Determining Depositional History through use of · PDF fileDetermining Depositional History...
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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
Reconnaissance Blends
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
Cognitive Interpretation Workflow
Data Conditioning
Noise Cancellation
Spectral Enhancement
Feedback Loops
Adaptive Horizon Interpretation
Iso-Proportional Slicing
Adaptive Geobody Extraction
Interactive Facies Classification
Reconnaissance Blends
High Definition Frequency Decomposition
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
Spectral Enhancement
• 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.
Spectral Enhancement
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
Spectral Enhancement
NW SE Spectrally Enhanced
0 1km
• Aim to;
- Improve the vertical
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
continuity of reflectors
- Improve the vertical
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
continuity of reflectors
- Improve the vertical
resolution.
- Enhance the
presence of
stratigraphic and
structural edges. 0 1km
Cognitive Interpretation Workflow
Data Conditioning
Noise Cancellation
Spectral Enhancement
Feedback Loops
Adaptive Horizon Interpretation
Iso-Proportional Slicing
Adaptive Geobody Extraction
Interactive Facies Classification
Reconnaissance Blends
High Definition Frequency Decomposition
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?
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.
Cognitive Interpretation Workflow
Data Conditioning
Noise Cancellation
Spectral Enhancement
Feedback Loops
Adaptive Horizon Interpretation
Iso-Proportional Slicing
Adaptive Geobody Extraction
Interactive Facies Classification
Reconnaissance Blends
High Definition Frequency Decomposition
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.
Adaptive Geobody Extraction
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.
Interactive Facies Classification
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.
Interactive Facies Classification
Blue = thin fan
deposits
Red = thick channel
axis
Yellow = channel
fill
Brown = thin fan deposits
Orange = channel
axis
Yellow = channel
fill
Interactive Facies Classification
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