1. 1
Seismic Expression of Polygonal Fault Systems: An example from North Sea, Dutch Offshore.
Priyadarshi Chinmoy Kumar*, Fawz Naim and Sunit Mohanty, AcSIR-National Geophysical Research Institute,
India; Indian School of Mines,Dhanbad; Pondicherry University, Puducherry, India.
Summary
Layer bound polygonal fault systems are small scale normal
faults demonstrating a polygonal pattern observed from the
map view of the subsurface. The analysis of the 3D seismic
data of the F3 block located in the north-eastern part of the
Dutch offshore of the North Sea, provides a detailed image
of the polygonal geometry of these fault systems observed
at the Mid- Miocene Unconformity section in the region.
The fault systems exhibit a random distribution indicating
major NNW-SSE structural trends.
Introduction
Polygonal Fault Systems (PFs) are perplexing geological
features found in many sedimentary basins. They consist of
numerous number of small-scale normal faults arranged in
a three dimensional complex array system. Their presence
can well be admired forming a beautiful polygonal pattern
obtained from a computer generated map of the subsurface.
They are characterized by an array of layer bound, densely
packed extensional faults present within a fine-grained
stratigraphic interval that exhibit a diverse range of fault
strike with random orientation which fully or partially
intersect to form a polygonal pattern observed from the map
view of the subsurface (Cartwright, 1994). The analysis of
these fault systems plays a very significant role in
understanding the process of fluid migration and their
capabilities for controlling the architecture of deep water
reservoirs (Rensbergen et al, 2003). Their existence was
first documented in the Lower Tertiary mudrocks of the
North Sea Basin (Cartwright, 1994) using 3D seismic data.
Lonergan and Cartwright (1999); Stuevold et al (2003)
have revealed the occurrence of these fault system in
coarser facies interbeded within the fine grained
sedimentary interval of a basin.
Fault systems are commonly epitomized as discontinuous
reflection patterns observed in seismic data if there is any
vertical displacement for a given seismic reflector (Jaglan
et al., 2015). These features can very well be imaged by the
use of seismic attributes and their effective recognition by
the help of attributes provides a powerful way to quickly
visualize and map these complex geological structures.
The present study identifies Polygonal Fault networks at the
Mid Miocene Unconformity (MMU) which has been
imaged by using offshore 3D data in the north-eastern part
of the Dutch Offshore in North Sea.
Geology and Data Base
The study area (Figure 1) is situated in the north eastern
part of the Dutch sector of the North Sea.
Figure 1: The geological map of the study area showing F3
3D seismic block marked as a black square.
Sediment progradation resulted in major sequences during
the Cenozoic era in the North Sea Basin (Michelsen et al.,
1998). One of such sequence got developed during the
Middle Miocene period which is called as the Mid Miocene
Unconformity (MMU) (Huuse and Clausen, 2001).This
MMU divides the Cenozoic succession into two major
packages out of which the lower package comprises of
mainly fine gained Paleogene sediments (Steeghs et al.,
2000) and the upper package comprises of coarser grained
Neogene sediments. The MMU is estimated to be of about
14 to 15 Ma (Huuse and Clausen 2001) within the Dutch
sector. After the Middle Miocene, the central and southern
part of the North Sea became relatively deep with an
estimated water depth ranging between 130 to 400m
(Overeem et al., 2001).
The 3D seismic data was taken from Open Seismic
Repository of dGB Earth SciencesTM
and the analysis was
done using OpendTectTM
5.0.0, an open source software
system used for seismic interpretation. The 3D seismic data
of F3 block is a Post-Stack Time Migrated volume covering
an area of 384 sqkm. The seismic data volume comprises of
600 inlines and 900 xlines with a line spacing of 25m in
both inline and crossline direction. The sampling rate is
4ms with a total data length of around 1.8 seconds.

2. SEG/SPG 2016 International Geophysical Conference
Methodology & Analysis
Taking into account the fact that any distortion effects
whether near-surface or relating to amplitude or phase
responses, should be taken care, the seismic data was
optimally conditioned. We began to condition the data by
computing dip-azimuth volume from the existing seismic
data volume. This volume is called as steering cube
(Tingdhal 1999; Tingdhal et al., 2001) which was prepared
by using a phase based dip calculation algorithm that
utilizes seismic phase attribute. Once the steering cube got
prepared, a median statistical filter known as Dip-steered
Median Filter (DSMF) was applied on the seismic data
using the pre-processed steering cube. This resulted in a
smoothed seismic volume (Figure 3(b)) thereby improving
the continuity of the seismic reflectors and removing the
background random noise. Seismic data generally exhibit a
diffused character closer to a fault zone (Jaglan et al.,
2015). Thus to optimize for this and improving the
sharpness of the faults an intermediate filter called as dip-
steered diffusion filter (DSDF) was applied which resulted
a diffusion seismic volume. Both of these (DSMF and
DSDF) seismic volumes were combined using a logical
expression that gave rise to a fault enhanced seismic (FES)
data (Figure 3(c)). This fault enhanced seismic data was
used as an input for seismic attribute analysis. The Time
Slice was then analyzed to visualize the fault networks. It
was observed that these fault networks showed their
prominence at t = 1.3 seconds (Figure 4) covering the NW,
SW and NE parts of the block. Keeping in mind these facts
and with the available geological markers, a suitable
horizon from the top of the formation (MMU) (Figure 5)
was picked at every 5th inline and crossline. Taking into
account the fact that horizon picked on the seismic data
would get contaminated by the miss-picks which could
result in misleading interpretation, the horizon was filtered
using median filtering technique.
Fault signatures are generally more pronounced with high
discontinuity or low similarity in seismic events. A
similarity attribute volume that gives a direct measurement
of discontinuities in the seismic events, was computed
using fault enhanced seismic data as input. This attribute is
very sensitive to phase changes, which makes it very useful
to highlight vertical displacements associated with faults.
When this attribute was visualized over time slice t = 1.3
seconds (Figure 6), the polygonal networks of the fault
were imaged in a more enhanced way, most of which was
observed in the NWSW and NE part of the block.
Similarly, volumetric curvature attributes (most positive
and most negative) (Figure 7 (A & B)) were computed
using pre-computed dip field. These attributes were then
corendered (opacity = 50%) using similarity attribute
volume (Figure 7(C)) to correlate the areas of low similarity
with that of the curvature images.
Figure 3: Inline 425 from the seismic data volume showing
(a) Original seismic volume, (b) Dip-Steered Median Filter
seismic volume (DSMF), and (c) Fault Enhanced Seismic
data volume (FES).The fault enhanced seismic data shows a
sharper definition of faults thereby improving visualization.
The attributes were also visualized over the horizon picked
at the top of the formation (Figure 8). Fault discontinuities
were clearly brought though this analysis. Most of them
were pronounced in NWSW and NESE areas of the block.
The similarity attribute volume was corendered
(opacity=50%) with most positive and most negative
curvature attributes and displayed over the horizon slice in-
order to correlate the fault images obtained from the
similarity attribute with that of the curvature images.
Subsequently the rose plot (Figure 9) was prepared in-order
to understand the orientation of these irregular fault
geometries. From the rose plot it was observed that, the
polygonal fault system exhibits a wide range of strike
pattern indicating the major EW trend.

3. SEG/SPG 2016 International Geophysical Conference
Figure 4: Visualization of Polygonal Fault networks over
Time slice at time t = 1.3 seconds. The fault networks are
indicated by green ovals, displayed using grey color scale
and these networks much more prominent at this time level.
Figure 5: Horizon slice for the Mid Miocene Unconformity
top. The horizon was tracked in every 5th
inline and
crossline from the seismic data volume.
Conclusions
The analysis of 3D seismic data of F3 block suggests that,
the Mid Miocene Unconformity is characterized by the
layer-bound fault system which is polygonal in nature. The
seismic attribute study bought out clearly the polygonal
nature of these layer bound fault system. Further research
can be carried out to understand the response of these fault
systems for understanding the fluid migration mechanism
and reservoir architecture.
Figure 6: Time Slice at t = 1.3 seconds obtained from
similarity volume using fault enhanced seismic volume as
an input. The fault networks are characterized by low
similarity, indicated within squared boxes and displayed
using similarity color scales.
Acknowledgments
The authors provide their sincere thanks to the Department
of Applied Geophysics, Indian School of Mines, Dhanbad
for providing support and encouragements to carry out this
work. Thanks to dGB Earth ScienceTM
for making seismic
data freely available for the public and for providing their
software for academic research. A special thanks to Miss
Karabi Talukdar for her support and encouragements.
References
Cartwright, J.A, 1994. Episodic basin-wide hydrofracturing
of overpressured early Cenozoic mudrock sequences in the
North Sea Basin. Marine and Petroleum Geology, 11(5),
pp.587-607.
Huuse, M., and Clausen, O.R., 2001. Morphology and
origin of major Cenozoic boundaries: eastern Danish North
Sea. Basin Research, 13, pp.17-41.
Jaglan, H., Qayyum, F. and Helen, H., 2015.
Unconventional seismic attributes for fracture
characterization. First Break, 33 (3), pp.101-109.
Lonergan, L. and Cartwright, J.A., 1999. Polygonal faults
and their influence on deep-water sandstone reservoir
geometries, Alba Filed, United Kingdom Central North
Sea. AAPG bulletin, 83(3), pp.410-432.
Overeem, I., Weltje, G.J., Bishop-Kay, C. and
Kroonenberg, S.B., 2001. The Late Cenozoic Eridanos
delta system in the Southern North Sea Basin: a climate
signal in sediment supply?. Basin Research, 13(3), pp.292-
312.

4. SEG/SPG 2016 International Geophysical Conference
Steeghs, P., Overeem, I. and Tigrek, S., 2000. Seismic
volume attribute analysis of the Cenozoic succession in the
L08 block (Southern North Sea). Global and Planetary
Change, 27(1), pp.245-262.
Stuevold, L.M., Faerseth, R.B., Arnesen, L. Cartwright, J.
and Moller, N., 2003. Polygonal faults in the Ormen Lange
field, More basin, offshore mid Norway. Geological
Society, London, Special Publications, 216(1), pp.263-281.
Figure 7: Time Slice at t = 1.3 seconds obtained from (a)
most positive (b) most negative volumetric curvature
attribute and (c) corendering similarity with volumetric
curvature attributes shows that the areas of minimum
similarity correlates with that of the curvature images.
Tingdhal, K.M., 1999. Improving seismic detectibility
using intrinsic directionality; Technical Report, Earth
Sciences Center, Goteborg University, B194.
Tingdhal, K.M., Brill, A. H. and de Groot, P. E. M., 2001.
Improving seismic chimney detection using directional
attributes, Journal of Petroleum Science and Engineering,
29, pp.205-211.
Figure 8: Horizon Display of similarity attribute corendered
with most positive and most negative attribute to correlate
the fault signatures with that of the curvature images.
Figure 9: Rose Plot showing the random distribution of the
polygonal fault networks. Note that the polygonal fault
networks shows a random strike distribution indicating
major EW trend.