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Determining Depositional History through use of

Cognitive Interpretation Workflows

Ryan Williams & Rachael Moore

What is Cognitive Interpretation?

Cognitive Interpretation combines the power of algorithmic computation

within software with the benefits of an interpreter’s knowledge and

experience.

About 40% of the brain is devoted to visual cognition and there are strong

links between visual system and memory, thus linking current visual data

with past experience and learnings in order to make sense of incomplete or

ambiguous data.

Leading the interpreter to understand the geology before interpreting it.

Cognitive Interpretation Workflow

Data Conditioning

Noise Cancellation

Spectral Enhancement

Geological

Understanding

Reconnaissance Blends

Data Conditioning

Geobody

Extraction

Iso-Proportional

Slicing

Horizon

Interpretation

Interactive Facies

Classification

Feedback Loops

Adaptive Horizon Interpretation

Iso-Proportional Slicing

Adaptive Geobody Extraction

Interactive Facies Classification


High Definition Frequency Decomposition

Near/Mid/Far

Cognitive Interpretation Case Study

During a regional reconnaissance

project, a seismic anomaly was

discovered in the Northern Graben of

the Taranaki Basin, New Zealand, the

origin of which caused much discussion.

Using Cognitive Interpretation

workflows, a rapid understanding of the

depositional history of this bright

anomaly was determined.

Cognitive Interpretation Case Study

During a regional reconnaissance

project, a seismic anomaly was

discovered in the Northern Graben of

the Taranaki Basin, New Zealand, the

origin of which caused much discussion.

Using Cognitive Interpretation

workflows, a rapid understanding of the

depositional history of this bright

anomaly was determined.

Little 2012


Data Conditioning

Noise Cancellation


Feedback Loops







Near/Mid/Far

Noise Cancellation describes

the process of removing clutter

from seismic data.

Such clutter in the data can

arise from both acquisition and

processing.

The attenuation of clutter or

noise can improve the clarity

of the signal that arises from

changes in geology.

Noise Cancellation

Coherent Noise

- Minor acquisition or

migration noise

Random Noise

- Jitter along the

reflectors

Aggressive Noise

- Salt / basalt effects

- Multiples

Noise Cancellation

NW SE Original Volume

0 1km

Aim to;

- Remove noise

(random and

coherent)

- Improve lateral

continuity of reflectors

Removal of

random “jitter”

Improved lateral

continuity

Noise Cancellation

NW SE Noise Cancelled

0 1km

Aim to;

- Remove noise

(random and

coherent)

- Improve lateral

continuity of

reflectors

Removal of

random “jitter”

Improved lateral

continuity


• The aim of the Spectral Enhancement tool is to differentiate previously

unresolved events by maximising the mean frequency bandwidth and to

produce a ‘white’ spectrum, where all frequencies contribute equally to the

power in the signal.


NW SE Noise Cancelled

0 1km

• Aim to;

- Improve the vertical

resolution.

- Enhance the

presence of

stratigraphic and

structural edges.

Edge preservation/

enhancement

Improved vertical

resolution


NW SE Spectrally Enhanced

0 1km

• Aim to;


resolution.

- Enhance the

presence of

stratigraphic and

structural edges.

Edge preservation/

enhancement

Improved vertical

resolution

Before and After

NW SE Original Volume

0 1km

• Aim to;

- Remove noise

(random and coherent)

- Improve lateral



resolution.

- Enhance the

presence of

stratigraphic and

structural edges.

Before and After

NW SE Data Conditioned

0 1km

• Aim to;

- Remove noise

(random and coherent)

- Improve lateral



resolution.

- Enhance the

presence of

stratigraphic and

structural edges. 0 1km


Data Conditioning

Noise Cancellation


Feedback Loops







Near/Mid/Far

3D Reconnaissance Volumes

Red, Green and Blue (RGB) reconnaissance blends allow the

comparison of 3 varying datasets.

Here two blends variations were generated to best identify the anomaly.

High Definition Frequency Decomposition (HDFD) Blending was undertaken to

show variation within frequency of the anomaly.

Near, Mid and Far (NMF) envelope volumes were blended to identify the

variations observed between the angle stacks.

How many

features can

you see in 10

second?

HDFD Blending

High Definition Frequency Decomposition (HDFD) uses a matching

pursuit algorithm which reconstructs the original signal from a dictionary

of synthetic Gabor wavelets chosen to approximate the seismic

waveform.

After the seismic signal has been successfully matched, the entire trace

can be reconstructed for 3 component frequency bands (Low, Medium

and High Frequency Bands).

This results in minimal vertical smearing whilst delivering the highest

frequency resolution available for 3D seismic.

HDFD Blending

15 Hz, 30 Hz, 45 Hz

High Definition Frequency Decomposition RGB Blend

15 Hz

30 Hz 45 Hz

High

magnitude

Low

magnitude

High

magnitude

Low

magnitude

High

magnitude

Low

magnitude

15 Hz

30 Hz

45 Hz

HDFD Blend Results

Bright channel-like anomaly.

Identification of large scale

amalgamated channel events.

Internal variations observed within the

event

Possible geological variations.

Thicker sections have a lower (red)

frequency.

NMF Blend

Blending together the envelope response of the Near, Mid and

Far angle stacks allows differences between the different angle

stacks to become more evident.

This method can be used as a AVO reconnaissance volume to

help identify any changes in fluid content.

Dominant response from the Far stack (blue) could suggest a Class III type

response – gas sand.

NMF Blending

Near, Mid, Far

Frequency Decomposition RGB Blend

Near

Mid Far

High

magnitude

Low

magnitude

High

magnitude

Low

magnitude

High

magnitude

Low

magnitude

Near

Mid

Far

NMF Blend Results

Secondary spill events.

Additional smaller events to the south.

Far stack dominant

Gas?

Lithology?

Sharp feature delineation.

Blending Results

Identify and delineate the feature.

Identify internal structure.

Thicker/thinner sections.

Geological understanding

Appearance of a number of geological

features such as debris flow or crevasse

splay .

Identification of other features to assist in

building a semi-regional geological model.


Data Conditioning

Noise Cancellation


Feedback Loops







Near/Mid/Far

Iso-Proportional Slicing (IPS)

With the anomaly identified, IPS is a method of analysing the feature in

great detail.

An analysis window ±100ms around the feature has been identified.

Analysis undertaken on the NMF Blend.

Iso-Proportional Slicing – NMF Blend

Oldest

Youngest

Iso-Proportional Slicing Results

Ability to identify more geological information

about the feature.

Early stage drainage features.

Late stage clinoforms.

Distal fan like deposits.

Drainage to the southern hanging wall,

suggesting deposition was post fault

movement.


Now the feature has been

identified and delineated, the user

is able to extract a geobody based

of the 3D understanding created

by the reconnaissance and IPS

blends/volumes.

Ability to generate the geobody as

a whole or as individual

components of the feature.

Volumetrics can be created

directly from the geobody extents.


If the entire feature has been delineated in a geobody, it is

possible to separate out the feature through facies classification.

In this instance 3 areas have been delineated

Channel axis – orange

Channel fill – yellow

Fan – brown

The HDFD Blend was used as it has high vertical and high

frequency resolution.

Frequency content can show changes in lithology, fluid and bed thickness.


Blue = thin fan

deposits

Red = thick channel

axis

Yellow = channel

fill

Brown = thin fan deposits

Orange = channel

axis

Yellow = channel

fill


Possible to link the

anomaly to the bright

amplitudes.

Only possible to

identify individual

facies with the use of

HDFD blend

frequency

differences.

Inline Crossline

Low amplitude

response

No differentiation

in reflector

strength

No differentiation

in reflector

strength

Detailed facies

analysis Detailed facies

analysis

Distal fan features

Improved Geological Understanding

Earlier it was mentioned that the response looked like either a debris flow

or a crevasse splay.

If this was a debris flow, a sediment source location is required. Possibly from the

fault, but the volume deposited does not match that which has been eroded.

If this is a crevasse flow is the sediment sourced from the larger mega-channel

systems?

N

Debris Flow?

Sediment source located updip from

the event.

Internal structure of the event could be

hard to distinguish on seismic (scale).

Occur as individual events.

Evidence in the IPS that events are linked.

Hard to link the geological anomaly

with a Debris flow.

Crevasse Splay?

Comprised of a main channel event

and subsequent infill area.

Can comprise of multiple events.

Sourced from a larger channel

system.

Strong correlation between a

Crevasse Splay and the anomaly.

N

Improved Geological Understanding

With a preferred interpretation identified, more traditional

workflows can be undertaken to further increase confidence of

the interpretation.

Seismic interpretation.

Horizon flattening.

Attribute generation.

These can focus on the specific geological responses identified

from the Cognitive Interpretation workflows.

Seismic Volume

Anomaly

SE NW

Top Sequence

Boundary

Base Sequence

Boundary

Top

Anomaly

Seismic Interpretation

The event is possible to see by its bright amplitude response, but due to

faulting it is difficult to distinguish the event laterally.

SE NW

Anomaly

Seismic volume – Flattened on Top Sequence Boundary SE NW

Top Sequence

Boundary

Base Sequence

Boundary

Top

Anomaly

Seismic Interpretation – Flattened on Top Sequence Boundary SE NW

Top Sequence

Boundary

Base Sequence

Boundary

Top

Anomaly

Seismic Interpretation – Flattened on Top Sequence Boundary

Mega-channel

system

Mega-channel

system

Flood Plain

Anomaly

By flattening the volume on the top Sequence Boundary it is possible to see

the mega-channel systems and subsequent crevasse splay events.

SE NW

Envelope (Reflector Strength) – Flattened on Top Sequence Boundary

High amplitude response

indicates the feature

Although the feature can be identified by reflectivity strength, it isn’t possible to

differentiate between different geological components of the feature.

SE NW

Conclusions

Cognitive interpretation uses the interpreters past experiences and

knowledge to enable them to interpret new geological features.

Use of 3D reconnaissance RGB Blends helps the interpreter understand

features geologically prior to any conventional interpretation.

By creating Geobodies the interpreter is able to accurately identify and

delineate events which can be derived into potential facies variations

within the feature.

With the event identified, delineated and understood geologically more

traditional interpretation method can be incorporated to the workflow to

start building geological models.

References

Little, M. 2012. Parihaka 3D PSTM Final Processing Report. Ministry of

Economic Development New Zealand Unpublished Petroleum Report

PR4582

Cognitive Interpretation

www.GeoTeric.com

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