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Geo & Seismic

Seismic attribute analysis in structural interpretation of the Gullfaks
Field, northern North Sea
Jonny Hesthammer and Haakon Fossen1
Statoil, GF/PETEK-GEO, N-5020 Bergen, Norway (e-mail: [email protected])
Present address: University of Bergen, Department of Geology, Allegt. 41, N-5007 Bergen, Norway
(e-mail: [email protected])
ABSTRACT: Seismic attribute maps provide a useful tool in interpreting faults,
particularly those close to or below seismic resolution. Dip, relief, azimuth and
amplitude maps are most useful. Optimal use of such maps requires careful filtering
and appropriate use of colours and light sources. One of the challenges is to
distinguish between anomalies related to real geological features and to seismic noise
– both of which may occur as linear or curvi-linear, continuous features on the
attribute maps. This challenge must be solved by use of independent data. In the
North Sea Gullfaks Field, a family of (curvi-)linear features on the attribute maps are
subparallel to contour lines on time maps. Core data, dipmeter data, stratigraphic log
correlation and forward modelling show that these features are related to seismic
noise rather than real faults.
KEYWORDS: structural geology, fault (geology), seismic interpretation, Gullfaks Field
Detailed knowledge of structural geometries is crucial in order
to optimize production from North Sea and other oil fields.
The importance of accurate structural maps is emphasized as
new discoveries of oil and gas fields are often smaller in size
and more structurally complex. A sound structural interpretation provides a basis for understanding fluid flow patterns and
distinguishing structurally complex areas from less deformed
domains. Large-scale structures can commonly be identified
by standard seismic interpretation methods. However, minor
structures may have a dramatic effect on reservoir performance
(e.g. Antonellini & Aydin 1994), particularly for thin reservoir
units separated by impermeable shales, but also for thicker
sandstone units where deformation structures may drastically
decrease permeability. Detection and mapping of structures
close to or below seismic resolution (typically 15–30 m in the
Gullfaks Field, depending both on lateral and vertical resolution) require additional methods, such as seismic attribute
mapping and structural core analyses.
Since the introduction of computers, data manipulation has
become easier. With today’s technology, seismic attribute maps
can be created within seconds on a seismic workstation. These
maps allow the seismic interpreter to identify structures such as
fractures and folds as well as sedimentological features, and are
commonly incorporated into the seismic interpretation of oil
fields and in exploration. However, without a sound knowledge
of what information the attribute maps can yield, they may
not always be used to the extent possible, and may be
over-interpreted and misunderstood.
This paper shows an integrated use of seismic attribute maps
and well information on the Gullfaks Field in the Tampen Spur
area, northern North Sea (Fig. 1). The field covers an area of
c. 55 km2. Total recoverable reserves amount to c. 310#106
Sm3 of oil and some 30#109 Sm3 of gas in the Jurassic Brent
Petroleum Geoscience, Vol. 3 1997, pp. 13–26
Group, the Cook and the Statfjord Fm. reservoirs (Fig. 2). The
Gullfaks Field occupies the eastern half of a major 10–25 km
wide NNE-trending fault block and is bounded by faults with
kilometre offset to the east and west. Structurally, the field can
be separated into three contrasting compartments: a western
domino system with domino-style fault block geometry, a
deeply eroded eastern horst complex of elevated subhorizontal
layers and steep faults, and a transitional accommodation zone
(graben system) which is in part identified as a modified fold
structure (Fig. 1). The Gullfaks Field is one of the most
structurally complex oil fields in the North Sea. Although the
seismic data are generally of poor quality, an enormous amount
of well data aid understanding the complex geometry. About
150 wells have been drilled, yielding more than 6 km of
cores and 34 km of dipmeter data, together with 110 km of
standard log data. An integrated structural analysis of the available data on the field has recently been carried out (Fossen &
Hesthammer in press).
Most larger-scale structures (faults with several tens to
hundreds of metres offset and folds with wavelengths of several
kilometres) can be recognized by direct interpretation of E–W
seismic lines, N–S lines and time slices. In the following we
focus on identifying smaller-scale structural features, such as
faults with only a few metres offset and drag folds. Due to the
large amount of well data available, it is often possible to
distinguish between seismic noise and real structural features.
By noise, we refer to unwanted energy that can produce a
damaging effect on the desired seismic reflection signals (Sheriff
1977; Fitch 1981), i.e. patterns observed on seismic data that
are not related to real geological features.
In 1991–92, the existing seismic 3D dataset of the Gullfaks
structure was reprocessed. This improved the data quality and a
1354-0793/97/$10.00 ?1997 EAGE/Geological Society, London
J. Hesthammer and H. Fossen
Fig. 1. Structure map of the Rannoch Fm. (near intra-Ness) in the Gullfaks Field. An E–W profile through the field shows the rotated
domino blocks in the western part which are the subject of the present study.
Attribute analysis in structural interpretation
reinterpretation was carried out. Since the previous seismic
interpretation (based on a 1985 3D dataset), significant improvements in hardware and software allowed for more interactive interpretation of E–W lines, N–S lines, random lines, and
time slices. For structural studies, seismic attribute mapping
became most important for recognizing small-scale structures (such as faults with offsets of less than 20–30 m and
drag structures), but also helped with the interpretation and
understanding of larger structures.
Active use of attribute maps resulted in far more accurate
and detailed structural maps of the Gullfaks Field (Fig. 3).
Previous interpretations showed anomalously high densities of
faults around the platforms, where most wells were drilled. The
new maps show no such anomalies and the more even
distribution of faults indicates a more realistic interpretation.
The detailed maps have helped the reservoir engineers to better
understand fluid flow in the reservoirs (Fig. 4b). Finally, the
new maps allowed for a better and more optimal planning of
new wells where accuracy is crucial for success. We discuss the
seismic attribute maps that have proven useful for structural
analysis of the Gullfaks Field: dip (both normal dip map and
‘relief’ map), azimuth and amplitude maps. Other seismic
sample and volume attribute maps such as instantaneous
frequency, acoustic impedance contrast and reflection intensity
maps were tested but did not add to the interpretation.
To create the attribute maps, the interpreted surfaces were
interpolated so that all gaps were closed. Generally, every eighth
E–W line (100 m) was systematically interpreted. Interpreted
N–S lines, random lines and additional E–W lines were
removed prior to interpolation to avoid inconsistencies. The
maps were then ‘snapped’ (refined) to the nearest minimum or
maximum within a specified time window. One of the reflections was also interpreted using an automatic tracking routine
and then compared with the refined map. The overall picture
showed no differences between the ‘snapped’ and the autotracked interpretation, indicating that the true seismic signal
was within the specified refinement time window. It was
therefore considered unnecessary to auto-track the other
seismic reflections.
Fig. 2. Stratigraphic column for the Jurassic and Triassic reservoir
units on the Gullfaks Field (modified from Tollefsen et al. 1994).
Dip maps
The dip maps proved most useful during the seismic reinterpretation of the Gullfaks Field (Fig. 4a). The use of the dip map
for structural interpretation has been documented by Dalley
et al. (1989), Hoetz & Watters (1992), Voggenreiter (1993),
Rye-Larsen (1994) and Dorn et al. (1995). This sample level
attribute map highlights changes in dip of an interpreted
seismic reflection. Since faults offset strata, a continuous
interpretation across the fault will result in a change in dip of
the interpreted horizon, provided the vertical resolution is high
enough to resolve the offset (Yilmaz 1987). How abrupt the
change in dip across a fault is depends on the seismic dataset’s
bin size as well as lateral resolution, which is governed by the
size of the Fresnel zone (Yilmaz 1987). Where the data quality
is poor, the lower frequency of the seismic signal results in a
larger Fresnel zone and the offset of a reflector will appear
more gradual. On the Gullfaks Field, bin size is 12.5 m in the
E–W direction and 50 m in the N–S direction. As a result,
N- or S-dipping features will generally display a more gradual
dip than E- or W-dipping structures. As dip maps highlight
even the most subtle changes in dip, it is now possible to
identify faults with offsets down to approximately 5 m.
The dip maps of the Gullfaks Field have a tendency to
highlight linear or curvi-linear features that run subparallel to
the strike of bedding, i.e. N–S. As will be shown later, these
J. Hesthammer and H. Fossen
Fig. 3. Comparison of the 1991 and
1995 interpretation of the Ness Fm. of
the Middle Jurassic Brent Group in one
of the rotated fault blocks of the
Gullfaks Field. The 1995 interpretation
is based on reprocessed seismic data
and extensive use of seismic attribute
maps. As a result, much more detail is
added to the structure maps. Contour
values are in metres below sea-level.
features are mostly related to seismic noise. Similar effects have
also been recognized by Hoetz & Watters (1992). Due to the
overprinting effect of the noise, N-trending faults on the
Gullfaks Field may be under-represented with respect to more
E-trending structures. Generally, only the more continuous
lineaments or groups of lineaments were interpreted as faults.
Relief maps
The ability to ‘illuminate’ the interpreted seismic reflection with
a computer-generated ‘light source’ helps to highlight any
changes in dip and makes faults stand out as easily distinguished
lineaments (Fig. 4b). If the seismic signal is relatively low
frequency, thus resulting in a more gradual offset of the
interpreted reflection, the inclination of the light source can be
adjusted to enhance this change. In addition, more subtle
changes in dip, such as draping of a layer above existing
faults, features related to differential compaction, and folds are
highlighted by means of shading, and can be made very
obvious. If subparallel (curvi-)linear features related to seismic
noise are present, the artificial illumination azimuth can be set
so that the effect of overprinting noise is minimized. It is thus
possible to extract the real structures and obtain a better and
more detailed interpretation. By colour-contouring the seismic
relief map, the visual effect can be further enhanced (Fig. 4b).
The technique of artificially illuminating surfaces has been used
for aeromagnetic data for more than a decade (Tucker et al.
1985; Kowalik & Glenn 1987) but has only recently been
incorporated into seismic interpretation (Hoetz & Watters
1992; Dorn et al. 1995; Eggink et al. 1996).
On the Gullfaks Field, the interpreted and snapped horizons
have been artificially illuminated from the north, south, east and
west (Fig. 5). Since dark lineaments are easier to identify than
bright trends, it may be necessary to illuminate the surface both
from the north and south (two maps) when trying to identify
E-trending structures. Seismic relief maps from the Gullfaks
Fig. 4. Seismic attribute maps of the intra-Ness Fm. (see Fig. 2) reflection in one of the rotated fault blocks of the Gullfaks Field. See Fig.
1 for location. (a) Dip map. Dark colour indicates steep dips. (b) Colour-contoured relief map artificially illuminated from the north, where
yellow and red represent shallow depths, and purple indicates deeper levels. Well 34/10-A-42 was drilled into the reservoir to the southeast
of producer 34/10-A-9H and gas was injected in the well. The gas migration time was much longer than expected and corresponds with the
path indicated in (b), which utilizes a soft-linked relay structure between two NE-trending fault segments between the wells. (c) Azimuth
map. Yellow colour represents dips to the east, green shows dips to the west, red indicates dips to the southeast, and blue shows dips to
the northwest. (d) Amplitude map. Red colour represents areas of high amplitudes, whereas blue shows areas of low amplitude values.
Attribute analysis in structural interpretation
Fig. 4.
J. Hesthammer and H. Fossen
Fig. 5.
Attribute analysis in structural interpretation
Fig. 6. (a) Colour-contoured seismic relief map of the intra-Ness Fm. reflection in one of the rotated fault blocks on the Gullfaks Field,
artificially illuminated from the north. See Fig. 1 for location. (b) The same relief map with interpretation of faults shown. Interpretation
based on 1991 reprocessed data is shown as red lines. Although this interpretation is much more detailed than that prior to 1991 (shown in
green), the new seismic relief maps add a potential for even more accurate and detailed interpretation (indicated by yellow lines).
Field that are artificially illuminated from the east or west (Fig.
5c–d) tend to highlight the seismic noise features seen in the
dip maps (Fig. 4a).
The main faults on the Gullfaks Field are roughly N-trending
and E-dipping. They are therefore best observed on relief maps
illuminated from the east (bright faults, Fig. 5c) or the west
(dark faults, Fig. 5d). However, since the faults are commonly
curved, the visual effect is best when the maps are illuminated
from a more northerly or southerly direction (Fig. 5a–b).
Figure 6 shows how the use of seismic attribute maps greatly
improved the interpretation that existed before the 1991
reprocessing of the data. It is also possible, as shown in Fig. 7,
to cut a 3D-seismic cube along an interpreted seismic horizon
and add a relief map to that horizon. As a result, one can now
interactively interpret seismic E–W lines, N–S lines, random
lines, and time slices, and at the same time make use of the
seismic attribute maps.
Azimuth maps
Dalley et al. (1989) first demonstrated the use of seismic
azimuth maps for detection of small-scale faults. Later work
includes that by Voggenreiter (1991, 1993), Hoetz & Watters
(1992), and Dorn et al. (1995). The azimuth map (Fig. 4c)
highlights the most subtle changes in dip direction of the
interpreted seismic reflection. Since faults are often associated
with a change in dip direction as well as dip, these structures
Fig. 5. Seismic relief maps of the intra-Ness Fm. reflection in one of the rotated fault blocks on the Gullfaks Field. See Fig. 1 for location.
(a) Artificially illuminated from the north. (b) Artificially illuminated from the south, with current interpretation shown. (c) Artificially
illuminated from the east. (d) Artificially illuminated from the west, with current interpretation shown. Abundant N-trending (curvi-)linear
features that are best seen on relief maps illuminated from the east or west are, based on independent data, believed to be seismic noise. By
artificially illuminating the maps from the north or south, the overprinting effect of this noise can be greatly reduced.
J. Hesthammer and H. Fossen
Fig. 7. It is possible to add a seismic relief map, or any other attribute map to an interpreted seismic horizon in a 3D cube. This allows for
integrated seismic interpretation of E–W lines, N–S lines, random lines, time slices, and attribute maps. See Fig. 1 for location.
tend to be highlighted on the azimuth map. Folds (including
drag structures) and the general shape of the reservoir may also
be easily displayed.
The azimuth map is highly affected by seismic noise. This
problem may, to a large extent, be avoided by filtering the data
prior to generating the attribute map. Figure 4c shows a filtered
azimuth map from a fault block on the Gullfaks Field. Yellow
indicates dip to the east, green shows dip to the west, red shows
dip to the southeast, and blue indicates dip to the northwest.
Because yellow is the most eye-catching colour in the map,
E-dipping features will be highlighted in the figure. On the
Gullfaks Field, this will apply to most major faults. By letting
yellow represent dips to the south or north, the E-trending
structures will be highlighted.
Because the azimuth map efficiently displays larger and more
general changes in dip, folds are easily recognized. Thus, the
azimuth attribute map may help identify relay ramps or areas
affected by drag. Several gentle folds are observed in Fig. 4c
where the colours change from green (westerly dip) to blue
(northwesterly dip).
Amplitude maps
The most commonly used sample level seismic attribute map is
the amplitude map (Fig. 4d), which simply displays the amplitude value at any point along an interpreted seismic horizon.
This type of map was probably the first attribute map used
for seismic interpretation, and is commonly utilized in structural analyses (e.g. Buchanan et al. 1988; Flint et al. 1988;
Voggenreiter 1991). The amplitude map has been successfully
used on the Gullfaks Field for identifying oil-filled reservoirs
(the seismic response for oil-filled and water-filled sandstone is
different in the field). In addition, the amplitude of a seismic
reflection is typically weakened along structural lineaments such
as faults. It is thus possible to map fault traces along an
interpreted seismic reflection by analysing the seismic amplitude map. The larger faults on the Gullfaks Field are easily
recognized by a marked decrease in amplitude value. We have,
however, found it difficult to identify minor structures based on
the amplitude map alone, and the map is therefore always used
in conjunction with the dip and azimuth map for structural
analysis. In areas of good seismic data quality, however, minor
structures with little or no observable stratal offset on the
Gullfaks Field can sometimes be identified solely on the basis
of a decrease in amplitude value. The ability to do so depends
on the width and character of a fault’s damage zone, the
frequency of the seismic signal, and travel time. The edges of
the damage zone must be sufficiently separated to allow the
corresponding diffraction patterns from the seismic signal to be
All seismic data will contain a mixture of signal and noise
(Sheriff 1978). Signal in seismic work refers to reflections from
interfaces in the subsurface, whereas noise is anything which
obscures the signal. The noise can be separated into coherent
(systematic) and random ambient noise and have many different
sources (e.g. Sheriff 1977; Yilmaz 1987). Since reflections and
noise are always mixed together, all seismic attribute maps will,
to some extent, be influenced by seismic noise. An obvious
method for identifying real faults is to look for continuous
anomalies and disregard more distributed, non-linear anomalies
as seismic noise. However, coherent seismic noise may also
Attribute analysis in structural interpretation
Because of the upward widening of the triangular zone and the
angular relationships between bedding and faults, we interpret
the deformation within the triangular zone to be higher than
elsewhere (see Fossen & Hesthammer in press for a more
detailed discussion). In the model shown in Fig. 8, this
deformation is modelled as a shear deformation, where the
shear strain is highest in the triangular zone. The high footwall
deformation that may be inferred from the seismic attribute
maps would be inconsistent with this model. This indicates
that most of the (curvi-)linear features in the footwall position
of the fault blocks represent seismic noise rather than real
Fig. 8. The structural model resulting from forward modelling of
the Gullfaks Field implies rigid rotation of fault blocks combined
with internal shear deformation, where the shear plane dips more
steeply than the final orientation of the main faults. This model
takes into consideration the non-planar bedding geometry and
implies high hanging wall strain and low footwall deformation.
This simple model is consistent with seismic data, core data,
dipmeter data, as well as field analogue data. Modified from
Fossen & Hesthammer (in press).
appear as linear or curvi-linear features. In general, if such noise
is not properly understood, noise-related patterns may be
interpreted as faults, and the resulting structural maps may in
part be based on data that are not real. This may lead to the
abandonment of favourable drilling sites, and optimal well
positioning would probably not be obtained. It is therefore
important to distinguish between anomalies or features related
to seismic noise and those of real events. In our experience,
areas of poor seismic data quality may easily be recognized on
seismic attribute maps, especially on amplitude (Fig. 4d), dip
(Fig. 4a) and relief maps (Figs 4b & 5). In the Gullfaks Field,
areas of poor data quality are often located along the crest of
rotated fault blocks (i.e. in a footwall position of a domino fault
block, see Fig. 1). If these areas of poor data quality represent
areas of high fault densities, then the footwall side of the
rotated fault blocks are more deformed than the hanging wall
side, and the deformation is present as mostly N-trending
minor faults. With the large number of wells drilled, and the
enormous quantity of data collected, it is possible to distinguish
between seismic noise and real structures in large parts of the
Gullfaks Field. In the following, we discuss the most important
types of data analysed and methods applied to make this
(a) Structural modelling
Structural modelling based on geometric analysis of bedding and fault geometries in the Gullfaks Field (Fossen &
Hesthammer in press) has resulted in the simple model shown
in Fig. 8. The model involves both slip along faults, fault-block
rotation and internal deformation. Ideally, the non-planar
bedding surfaces define a characteristic upward-widening triangular zone in the hanging wall where bedding is particularly
non-planar and shallowly dipping. Outside of this triangular
zone, bedding is more planar. Since the bedding surfaces were
planar prior to deformation, the curved bedding traces reflect
internal deformation of the domino blocks (e.g. Lisle 1994).
(b) Dipmeter data
Analysis of dipmeter data on the Gullfaks Field indicates that
the deformation associated with faults is stronger in the
hanging wall than in the footwall (Fossen & Hesthammer in
press). This means that the zone of brittle (faulting) or ductile
(drag) deformation around faults is wider in the hanging wall,
and that footwalls are less deformed. The same conclusion has
been reached based on geological field work on faulted sandstones in Utah and Suez (unpublished) and on results from
physical modelling (Fossen & Gabrielsen 1996). These observations suggest that the (curvi-)linear features shown on the
seismic attribute maps in the footwall position of the rotated
fault blocks are not related to faults. Although examples of
gravitational failure such as those seen on the east flanks of the
Statfjord and Brent Fields to the west may result in a more
deformed footwall (Kirk 1980; Livera & Gdula 1990), no
evidence of such gravity collapse structures are observed related
to the rotated fault blocks on the Gullfaks Field. In addition,
the (curvi-)linear features observed on the relief maps dip to the
west, whereas gravity collapse structures would dip in the same
direction as the main faults; i.e. to the east.
(c) Stratigraphic logs
Stratigraphic log correlation is being carried out in detail on the
Gullfaks Field, thanks to many wells and the detailed stratigraphic model in the area. Missing (or repeated) sections down
to about 10 m and locally less (depending on stratigraphic level)
are detectable from log information in the reservoir (Fossen &
Rørnes 1996). This type of information from the many wells in
the reservoir does not show an increase in fault density in the
footwall portions of fault blocks where the seismic (curvi-)
linear features are observed on attribute maps (e.g. Fig. 5c).
Instead, there appears to be more faults located in the hanging
wall portions, which is consistent with the results from
structural modelling.
(d) Core data
Structural core analysis, where faults with displacements down
to the millimetre-scale can be identified, indicates a surprisingly
low fracture population in the footwall parts of the rotated fault
blocks on the Gullfaks Field. More than 400 m of cores were
analysed from two of the many (subvertical) wells located in a
footwall position to the rotated fault blocks. In the cores from
these two wells, 34/10-A-9H (Fig. 4b) and 34/10-3 (Fig. 5d),
5–10 micro-fractures with no more than millimetre-scale offset
were the only faults identified. There is always a possibility that
faults may be represented by missing intervals in incompletely
cored wells, or as non-cohesive rock which is occasionally seen
in some shaly intervals or in loosely consolidated sandstones.
J. Hesthammer and H. Fossen
Fig. 9. Fracture distribution around a
fault with 11 m of missing section in
well 34/10-A-15. See Fig. 1 for
location. This type of distribution of
fractures (micro-faults) around the fault
defines a damage zone of only a few
metres width, and is typical for cored
faults in the Gullfaks Field. Almost no
micro-fractures are observed outside the
fault damage zone. On the structurally
complex Gullfaks Field, several hundred
metres of continuous core without any
micro-faults suggest that deformation
was largely by a more widely distributed
grain reorganization rather than by
discrete and pervasive fracturing.
Because faults with throws down to less than 10 m can be
identified with confidence from log correlation with neighbouring wells (see above), such faults should be checked for in cored
intervals. Figure 9 shows the distribution of micro-fractures
around an identified fault with 11 m of missing section on
the Gullfaks Field (well 34/10-A15). The figure shows that
although the fractures are restricted to a damage zone a few
metres wide around the identified fault, several tens of fractures
exist within this zone. Because almost no fractures exist outside
of the damage zone, fracture analysis provides a clear definition
of the fault in its own right. Several faults that had been
identified solely by log correlation by Gullfaks sedimentologists
have been cored, and they all show well-defined fracture
frequency peaks of the type shown in Fig. 9. This fact strongly
suggests that all faults detected from log correlation should be
recognizable in cores. Core studies also indicate that there are
few faults with a throw of a metre-scale and up that are not
already detected by detailed log correlation. Based on this, too
few faults and micro-fractures exist to allow the (curvi-)linear
features observed on seismic attribute maps in footwall parts of
the fault blocks to be explained as fault structures.
(e) Dip angles
If the deformation within the rotated fault blocks occurred
mainly as a result of faulting rather than a more distributed
grain reorganization, dip angles calculated from dipmeter data
and from seismic reflections should generally be different. For
example, if antithetic faults are concentrated in the footwall
portion of the rotated fault blocks, the dip angles from
dipmeter data should be shallower than those indicated by the
seismic data (Fig. 10). Statistical analyses of dipmeter data from
48 wells (c. 23 km) from the Gullfaks Field reveal no significant
differences in dip angles from dipmeter data and seismic data.
This suggests that the main deformation mechanism was
something other than faulting of the reservoir. Most of the
internal fault-block deformation was probably accommodated
by a widely distributed ‘ductile’ reorganization (rotation and
translation) of grains without the influence of discrete zones of
grain-size reducing processes (Fossen & Hesthammer in press).
This conclusion is supported by the fact that even in a hanging
wall position, large cored intervals without micro-fractures are
identified. This deformation requires low grain-contact stresses,
which for the Gullfaks reservoir sands can be explained as
being due to shallow or no burial depth at the time of
deformation, and possibly also due to elevated fluid pressures.
Fig. 10. Illustration of the differences in dip as extracted from
seismic data and dipmeter data. In the case where sub-seismic
antithetic faults occur in the footwall part of the fault blocks, the
dip of bedding is lower from the dipmeter data than inferred from
the seismic data. For the case of synthetic minor faults, the
situation would be reversed. No such difference in dip is found
between dipmeter and seismic data on the Gullfaks Field.
Recognizing that the generally N-trending linear or curvi-linear
features on seismic attribute maps in a footwall position most
likely represent seismic noise, it is important to find the source
of this noise. Noise can have many different sources. Systematic
coherent noise may result from effects such as cable noise,
sea state noise, side-scattered noise, water-borne diffractions,
propeller noise, acquisition geometry, multiples and peg legs
(e.g. Yilmaz 1987). Systematic noise may also be generated by
the filter operations in the processing sequence. Random
seismic noise includes effects such as natural noise (e.g. wind
motion), incoherent seismic interference, instrumental noise
and imperfect static corrections (e.g. Fitch 1979).
The fact that the seismic (noise) features on the attribute
maps tend to parallel the strike of the seismic reflection may
suggest that the noise is a result of interference between the
dipping reflection that represents bedding and a subhorizontal
seismic signal such as a multiple, or between bedding and other
N-trending features. It is important to note that the line of
intersection between the real seismic reflection and the seismic
noise (e.g. remnants of multiples or coherent linear dipping
Attribute analysis in structural interpretation
Fig. 11. A conjugate set (E- and W-dipping) of seismic artefacts cut through most of the seismic data on the Gullfaks structure, and is
especially pronounced where the data are poor. This coherent noise affects data both above and within the reservoir (the auto-tracked red
reflector is above the reservoir). The interference of this seismic noise with weak reflections causes the weaker reflections to break up into
segments that rotate subparallel to the dipping seismic noise features. Thus, the horizon may come to have the appearance of being highly
faulted although well data show that it is not. See Fig. 1 for location.
Table 1. Key recording and processing information for the 1985 seismic dataset of the Gullfaks Field
Gun depths:
Total volume:
Shot interval:
Record length:
Recording fold:
Sample interval:
7.5 m
2856 CU.IN
25.00 m
2 ms
No. of groups:
Group interval:
Cable depth:
Near group dist.
56 m
Signature deconvolution
FK demultiple
Dip moveout
Predictive deconvolution
Bin sort to nominal 48 fold
Bin size:
Line spacing
12.5 m in line
50.0 m cross line
25.0 m
*Recorded by GECO, April–August 1985.
†Reprocessed by Seismograph Service, April 1991–March 1992.
Pre migration filter
In-line finite difference migration
Cross line interpolation to 12.5 m
Cross line finite difference migration
FK filter: with 40% feedback
Predictive deconvolution
Zero phase conversion
J. Hesthammer and H. Fossen
Fig. 12. W–E orientated seismic in-line through some of the rotated fault blocks on the Gullfaks Field. See Fig. 1 for location. The deeper
Amundsen Fm. (see Fig. 2) reflection is weaker than the intra-Ness Fm. reflection and therefore more affected by seismic noise. The
intra-Ness Fm. reflection (Ness1) is seen to become weak just below the Base Cretaceous reflection. This may be due to the proximity of
the Base Cretaceous reflection (interference with Base Cretaceous ‘follow-cycle’) or to gas escape at the crest of the fault block, which
results in reduced seismic data quality. The Amundsen Fm. reflection appears weaker vertically below the point where the intra-Ness Fm.
reflection becomes weak, thus supporting the latter theory.
noise) is represented as a (curvi-)linear feature on the attribute
maps, similar to the features generated by faults. This complicates the separation between real features and seismic noise.
Generally, seismic noise is more apparent where the seismic
reflection is weak. On the Gullfaks Field, the overall relatively
poor data quality at Jurassic levels is a result of the combination
of strong water layer multiples, shallow gas and smaller
amounts of gas in the Tertiary and Cretaceous strata. Within the
reservoir, the structural complexity also results in a weaker
signal-to-noise ratio. Seismic E–W lines on the Gullfaks Field
show a network of conjugate (E- and W-dipping) features (Fig.
11). In 1995, an additional seismic dataset was collected from
parts of the Gullfaks Field. These data were identically processed to the 1985 dataset and manipulated so that the
strengths and positions of the seismic signals were comparable.
A comparison of the two shows that the dipping features are
present in both. A difference cube cancels all real reflections,
whereas the dipping coherent noise as well as random noise
from both sets remain. This shows that the dipping noise is
somewhat offset between the two datasets, and the difference
cube thus displays the noise present in both. The fact that real
reflections are cancelled whereas dipping noise remains suggests that the noise is a result of the data recording procedure
rather than inhomogeneities in the rocks. Although several
attempts have been made to increase the seismic signal-to-noise
ratio on the Gullfaks Field (Table 1), much of this coherent
linear dipping noise remains. Yilmaz (1987) anticipated that
most linear noise observed on a stacked section results from
scattered energy along the flanks of its travel-time curve that
was stacked together with any high-velocity primary energy. In
theory, however, side-scattered noise signals should in 3D
seismic be migrated to their proper position. Hoetz & Watters
(1992) related the (curvi-)linear features observed on a dip map
to reflection discontinuities resulting from migration and/or
stack errors in seismic processing. It is also possible that the
linear dipping events on the stacked sections result from
‘streamer noise’ caused by pull and drag on the streamer. On
the Gullfaks Field, the dipping coherent noise was suppressed
by DMO (dip moveout), migration of the data and postmigration f–k dip filtering. However, since the dips and strikes
of bedding and faults are subparallel to the dipping coherent
linear noise, processing methods that remove all dipping noise
would also result in the removal of primary reflections. It was
therefore impossible to obtain a processed seismic dataset free
of dipping coherent noise. An alternative solution, that will be
tested on the Gullfaks Field, is to combine the 1985 and 1995
datasets to increase the signal-to-noise ratio.
The dipping coherent linear noise on the Gullfaks Field is
present both in the deformed reservoir and in the relatively
undeformed post-rift strata above the Base Cretaceous unconformity, and is especially pronounced where the seismic
primary reflections are weak. Within the reservoir zone, the
Attribute analysis in structural interpretation
Fig. 13. Filtering of the interpreted and snapped (or autotracked) seismic horizon may have a large effect on the final result. The filter used
with most success on the Gullfaks Field is a median filter that removes extreme values. When snapping or autotracking a horizon without
any filters applied, much seismic noise will be recorded. The figure shows the effect of median filters of 6#6 and 9#9 (refers to grid cell
sizes of n E–W lines and n N–S lines; i.e. 75#75 m and 112.5#112.5 m respectively) applied to the interpreted and snapped Amundsen
Fm. reflection on the Gullfaks Field. It is clear how a median filter removes local ‘spikes’ and allows the interpreter to ‘see through’ the
noise. However, some noise remains and must be ‘removed’ by other methods, such as artificially illuminating the seismic dip map. See Fig.
1 for location.
coherent noise interferes with dipping reflections and causes
the reflection to break up and rotate towards parallelism with
the noise features. The weaker the reflection, the stronger the
effect of interference. This explains why the noise observed on
the dip maps is more pronounced along the weaker reflections.
The stronger reflections are mostly affected in the footwall
parts of the rotated fault blocks (Fig. 4), where the seismic
signal generally is poorer.
Several explanations exist for the weaker reflections observed
in footwall parts of the rotated fault blocks. First, remnants of
subhorizontal multiples, especially from the top of the Cretaceous, which evidently occur in the dataset, will cause interference with the dipping reflection. A second explanation may be
due to the proximity to the Base Cretaceous reflection (Fig. 12):
‘follow-cycles’ will obscure the data immediately below the
stronger Base Cretaceous signal. A third explanation may be the
escape of gas at the crest of the rotated fault blocks (Fig. 12). If
gas has migrated to shallower levels, the high acoustic impedance contrast of shallow gas zones may result in poor seismic
data quality below the shallow gas pockets. Also, hydrocarbons
within the reservoir may affect the seismic signal. Where
oil-filled sandstone occurs immediately beneath the Base
Cretaceous unconformity (commonly at the crest of the rotated
fault blocks), the resulting acoustic impedance causes a strong
reflection. This will obscure the seismic data below and weaken
the reflections within the reservoir. Oil-filled Ness Fm. may
also weaken the generally strong intra-Ness Fm. reflection, thus
causing a poorer reflection in a footwall position.
It is clear from the discussion above that we cannot point to
one single explanation for the origin of seismic noise or what
causes poor seismic data quality. On the other hand, this is not
required for an integrated use of seismic and well data to
distinguish real features from (curvi-)linear noise features on
the seismic attribute maps. It is, however, crucial to identify the
characteristics of the noise and areas of poor seismic data
quality in order to produce the best possible interpretation of
the data set.
To take full advantage of the seismic attribute maps, the
autotracked or refined interpretation should be filtered so that
the effect of seismic noise is minimized. Figure 13 shows the
effect a median filter may have on the final results. It is clear
that, rather than losing detail, the median filter tends to enhance
subtle structures by removing local extreme values related to
seismic noise. Noise will still be present, but it is easier to
identify the more continuous lineaments related to real structural trends. Since noise can have a preferred orientation, it may
be worth exploring filters such as the median filter with
different x- and y-values. On the Gullfaks Field, a large x-value
may remove much of the N-trending seismic noise, whereas a
low y-value will keep as much detail as possible in an E–W
From our experience with the Gullfaks Field, the dip, relief,
amplitude and azimuth attribute maps are most useful in
mapping small-scale faults from seismic data. In contrast to the
dip maps, the relief maps will, depending on illumination
azimuth, reveal lineaments with preferred orientations. Combined with a colour contour map, a very strong visual effect is
J. Hesthammer and H. Fossen
created, and the relief maps become a powerful tool for
identifying detailed structures. The azimuth maps, when filtered
properly, will highlight subtle changes in dip direction (folds) as
well as larger faults. The amplitude maps help to identify areas
of good data quality and areas affected by much deformation or
seismic noise. Larger structural trends may be observed on the
amplitude maps as well.
In areas with existing seismic interpretations, snapping to
nearest amplitude minimum or maximum is a quick and
efficient method for providing input grids to seismic attribute
mapping. The resulting grids will be similar to those obtained
by auto-tracking. The grids are then filtered depending on the
structural features wanted and the amount of seismic noise
present. Although seismic data on the Gullfaks Field may, in
many places, be of poor quality, experience has shown that it is
possible to filter and process the data so that even minor
structures (faults with offset less than 20–30 m) can be identified. In order to obtain the best results, it is critical that time is
spent on finding the right parameters for input, the right type
and amount of filtering, and the correct colour setting for the
final display of the seismic attribute maps.
We stress the importance of using any available data or
method to help separate noise and real features. This includes
detailed well log correlation, dipmeter data, structural core
analysis, field analogue observations, section balancing, and
physical modelling. If observed (curvi-)linear features on seismic attribute maps are believed to be related to seismic noise,
efforts should be made to find the source of this noise.
Core data, dipmeter data, log correlation, structural modelling, and field studies all indicate that a family of (curvi-)linear
features that stand out on seismic attribute maps from the
Gullfaks Field cannot be geological fault structures. Furthermore, faulting cannot be responsible for all of the internal block
deformation in the Gullfaks Field, where the footwall of the
rotated fault blocks are less deformed than the hanging wall
side of the same blocks. Due to the weak degree of consolidation at the time of deformation, it is likely that deformation
by distributed grain reorganization dominated over localized
cataclasis (discrete micro-faulting). This type of deformation is
not detectable from seismic attribute maps, although it may be
significant in the Gullfaks and other North Sea oil fields.
The authors want to thank Norsk Hydro, Saga Petroleum and
Statoil for permission to publish these results. The article has
benefited from comments by Roy H. Gabrielsen, Lars Sønneland,
Lars K. Strønen and Tor E. Ekern. We thank Jon O. Henden and
Chris Townsend for stimulating discussions. The colour-contoured
relief map was created with help from Asle Strøm.
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Received 26 April 1996; revised typescript accepted 22 September 1996