FWS 06 L 13 - Seismic AttributesCourtesy of ExxonMobil Lecture 13 Horizon A Horizon B Good Seal Good...

FWS 06 L 13 - Seismic Attributes

Courtesy of ExxonMobil

Lecture 13Lecture 13

Horizon A

Horizon B

Good SealGood Reservoir

W1 W2W3

W5

W4

W6W7

W8 W9



•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



Limestone Shale

Seismic Response

What causes a seismic response?

1. Changes in bulk-rock velocity or density

• Lithology (e.g., sandstone, shale, limestone, salt)



Fast Slow

Seismic Response



•Porosity (e.g., intrinsic, compaction, diagenesis)





Seismic Response

•Mineralogy (e.g., calcite vs. dolomite, carbonaceous shales)









•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










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



The Convolution Method

Velocity Density ImpedanceReflection

CoefficientsWavelet Model Lithology

=x *

Shale

Sand

Shale

Sand

Shale



• 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



What are seismic attributes?

Seismic attributes are mathematical

descriptions of the shape or other

characteristic of a seismic trace over

specific time intervals.

Definition



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



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



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



Faults

Stratigrahicfeatures

Dip map

Multi-Trace Types



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



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




• Inline-parallel acquisition striping at water bottom (~ 40 ms)

Data QualityData Quality

Inline Direction



Data QualityData Quality


• Inline-parallel acquisition striping at 1000ms

Inline Direction



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)




Orange

Datum




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



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.


Conceptual Depositional Model:



Modern Analog:

Fluvial to nearshore progression resulting in wave dominated, barrier island complex (Texas Gulf Coast)




Modeled Seismic Response

Seismic modeling indicates the following response to changes in reservoir and seal quality:


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




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





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 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



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



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



Backstepping, unconfined sheet-sands comprising two multicycle reservoirs separated by a marine

shale

Geologic DescriptionGeologic Description



Well 9Well 2 Well 6

Sand Shale Sand Shale Sand Shale

Which seismic attributes differentiate average sand thickness?

Attribute ResponseAttribute Response



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



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



Low HighAverage Amplitude

W1W2

W3

W5

W4

W6

W7

W8 W9

Input Seismic AttributeInput Seismic Attribute

140125110957055



RESULT

Thin ThickAverage Sand Thickness

W1W2

W3

W5

W4

W6

W7

W8 W9

160 feet1401201008060



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



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



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



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.



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



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



• 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

Date post:	27-Mar-2015
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FWS 06 L 13 - Seismic AttributesCourtesy of ExxonMobil Lecture 13 Horizon A Horizon B Good Seal Good...

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