations (red arrows) are due to

residual groundroll overprinting P-wave reflections.

105

Pitfalls in seismic processing: part 2 velocity analysis sourced acquisition footprint

Summary

Seismic interpretations are often highly subjective and depend on the

imagination of the geologist or geophysicist constructing them. Because of this nature

an interpretation can be no better than the data under investigation or the interpreter’s

ability to understand and delineate features created from the processing procedure. If

acquired improperly or processed poorly the geological interpretation will have

shortcomings as well. Such processing errors could be the product of improper statics,

poor velocity analysis and not adequately removing coherent noise. The result of these

errors, if not mistaken as geology, is often classified under the broad category of

acquisition footprint.

In this paper we use synthetic seismic data composed of four reflectors to

investigate the effects of poor velocity analysis and normal moveout stretching. We

compare the results from the synthetic seismic dataset to a real 3D seismic dataset. We

show an attribute interpretation of both datasets and how inaccurate processing can lead

to fallacious claims about the geological background.

Introduction

Processing procedures can greatly affect the reliability of conventional

interpretation and the utility of seismic attribute interpretation. Because of this seismic

modeling can be an invaluable tool in quality checking your seismic processing job.

Very few efforts have been made to explain acquisition footprint and processing

generated noise using synthetic models. Hill et al. (1999) discussed acquisition footprint

caused by inaccurately picked NMO velocity. Ha (2014) used seismic modeling in an

106

attempt to better understand the response of a fractured granitic basement. He also used

elastic modeling to identify coherent seismic noise, such as groundroll. With the insight

gained from seismic modeling he was able to better identify and eliminate coherent

noise during seismic processing.

Seismic attributes, especially coherence and curvature, often exacerbate the

effects of inaccurate processing procedures (Verma et al. 2014; Marfurt and Alves,

2015). Because attributes are popular, particularly among younger interpreters, as a

method to hasten interpretations this could lead to pitfalls in our geologic model

(Marfurt and Alves, 2015).

One of the key factors affecting the resolution of seismic data is velocity

analysis. With improper velocity analysis and without muting NMO stretch the

frequency content of the reflectors can be greatly deteriorated. Furthermore, it can

create pseudo-geological artifacts that could lead to an incorrect interpretation. Using a

small 3D seismic land dataset from North Central Texas, we investigate the origin of

features in our final stack and attribute volumes after reprocessing this legacy dataset.

Motivation

We processed a small 3D land seismic dataset with a conventional workflow. Upon

completion we arrived at the conclusion that the geophysical interpretation contained

features that did not match the a priori geological background. With the geological

background being confirmed by vast numbers of wells in the adjacent area drilled over

many decades of oil and gas exploration. This led us to conclude that the cause of the

incorrect interpretation was at the fault of erroneous processing parameters. We

formulate that the processing job could be affected by any of the three factors:

107

1) NMO far offset stretch

2) Improper velocity analysis

3) Inadequate removal of groundroll

In this paper we investigate the effects of NMO stretching and improper velocity

analysis on synthetic seismic data. In Part 1 (Verma et al., 2015) of this abstract we will

try to understand the effect of groundroll on our seismic interpretation.

We then make an analogue to anomalies seen in our actual seismic data. In doing so we

are able to better understand how processing can affect the geological interpretation.

We are also able to connect common pitfalls in seismic processing using seismic

models.

Methodology

Seismic modeling

We created a simple 3D isotropic seismic model with four layers. The

acquisition geometry is shown in Figure 1, with 6 receiver lines and 9 shot lines. Each

receiver line contains 60 receivers totaling 360 geophones, and each shot line contains

18 sources totaling 162 shots.

Seismic processing

The seismic processing can be broken into 5 steps:

1) Importing the synthetic seismic data

2) Defining the geometry

3) Velocity analysis

4) NMO correction

5) Stacking the synthetic data

108

Figure 2a shows the raw synthetic seismic data sorted in shot versus offset. The

four hyperbolic reflectors in the model are clearly identifiable. Figure 2b shows the

semblance panel and Figure 2c shows the respective NMO corrected gather of the

picked semblances (in white). In this figure we see that the processor has picked the

velocities on the semblance panel to be too fast for reflector 2 and 4. The result is an

undercorrected NMO corrected gather. Figure 2d shows a semblance panel; Figure 2e

the respective NMO corrected gather; and Figure 2f the NMO corrected gather with a

30% stretch mute. However, in this case the processor has selected the semblances to

be too fast in reflector 1 and 3 resulting in an overcorrection. With these inaccurately

picked velocity models we NMO correct and stack the synthetic seismic model.

Attribute interpretation

We computed a suite of seismic attributes using a commercial software package

on both the modeled synthetic data and the real seismic data. Such attributes included

dip and azimuth, coherence and curvature. We then analyzed how improper velocity

analysis and NMO stretching affect attribute response. Using the information gained

from the synthetic model we were able to use our real 3D seismic data as an analogue to

better understand the influence of processing on interpretation.

Results

Figure 3a shows an inline through the processed and stacked four layered synthetic

seismic dataset. We can see quasi-hyperbolic artifacts caused by the constructive and

destructive interference of interfaces not properly flattened during velocity analysis.

The blue arrows point to the artifacts on reflector 1 and 3. This inline is constructed

from the velocity panel and NMO corrected gather shown in Figure 2d and e.

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Figure 3b shows an inline through the processed and stacked four layered modeled

seismic dataset. In this figure we have applied a 30% stretch mute, to mute far offset

stretching phenomena. The quasi-hyperbolic artifacts caused by the constructive and

destructive interference of interfaces not properly flattened during velocity analysis are

smaller, however still identifiable. The blue arrows point to the artifacts on reflector 1

and reflector 3. This inline is constructed from the velocity panel and NMO corrected

gather shown in Figure 2d and f.

Figure 3c shows an inline through the processed and stacked four layered modeled

seismic dataset. This data underwent velocity analysis that was intentionally picked too

fast. The resulting constructive and destructive interference patterns from the reflectors

results in hyperbolic anomalies. The blue arrows point to the artifacts on reflector 2 and

reflector 4. This inline is constructed from stacking the velocity panel and NMO

corrected gather shown in Figure 2b and c.

Figure 4a shows undulatory patterns in the real data similar to those seen in our

synthetic dataset. Figure 4b shows the attribute expression (most negative curvature)

through a timeslice at 850ms through the modeled seismic data. Compared with Figure

4c, the attribute expression of the actual seismic data at 365ms, we see that although

both have perturbations in their expression, they appear to have different patterns.

Although it appears that poor velocity analysis and NMO stretching can cause artifacts

in the seismic data, attribute interpretation seems to show that the cause of our footprint

comes from another source.

110

Conclusions

From interpreting the models, we initially conclude that inaccurate velocity analysis and

not removing NMO stretching can result in incorrect geological interpretations. While

looking at an inline it appears that our real seismic dataset suffers from NMO velocities

picked to be too slow. This can be seen as the undulations in the shallow sections that

could be interpreted as karst features or pock marks. However, analyzing the attribute

expression of both the modeled data and the real seismic data we see different patterns.

This leads us toward the conclusion that velocity analysis may not be the source of our

footprint. In Pitfalls in seismic processing: part 1, we analyze groundroll as a potential

source of acquisition footprint. We believe that in our real 3D seismic dataset, the

expression of the footprint is more aligned with that of groundroll than velocity

analysis. We also deduce that footprint and noise from groundroll or velocity analysis

can be deciphered by other means. From our observations groundroll’s expression will

start strongest at the surface and attenuate with depth. However, inaccurate velocity

analysis will only display features within the interval that the velocity is mis-picked.

Picking a correct velocity in the vertically adjacent section will result in reflectors with

no artifacts. Lastly, improper velocity analysis can create patterns that even more

experienced interpreters could perceive as geology as seen in Figure 4a.

111

East-West(ft.)

No

rth

-So

uth

(ft.

)

12000 10000 2000 0.0

6000

1000

Figure A-1: The acquisition geometry for the synthetic model. Sources

are in red and receivers are in green. The geometry is perfectly rectilinear

which is not the case with actual seismic data due to surface obstructions.

112

Figure A-2: (From left to right, top to bottom) Input modeled seismic data with four

hyperbolic reflectors. Semblance panels for raw gather. NMO corrected gathers for

semblance picked too fast. Semblance panels for raw gather. NMO corrected gather

for semblance picked too slow. Previous NMO corrected gather with 30% stretch

mute.

113

Figure A-3: (a) Modeled seismic data with NMO velocity picked too

slow. (b) is (a) with 30% stretch mute. (c) Modeled seismic data with

NMO velocity picked too fast.

114

Figure A-4: (a) Actual seismic data with hyperbolic artifacts that could be

the effect of inaccurate velocity analysis (blue arrows). (b) Timeslice

through most negative curvature of the modeled seismic data. (c) Timeslice

through most negative curvature of actual 3D seismic data. Notice the

discrepancy of the patterns of curvature between (b) and (c).

b)

a)

c)