1. Using Seismic Attributes
Lecture 13
Using Seismic Attributes
Horizon A
Horizon B
Slide 1
Introduction
Images appear within lecture
Good Seal
Good Reservoir W1 W2 W3 W5 W4 W6 W7 W8 W9
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2. Outline 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
Slide 2
Introduction
This is the outline for this unit
We will:
Review causes of seismic response
Talk about modeling the seismic response
Define what seismic attributes are
Give an overview of seismic attribute applications
Qualitative analyses
Quantitative analyses
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3. Thud! Ring! Seismic Response What causes a seismic response?
1. Changes in bulk-rock velocity or density
Lithology (e.g., sandstone, shale, limestone, salt)
Slide 3
What causes a seismic response?
It is changes in the velocity and/or density of the rock with fluid in the pore space
Several factors effect the velocity and density of bulk-rock
The most common factor is lithology
e.g., sandstones generally have different velocity and density values than shales or limestone.
Consider the last time you struck a hammer or pick against a rock
If you struck a dense limestone, the hammer would bounce back rapidly and the sound would have a high pitched ring
However, if your hammer struck a less dense shale, it would only yield a low pitched ‘thud’
The frequency of the sound you hear when your hammer strikes a rock is proportional to the density of that rock
Limestone
Ring!
Thud!
Shale
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4. Seismic Response What causes a seismic response?
1. Changes in bulk-rock velocity or density
Lithology (e.g., sandstone, shale, limestone, salt)
Porosity (e.g., intrinsic, compaction, diagenesis)
Slide 4
Another factor is porosity
Since the velocity of sound through water is much slower than the velocity of sound through rock, rocks that have a lot of
pore-space are ‘slower’ than those that have lower porosity.
Fast
Slow
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5. What causes a seismic response?
1. Changes in bulk-rock velocity or density
Lithology (e.g., sandstone, shale, limestone, salt)
Porosity (e.g., intrinsic, compaction, diagenesis)
Mineralogy (e.g., calcite vs. dolomite, carbonaceous shales)
Slide 5
The mineral composition of the rocks also affects the velocity and density
For example, carbonaceous material (such plant and animal matter) has a lighter density than most minerals
Therefore, organic-rich source rocks commonly have a lower velocity and density than surrounding rocks
Unfortunately, this difference is often too subtle to detect with typical seismic data
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6. Seismic Response What causes a seismic response?
1. Changes in bulk-rock velocity or density
Lithology (e.g., sandstone, shale, limestone, salt)
Porosity (e.g., intrinsic, compaction, diagenesis)
Mineralogy (e.g., calcite vs. dolomite, carbonaceous shales)
Slide 6
Velocity and density are also affected by the type and density of the pore fluid
For example, assume we have a cubic meter of rock with a porosity of 30%
Let’s also assume that the density of the solid matrix is equal 2.65 g/m3
If the pore spaces of the rock were filled with salt water, the bulk density would be g/m3
However, if the pore spaces were filled with methane, the bulk density would only be g/m3
Fluid type and saturation (water, oil, gas)
Sandstone with 30% Porosity:
Pore Fluid Density
Salt Water
Fresh Water
Oil
Gas
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7. Modeling the seismic response:
Seismic Modeling
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
Slide 7
We can model the seismic response at the boundary between two rock units if we know the velocity and density of each unit
We calculate the reflection coefficient using the formula shown
We then use a mathematical process called convolution to model the seismic response
I2 - I1
I2+ I1
RC=
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8. The Convolution Method
Reflection
Coefficients
Lithology
Velocity
Density
Impedance
Wavelet
Model = x  *
Shale
Sand
Slide 8
This slide shows the basic steps in modeling the seismic response
We multiply the velocity and the density to get the impedance
Next we calculate the reflection coefficient at each significant rock boundary
an increase in impedance across a boundary gives a positive RC
a decrease in impedance across a boundary gives a negative RC
We extract or assume the pulse shape or wavelet
Each RC is convolved with the wavelet
The responses for each individual RC are summed to get a modeled trace
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9. Seismic Modeling W E Wedge Modeling
A wedge model is used to display the interactions of reflection coefficients as the thickness changes
Note how the ‘middle peak’ changes amplitude, shape, and duration as the sand thins to the east
Slide 9
If RCs are close together (thin rock units), then the seismic wavelets will interfere with one another
We can build simple wedge models to display the interactions of reflected waves as the thickness changes
Note on this slide how the ‘middle peak’ changes amplitude, shape, and duration as the sand thins to the east
The ‘middle peak’ disappears about mid way across the model
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10. What are seismic attributes?
Definition
What are seismic attributes?
Seismic attributes are mathematical descriptions of the shape or other characteristic of a seismic trace over specific time intervals.
Slide 10
What are seismic attributes?
Seismic attributes are mathematical descriptions of the shape or other characteristic of a seismic trace over specific
time intervals
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11. Why are seismic attributes important?
Importance / Benefits
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
Slide 11
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
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12. Classes of seismic attributes?
Single-Trace Types
Classes of seismic attributes?
Horizon (loop)
Horizon A
Peak amplitude
Duration
Symmetry
Slide 12
There are several classes of seismic attributes
The more common attributes are calculated trace-by-trace; they are single-trace attributes
Single-trace attributes can be measurements for
a certain loop ( loop = a peak or a trough)
a specific time interval (e.g., from Horizon A to Horizon B)
a series of successive data samples – the instantaneous attributes
Interval
Average amplitude
Maximum (Minimum) Duration
Isochron
Sample (volume, instantaneous)
Amplitude
Time
Frequency
Horizon B
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13. Classes of seismic attributes?
Multi-Trace Types
Classes of seismic attributes?
Trace A
Trace B
Multi-Trace
Dip / azimuth
Coherency
Slide 13
Another class of seismic attribute involves the simultaneous analysis more than one trace
For example, an interpreter will map (correlate) a peak or trough through an area
Software can then calculate two attributes
the dip of the interpreted horizon (in msec/trace)
the azimuth – the compass direction of the maximum dip direction (e.g., 15° East of North)
Coherency is a popular multi-trace attribute in industry
it is a measure of similarity between a window of one trace relative to its neighboring trace or traces
we discussed coherency in the structural lecture (Unit 10)
it is a volume-based attribute – not needing an interpreted horizon
Correlation Window
R2 = 0.92
Amplitude A
Amplitude B
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14. Multi-Trace Types Dip map Faults Stratigrahic features
Slide 14
This is a display of the DIP attribute calculated along an interpreted horizon
Low dips are light; high dips are dark
Two types of geologic features are revealed:
linear segments of high dip associated with fault offsets of the horizon
curvilinear segments of high dip associated with channels and other stratigraphic features
How do we separate structural and stratigraphic features?
structural features are consistent over many time slices
stratigraphic features change rapidly from one time slice to the next (shallower or deeper)
Stratigrahic
features
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15. Seismic attribute applications:
Qualitative
Data quality; seismic artifact identification
Seismic facies; depositional environment
Slide 15
Seismic attributes serve two basic purposes:
Qualitative evaluations
checking seismic data quality – identifying artifacts (non-geologic features) in the data
performing seismic facies mapping to predict depositional environments
Quantitative analyses
using well data to provide calibration, we find an equation that uses one or more attributes to predict a rock or fluid property, such as:
Reservior thickness
Lithology
Porosity
Type of fluid fill (water, oil, gas)
The next few slides will show some examples
Quantitative
Equations relating rock property changes to changes in seismic attributes
Reservoir thickness
Lithology
Porosity
Type of fluid fill
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16. Data Quality Analysis (Artifact detection):
Qualitative Analyses
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)
Slide 16
This slide outlines some of the things we can use qualitative attribute analysis for to terms of data quality
We can identify such things as:
Acquisition gaps, Inline-parallel striping
Multiples, migration errors, incorrect velocities
Improper amplitude and phase balancing
Frequency attenuation
Artifacts due to the overlying geology (e.g., shallow gas, channel)
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17. Data Quality Analysis (Artifact detection):
Inline-parallel acquisition striping at water bottom (~ 40 ms)
Slide 17
Here is an amplitude-based attribute map at a level about 40 ms (10 data samples) below a shallow water bottom
Note the east-west trending “stripes”
This is an acquisition artifact associated with a multi-streamer boat collecting the data by sailing east-west and west-east
These ”stripes” tend to “heal” with depth; they may not impact our analysis of a deep reservoir interval
Inline
Direction
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18. Data Quality Analysis (Artifact detection):
Inline-parallel acquisition striping at 1000ms
Slide 18
This is the same attribute that was displayed on the previous slide, but at a depth of 1000 msec (1 sec) below the seafloor
The east-west “stripes” are still present, but at a much reduced level
This demonstrates how the acquisition ”stripes” tend to “heal” with depth
Inline
Direction
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19. Seismic facies mapping:
Qualitative Analyses
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)
Slide 19
Another qualitative applications is seismic facies mapping
Facies are packages of rocks that …
Seismic facies are …
Environment of Deposition (EoD) can be …
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20. Seismic Facies Mapping Exercise
Qualitative Analyses
Seismic Facies Mapping Exercise
Slide 20
You will be doing a simple seismic facies mapping exercise
This seismic line has been flattened (datumed) on the green horizon
This removes post-depositional tilting
Our interest is at the orange horizon
We want to determine where there is good reservoir rocks below the orange horizon with good seal rock just above it
Orange
Datum
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21. Seismic Facies Mapping Exercise
Conceptual Depositional Model:
Stacked, prograding fluvial to nearshore to offshore siliciclastic parasequences
Magenta
Slide 21
This is the conceptual model for deposition during the period we are interested in
This area was characterized by nearshore to offshore deposition
Note at the orange horizon
Below the horizon – there are fluvial and nearshore deposits
Above the horizon – there are offshore shale deposits
Orange
Fluvial
shales - sands
Nearshore
sands
Offshore
shales
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22. Seismic Facies Mapping Exercise
Conceptual Depositional Model:
Prograding sands increase in porosity upwards before being capped by variable quality marine shale.
Marine Shale (seal)
Magenta
Slide 22
At any location:
We could have poor to good reservoir quality (sand vs. silt and shale, reworking by waves, etc)
We could have fair to excellent seal quality (marine shales diluted with some to no silt)
Porous Sand (reservoir)
Marine Shale (seal)
Orange
Fluvial (reservoir)
Porous Sand (reservoir)
Marine Shale (seal)
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23. Seismic Facies Mapping Exercise
Modern Analog:
Fluvial to nearshore progression resulting in wave dominated, barrier island complex (Texas Gulf Coast)
Slide 23
These units were deposited in an environment like this – a barrier island
The nearshore (beach) sands would be great reservoir rocks (medium to course sand with good sorting)
Lagoonal muds and offshore shales would be good sealing lithologies
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24. Seismic Facies Mapping Exercise
Modeled Seismic Response
Seismic modeling indicates the following response to changes in reservoir and seal quality:
Good Seal
Good Reservoir
Poor Reservoir
Poor Seal
Strong Peak
Strong Trough
Moderate Trough
Moderate Peak
Slide 24
Some seismic models were generated for this area
The pulse is called quadrature phase – similar to a minimum phase response
The orange horizon occurs at a zero-crossing
Attribute of the peak above the orange horizon indicate the properties of the seal
Attribute of the trough below the orange horizon indicate the properties of the reservoir
a strong (high positive amplitude) peak over a strong (high negative amplitude) trough is what we want to find
if the peak (trough) is moderate or low amplitude, it indicates less favorable seal (reservoir)
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25. Seismic Facies Mapping Exercise
Objective:
Identify areas where good-quality seal rocks overlay good-quality reservoir rocks
Available data / tools:
Slide 25
So the objective of the exercise is to identify areas where good-quality seal rocks overlay good-quality reservoir rocks
You are given:
Two seismic attribute maps
Orange time structure map
Depositional model and seismic response
Tracing paper and pencils
Seismic attribute maps
Orange time structure map
Depositional model and seismic response
Tracing paper and pencils
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26. Seismic Facies Mapping Exercise
What We Want
Good Seal
Good Reservoir
Strong Peak
Strong Trough
Poor Seal
Good Reservoir
Good Seal
Poor Reservoir
Poor Seal
Poor Reservoir
Slide 26
Here again is the key – where is a strong peak followed by a strong trough centered on the orange horizon?
Moderate Peak
Strong Trough
Strong Peak
Moderate Trough
Moderate Peak
Moderate Trough
Have Some Fun Mapping!
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27. Seismic attribute applications:
Qualitative
Data quality; seismic artifact identification
Seismic facies; depositional environment
Slide 27
We have seen a few qualitative applications of seismic attributes
Now we will consider some more quantitative applications
Quantitative
Equations relating rock property changes to changes in seismic attributes.
Reservoir thickness
Lithology
Porosity
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28. Quantitative Analyses
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)
Slide 28
There are several requirements to perform a quantitative attribute analysis
Some are related to the seismic data; others relate to calibration data from wells
In terms of the seismic data:
The data processing should be Controlled Amplitude and Controlled Phase
Data quality should be high and checked by doing some reconnaissance
All wells should be tied to the seismic data (synthetics) and the results should be high-quality ties
In terms of calibration:
There should be sufficient well control so that variations in rock or fluid properties can be related to variations in one or more seismic attributes
We can use 1-D models to give more calibration points (e.g., if the well has a 50 m gas zone, we could model the seismic response and attribute characteristics of a similar gas sand that is 100 m or 25 m thick)
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29. Quantitative Analyses
Quantitative Attribute Exercise
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
Slide 29
We will go through a quantitative exercise together
Our goal is to build a correlation between seismic attributes and sand thickness
If we can do this, it will allow us to predict areas of high reservoir producibility.
We will use modeled seismic and well data
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30. Geologic Description
Backstepping, unconfined sheet-sands comprising two multicycle reservoirs separated by a marine shale
Slide 30
Our modeled reservoir is shown on this slide
There are two high sand-percent intervals (high Net:Gross) separated by a marine shale
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31. Attribute Response
Which seismic attributes differentiate average sand thickness?
Sand Shale
Sand Shale
Sand Shale
Slide 31
We can extract from the reservoir model the sand % and how the sands are stacked at various locations
This slide shows 3 of the 9 locations we will use to calibrate seismic attributes to average sand thickness
Well 2
Well 6
Well 9
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32. Calibration Duration Amplitude Maximum Average Minimum50
100
150
200
250
300 30 40 60 70 80 90
Maximum Loop Duration (ms)
Measured Average Sand Thickness (ft) 35 45 55 65 75
Average Loop Duration (ms) 10 20
Minimum Loop Duration (ms) 50
100
150
200
250
300 40 60 80
120
140
160
Average Positive Amplitude
Measured Average Sand Thickness (ft)
Amplitude 90
110
130
170
180
Maximum Amplitude 5 10 15 20 25 30 35
Average Amplitude
Maximum
Slide 32
Here we have 6 cross-plots – each has sand thickness on the vertical axis and a seismic attribute on the horizontal axis
The attributes for the top row: maximum time duration and maximum amplitude
The attributes for the middle row: average time duration and average amplitude
The attributes for the bottom row: minimum time duration and minimum amplitude
QUESTION: Of these 6 attributes, does any show a simple trend (linear is nice) so that from the attribute value we can predict sand thickness with a fair degree of confidence?
ANSWER: There is one – average amplitude – has a simple, near-linear trend
Average
Minimum
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33. Seismic Attribute Calibration
300
Thickness = APA
R2 = 0.869
250
200
Slide 33
This is the “well behaved” attribute plotted against sand thickness
From the last slide, the “well behaved” attribute is average amplitude
A linear regression was obtained (an equation) that fits the data fairly well (R2 value of 0.87)
As an example of how we can use this, if at a given location the attribute value is 100, then we would predict the
sand thickness to be 150 ft (from 100 on the X axis, go up to the yellow line and then look across to the Y value
at that level)
Measured Average Sand Thickness (ft)
150
100 50 40 60 80
100
120
140
160
Average Positive Amplitude
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34. Input Seismic AttributeW1 W2 W3 W5 W4 W6 W7 W8 W9
Slide 34
Here is the input seismic attribute – average amplitude
It varies from 55 to 145 amplitude units
We can put the amplitude values into our equation and predict the sand thickness
Average Amplitude
Low
High 55 70 95
110
125
140
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35. RESULT W1 W2 W3 W5 W4 W6 W7 W8 W9 Thin Thick Average Sand Thickness 60
Slide 35
Here is the result of using this equation
Since the equation had only one attribute and a linear relationship, the colors are the same
HOWEVER, the values are now predicted FEET of sand
Thin
Thick
Average Sand Thickness 60 80
100
120
140
160 feet
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36. Quantitative Analysis: A Brief Example
An Oil Field, Onshore Alabama
Porosity in the
Upper Smackover
No Porosity in the
Upper Smackover
Impedance
Impedance
Slide 36
Next we will review an example of a quantitative attribute analysis using real data
This study is for an oil field from onshore Alabama
The reservoir is a carbonate interval called the Smackover formation
In some locations, the upper Smackover is porous and contains oil; elsewhere it is tight (no porosity)
We want to use seismic data to predict where the Smackover is porous
Haynesville
Haynesville
Porous
Zone
Tight
Smackover
Smackover
Norphlet
Norphlet
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37. Change Porosity -> Change Seismic Response
in the Smackover
No Porosity
in the Smackover
Representative In-Line
2.82
2.82
2.84
2.84
Slide 37
Here is a seismic line that connects two well locations
The well on the left has porosity (and oil); the well on the right has no porosity
NOTE the black reflection cycle (a trough) associated with the upper Smackover
on the left, it is not as black (not as negative) as it is on the right
also, on the left there is a thin interval of white that dies out to the right
These are subtle differences that may be related to the presence/absence of porosity
It would be hard to visually use these observations; but seismic attributes make it relatively easy
2.92
2.92
The trough is
lower in amplitude
and loop duration
is longer
The trough is
higher in amplitude
and loop duration
is shorter
Mapped
Horizon
(white)
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38. 1-D Seismic Modeling
Changing the porosity in the Upper Smackover in 1-D models confirms there is a seismic signature related to porosity
16 ft
Porous
Zone
10 ft
Porous
Zone
3 ft
Porous
Zone
Slide 38
We can check to see if some attributes are sensitive to the amount of porosity
Here we have a “real” well and two “pseudo-wells”
The real well had a 10 foot thick porous zone
We edited the well data to give us two “pseudo-wells” (modifications of a real well)
On the left is a pseudo well with 16 feet of porosity
On the right is a pseudo well with 3 feet of porosity
We also show a modeled trace for all three
NOTE how the trough varies in amplitude and shape as the thickness of the porous zone changes
Using seismic attributes, we can capitalize on these subtle variations
Haynesville
Smackover
Norphlet
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39. Attribute Calibration & Evaluation
Porosity for the Smackover
Predicted based on 4 attributes
Calibration based on 8 wells
Actual Average Smackover Porosity 5 3 2 1 8 9 7 6 4
Predicted Average Smackover Porosity
Best Fit
95% C.I.
Slide 39
Here is a cross-plot in which:
The horizontal axis is porosity thickness (data points are only from actual wells)
The vertical axis is our predicted porosity thickness from an equation that uses 4 seismic attributes as terms (each attribute has a different weight)
A best linear fit was derived (mathematically) and 95% confidence intervals are shown
We could use pseudo-well to add more calibrations points, but that was deemed unnecessary for
this case since we have 8 wells with a good spread of values
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40. A Predicted Porosity Map
Applying the derived attribute “equation” to the 3D seismic survey resulted in a Smackover porosity map
Possible New
Well Location
Slide 40
We use the equation (based on 4 attributes) to derive a map of predicted feet of porosity
throughout the area
Hot colors are where we predict the thickest porosities exist
There are some “edge effects” near the boundaries of the survey that have to be ignored
The zoomed in area gives a high level of detail for a known producing zone
We can use this detail to position additional production wells
This map was also used to identify other drill locations for near-field wildcats
18%
porosity
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41. Potential Pitfalls / Solutions
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
Slide 41
This slide lists some attribute pitfalls and some of the possible solutions/remedies
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42. Summary
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)
Slide 42
In summary ….
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