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Lecture 13Lecture 13
Horizon A
Horizon B
Good SealGood Reservoir
W1 W2W3
W5
W4
W6W7
W8 W9
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•Review causes of seismic response
•Modeling the seismic response
•What are seismic attributes?
•Overview of seismic attribute applications
- Qualitative analyses
Exercise: Mapping depositional environments
- Quantitative analyses
Exercise: Predicting average porosity
Outline
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Limestone Shale
Seismic Response
What causes a seismic response?
1. Changes in bulk-rock velocity or density
• Lithology (e.g., sandstone, shale, limestone, salt)
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Fast Slow
Seismic Response
What causes a seismic response?
1. Changes in bulk-rock velocity or density
•Porosity (e.g., intrinsic, compaction, diagenesis)
•Porosity (e.g., intrinsic, compaction, diagenesis)
• Lithology (e.g., sandstone, shale, limestone, salt)
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Seismic Response
•Mineralogy (e.g., calcite vs. dolomite, carbonaceous shales)
•Mineralogy (e.g., calcite vs. dolomite, carbonaceous shales)
What causes a seismic response?
1. Changes in bulk-rock velocity or density
•Porosity (e.g., intrinsic, compaction, diagenesis)
•Porosity (e.g., intrinsic, compaction, diagenesis)
• Lithology (e.g., sandstone, shale, limestone, salt)
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•Fluid type and saturation (water, oil, gas)•Fluid type and saturation (water, oil, gas)
Pore Fluid DensitySalt Water 2.164Fresh Water 2.155Oil 2.095Gas 1.856
Pore Fluid DensitySalt Water 2.164Fresh Water 2.155Oil 2.095Gas 1.856
Sandstone with 30% Porosity:Sandstone with 30% Porosity:
Seismic Response
•Mineralogy (e.g., calcite vs. dolomite, carbonaceous shales)
•Mineralogy (e.g., calcite vs. dolomite, carbonaceous shales)
What causes a seismic response?
1. Changes in bulk-rock velocity or density
•Porosity (e.g., intrinsic, compaction, diagenesis)
•Porosity (e.g., intrinsic, compaction, diagenesis)
• Lithology (e.g., sandstone, shale, limestone, salt)
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Modeling the seismic response:
•Determine bulk-rock velocity and density
•Calculate impedance (Recall: I = ρ x v)
•Represent impedance changes as reflection coefficient
•Convolve seismic wavelet to reflection coefficients
I2 - I1I2+ I1
RC=
Seismic Modeling
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The Convolution Method
Velocity Density ImpedanceReflection
CoefficientsWavelet Model Lithology
=x *
Shale
Sand
Shale
Sand
Shale
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• A wedge model is A wedge model is used to display the used to display the interactions of interactions of reflection reflection coefficients as the coefficients as the thickness changesthickness changes
• Note how the Note how the ‘‘middle peakmiddle peak’’ changes amplitude, changes amplitude, shape, and duration shape, and duration as the sand thins to as the sand thins to the eastthe east
W EWedge ModelingWedge Modeling
Seismic Modeling
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What are seismic attributes?
Seismic attributes are mathematical
descriptions of the shape or other
characteristic of a seismic trace over
specific time intervals.
Definition
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Why are seismic attributes important?
• Our increasing reliance on seismic data requires that we extract the most information available from the seismic response
• Seismic attributes enable interpreters to extract more information from the seismic data
• Applications include hydrocarbon play evaluation, prospect identification and risking, reservoir characterization, and well planning and field development
Importance / Benefits
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Classes of seismic attributes?
•Horizon (loop) Horizon A•Peak amplitude•Duration•Symmetry
•Sample (volume, instantaneous)•Amplitude•Time•Frequency
•Interval•Average amplitude•Maximum (Minimum) Duration•Isochron
Horizon B
Single-Trace Types
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Classes of seismic attributes?
•Multi-Trace-Dip / azimuth-Coherency
Cor
rela
tion
Win
dow
Trace A Trace B
Am
plit
ude
A
Amplitude B
R2 = 0.92
Multi-Trace Types
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Faults
Stratigrahicfeatures
Dip map
Multi-Trace Types
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Seismic attribute applications:
• Qualitative
• Quantitative
-Data quality; seismic artifact identification-Seismic facies; depositional environment
- Equations relating rock property changes to changes in seismic attributes
▪ Reservoir thickness▪ Lithology▪ Porosity▪ Type of fluid fill
ApplicationsApplications
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Data Quality Analysis (Artifact detection):
• Identify zones where seismic data quality is adversely affected by acquisition or processing methods or by geologic interference.
- Acquisition gaps, Inline-parallel striping
- Multiples, migration errors, incorrect velocities
- Improper amplitude and phase balancing
- Frequency attenuation
- Overlying geology (e.g., shallow gas, channel)
Qualitative AnalysesQualitative Analyses
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Data Quality Analysis (Artifact detection):
• Inline-parallel acquisition striping at water bottom (~ 40 ms)
Data QualityData Quality
Inline Direction
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Data QualityData Quality
Data Quality Analysis (Artifact detection):
• Inline-parallel acquisition striping at 1000ms
Inline Direction
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Seismic facies mapping:
• Facies are packages of rocks that exhibit similar characteristics (e.g., lithofacies, petrophysical facies, depositional facies)
• Seismic facies are packages of seismically-defined bodies that exhibit similar seismic characteristics (e.g., reflection geometry, amplitude, continuity, frequency).
• Environment of Deposition (EoD) can be interpreted from patterns of seismic facies (i.e., similar seismic attributes)
Qualitative AnalysesQualitative Analyses
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Orange
Datum
Qualitative AnalysesQualitative Analyses
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Conceptual Depositional Model:
Stacked, prograding fluvial to nearshore to offshore siliciclastic parasequences
Orange
Magenta
Seismic Facies Mapping ExerciseSeismic Facies Mapping Exercise
Fluvial shales - sands
Offshore shales
Nearshore sands
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Marine Shale (seal)
Porous Sand (reservoir)
Marine Shale (seal)
Marine Shale (seal)
Porous Sand (reservoir)
Fluvial (reservoir)Orange
Magenta
Prograding sands increase in porosity upwards before being capped by variable quality marine shale.
Seismic Facies Mapping ExerciseSeismic Facies Mapping Exercise
Conceptual Depositional Model:
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Modern Analog:
Fluvial to nearshore progression resulting in wave dominated, barrier island complex (Texas Gulf Coast)
Seismic Facies Mapping ExerciseSeismic Facies Mapping Exercise
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Modeled Seismic Response
Seismic modeling indicates the following response to changes in reservoir and seal quality:
Good SealGood Reservoir
Good SealPoor Reservoir
Poor SealGood Reservoir
Poor SealPoor Reservoir
Strong PeakStrong TroughStrong Peak
Strong TroughStrong Peak
Moderate TroughStrong Peak
Moderate TroughModerate PeakStrong TroughModerate PeakStrong Trough
Moderate PeakModerate TroughModerate Peak
Moderate Trough
Seismic Facies Mapping ExerciseSeismic Facies Mapping Exercise
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Objective:
• Identify areas where good-quality seal rocks overlay good-quality reservoir rocks
Available data / tools:
• Seismic attribute maps
• Orange time structure map
• Depositional model and seismic response
• Tracing paper and pencils
Seismic Facies Mapping ExerciseSeismic Facies Mapping Exercise
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Seismic Facies Mapping ExerciseSeismic Facies Mapping Exercise
Good SealPoor Reservoir
Poor SealGood Reservoir
Poor SealPoor Reservoir
Good SealGood Reservoir
Strong PeakStrong TroughStrong Peak
Strong TroughStrong Peak
Moderate TroughStrong Peak
Moderate TroughModerate PeakStrong TroughModerate PeakStrong Trough
Moderate PeakModerate TroughModerate Peak
Moderate Trough
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Seismic attribute applications:
• Qualitative
• Quantitative
-Data quality; seismic artifact identification-Seismic facies; depositional environment
- Equations relating rock property changes to changes in seismic attributes.
▪ Reservoir thickness▪ Lithology▪ Porosity
ApplicationsApplications
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Quantitative Seismic Attribute Analysis
• Requirements:
- Controlled Amplitude, Controlled Phase processing
- Data quality reconnaissance
- Good well-seismic ties
- Sufficient well control (additional seismic modeling is usually necessary)
Quantitative AnalysesQuantitative Analyses
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Goal:• Build a correlation between seismic
attributes and sand thickness to predict areas of high reservoir producibility.
Tools:• Seismic - well log (i.e., rock property) models
Quantitative AnalysesQuantitative Analyses
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Backstepping, unconfined sheet-sands comprising two multicycle reservoirs separated by a marine
shale
Geologic DescriptionGeologic Description
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Well 9Well 2 Well 6
Sand Shale Sand Shale Sand Shale
Which seismic attributes differentiate average sand thickness?
Attribute ResponseAttribute Response
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Max
imu
mA
vera
ge
Min
imu
m
Duration
0
50
100
150
200
250
300
30 40 50 60 70 80 90 100
Maximum Loop Duration (ms)
Mea
sure
d A
vera
ge
Sa
nd
Th
ickn
ess
(ft)
0
50
100
150
200
250
300
30 35 40 45 50 55 60 65 70 75 80
Average Loop Duration (ms)
Mea
sure
d A
vera
ge
Sa
nd
Th
ickn
ess
(ft)
0
50
100
150
200
250
300
0 10 20 30 40 50 60 70
Minimum Loop Duration (ms)
Mea
sure
d A
vera
ge
Sa
nd
Th
ickn
ess
(ft)
0
50
100
150
200
250
300
40 60 80 100 120 140 160
Average Positive Amplitude
Mea
sure
d A
vera
ge
Sa
nd
Th
ickn
ess
(ft)
Amplitude
0
50
100
150
200
250
300
80 90 100 110 120 130 140 150 160 170 180
Maximum Amplitude
Mea
sure
d A
vera
ge
Sa
nd
Th
ickn
ess
(ft)
0
50
100
150
200
250
300
0 5 10 15 20 25 30 35 40
Average Amplitude
Mea
sure
d A
vera
ge
Sa
nd
Th
ickn
ess
(ft)
CalibrationCalibration
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Seismic Attribute Calibration
0
50
100
150
200
250
300
40 60 80 100 120 140 160
Average Positive Amplitude
Mea
sure
d A
ver
age
Sa
nd
Th
ick
nes
s (
ft)
Thickness = 3.3787 APA - 187.67R2 = 0.869
Seismic Attribute CalibrationSeismic Attribute Calibration
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Low HighAverage Amplitude
W1W2
W3
W5
W4
W6
W7
W8 W9
Input Seismic AttributeInput Seismic Attribute
140125110957055
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RESULT
Thin ThickAverage Sand Thickness
W1W2
W3
W5
W4
W6
W7
W8 W9
160 feet1401201008060
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Quantitative Analysis: A Brief Example
Porosity in the Upper Smackover
PorousZone
Impedance
Sm
ackover
No
rph
letH
aynesville
No Porosity in the Upper Smackover
Tight
Sm
ackover
No
rph
letH
aynesville
Impedance
An Oil Field, Onshore Alabama
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Change Porosity -> Change Seismic Response
Representative In-LinePorosity
in the SmackoverNo Porosity
in the Smackover
The trough islower in amplitudeand loop duration
is longer
The trough ishigher in amplitudeand loop duration
is shorter
MappedHorizon
(white)
2.84
2.82
2.92
2.84
2.82
2.92
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1-D Seismic Modeling
Changing the porosity in the Upper Smackover in 1-D models confirms there is a seismic signature related to porosity
Smackover
Haynesville
Norphlet
16 ftPorousZone
3 ftPorousZone
10 ftPorousZone
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Attribute Calibration & Evaluation
Porosity for the Smackover– Predicted based on 4 attributes– Calibration based on 8 wells
Actual Average Smackover Porosity
0 5321 8 9764
5
0
6
1
4
3
2
8
7
9
Pre
dic
ted
Av
era
ge
Sm
ac
ko
ve
r P
oro
sit
y
Best Fit
95% C.I.
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A Predicted Porosity Map
Applying the derived attribute “equation” to the 3D seismic survey resulted in a Smackover porosity map
18%
0
po
rosi
ty
Possible NewWell Location
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Inadequate well control:• Wells don’t represent all variability within
reservoir• Use seismic modeling to infill gaps
Redundant attributes• Different attributes highly correlated to one
another• Remove redundant attributes; keep one that
correlates best with rock property
Linear correlation• Nonlinear correlation may be better
representation• Test other nonlinear correlation schemes but
be aware of extrapolation problems
Potential Pitfalls / SolutionsPotential Pitfalls / Solutions
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• Seismic attributes describe shape or other characteristics of a seismic trace over specific intervals or at specific times
• Seismic attributes are important because they enable interpreters to extract more information from seismic data
• Seismic attributes can be derived from a single-trace or by comparison of multiple traces
• Three common types of single-trace attributes are horizon-, interval-, and sample-based
• Seismic attributes are used for qualitative analysis (e.g., data quality, seismic facies mapping) and quantitative analysis (e.g., net sand, porosity prediction)
SummarySummary