Automated 3D seismic facies mapping of Upper Paleozoic carbonates in the
southwestern Norwegian Barents Sea
1
Rafaelsen, B., 2Elvebakk, G., 3Hunt, D., 1Andreassen, K., 4Randen, T.
1
Department of Geology, University of Tromsø
Norsk Hydro ASA, Harstad
3
Norsk Hydro ASA, Bergen
4
Schlumberger Stavanger Research, Stavanger
2
bjarne@ibg.uit.no, Universitetet i Tromsø, N-9037 Tromsø, Norway
Introduction
Manual 2D seismic facies analysis has become a technique that routinely is used to
define the environmental setting and seismic facies of hydrocarbon prospects. Seismic facies
are defined as groups of seismic reflections whose parameters (configuration, continuity,
frequency and interval velocity) differ from adjacent groups. The method has traditionally
been developed for 2-D seismic data, and is extremely time consuming when mapping facies
of large volumes. The development of new software for automated seismic texture mapping
opens for a more efficient, quantitative and reliable seismic facies analysis. In addition to
classical seismic facies mapping, the software also detects patterns that may easily be ignored
or misinterpreted as seismic noise when manually inspecting the data line by line. A
procedure for automated seismic facies analysis is, as part of the EU project TriTex (IST1999-20500), tested on the Upper Paleozoic carbonate platforms of the southwestern
Norwegian Barents Sea. In this test we have studied two 3-D seismic surveys (Loppa High
and Finnmark Platform), covering more than 1500 km2 (Fig. 1).
Geological setting
In the southwestern Barents Sea, Carboniferous and Permian rifting led to the
development of a mosaic of fault-controlled basins and more stable platform areas
Figure 1. Location of the studied 3-D seismic areas.
(Beauchamp and Desrochers, 1997). In the study area, the Upper Carboniferous - Lower
Permian succession consists mainly of shallow marine, locally evaporitic, warm-water
dolomite-dominant carbonates with Palaeoaplysina-phylloid algal build-ups of the Gipsdalen
Group (Larssen et al. 2002). During the Permian the study area drifted northwards and in the
Sakmarian an abrupt change towards cooler climate conditions took place. This cooling trend
marked the transition to the overlying calcite-dominated Bjarmeland group where large
bryozoan-Tubiphytes cementstone build-ups of intra Sakmarian to Kungurian age occur
(Blendinger et al., 1997). In the Bjarmeland and Tempelfjorden groups (Lower to Upper
Permian) limestones dominate, while cherty limestone, shale and siltstone are represented in
the uppermost Permian. Prior to the Triassic transgression, a c. 25 Ma period of sub-aerial
exposure is interpreted to have lead to the extensive karstification of the Lower Paleozoic
Loppa High succession (Hunt et al., 2003).
Automated 3D seismic facies mapping
A number of attribute cubes, that each enhances specific seismic parameters, have
been generated from the original 3-D seismic data (Fig. 2A-D). Several hundred training
points were then manually selected as representative of specific seismic textures (example
shown in Fig. 2A). Visual assessment of their cluster distribution optimized their cluster
distribution prior to classification of a data volume. Classification of a small sub-cube of the
seismic data then proceeded using a number of attribute cubes, selected from visual
assessment of their potential to differentiate between the textures trying to be mapped. Several
iterations of this procedure were required to produce an optimal classification of the entire
volume.
Figure 2. From the original seismic cube (A), several attribute cubes (B, C, and D) were generated. Each
attribute cube enhances specific parameters and is combined with hundreds of user-selected training points in
order to produce map patterns (i.e. E) that are normally impossible to differentiate based on seismic amplitude
data alone. The depression on the Top Paleozoic surface (A) is interpreted to be located above a collapsed
palaeo-karst cavern (see loss of reflector continuity below depression) and assigned to a chaotic class (E). E) The
chaotic texture class (red) is interpreted to isolate areas within and overlying the Upper Paleozoic carbonates
affected by the collapse of buried palaeo-karst features, i.e. unfilled palaeo-caverns (E). E) Attribute cubes
utilized: chaos, projected principal gradient, volume reflection spectrum, gradient and fault edge). PPG =
Principal projected gradient, VRS = Volume reflection spectrum. For location, see Fig. 3.
Results
Karst distribution on the Top Paleozoic surface on the Loppa High appears to be
controlled by palaeo-depressions, faults and bedrock lithologies. Local circular depressions on
the Top Paleozoic surface are interpreted to represent collapsed palaeo-karst caverns (Fig.
2A). On dip maps the collapsed karst occur as dark circular features as well as large NE-SW
trending elliptically-shaped depressions preferentially located along synsedimentary faults
(Fig. 3A). In the Upper Paleozoic section of the automated seismic facies classified cube,
these features correlate with the chaotic class (Fig. 3B) and is therefore interpreted as karst
features. The chaotic class extends vertically above the collapsed carbonates and into the
overlying Triassic succession (just above the Top Paleozoic horizon in Fig. 2E), where it is
interpreted as collapse features in the basal parts of the Triassic, related to underlying karst.
These Lower Triassic features had previously not been detected by manual interpretation, and
indicate that the automated classification provide added value to the user. While dip maps
provide a surface-based interpretation of the karst, they tell little of its 3-D form within the
upper Paleozoic carbonate succession. Rendering of the karst sub-volume suggests that the
caverns form an interconnected network within the Upper Paleozoic carbonates (Fig. 4).
On the Finnmark Platform, automated seismic facies classification has been used to
classify carbonate build-ups and evaporites. As both build-ups and evaporites have significant
acoustic impedance contrasts, they have so far been assigned to the same class, but work is
being performed in order to subdivide them into two separate classes.
Figure 3. A) Dip-map of Top Gipsdalen. B) Classified map of Top Gipsdalen (chaotic class is red). Black twoheaded arrows indicate the profile shown in Fig. 2.
Conclusions
A fundamental advantage of the SeisClass 3D software over manual 2-D seismic
identification and mapping is that it is much faster and able to analyze data from multiple
attribute cubes, producing map patterns that are hard to detect by visual assessment from
amplitude data alone. From the original seismic cube the attribute cubes, which each enhances
specific parameters, are used in combination with carefully user-selected training points. The
supervised automated facies classification and mapping provide a significant contribution to
the identification and interpretation of karst-related features, evaporites and carbonate buildups in the study area.
Figure 4. Seismic lines combined
with a sub-volume of the
classified cube. The classified
cube reveals the 3-D extent of the
chaotic class (red volume), which
appear to be semi-parallel to the
platform edge. The volume of the
sub-cube is c. 300 m high and 1
km2. Attribute cubes utilized:
flatness, gradient, fault edge,
volume reflection spectrum and
variance (Carrillat et al., 2002).
Acknowledgements
Norsk Hydro ASA and the European Communities project TriTex (IST-1999-20500) are acknowledged for
funding the research project. Norsk Hydro ASA, Statoil ASA, Norsk Agip A/S and Fortum Petroleum A/S are
acknowledged for providing the seismic data. We offer our sincere thanks to A. Carrillat for valuable input and
fruitful discussions. The University of Tromsø acknowledges GeoQuest for computer software and guidance on
technical issues. The map on Fig. 1 was generated with GMT.
References
Beauchamp, B. and Desrochers, A., 1997, Permian warm- to very cold-water carbonates and cherts in northwest
Pangea, in N. P. James and J. A. D. Clarke, eds., Cool-water carbonates: Tulsa, Oklahoma, Society for
Sedimentary Geology, Special Publication, 56, p. 327-347.
Blendinger, W., Bowlin, B., Zijp, F. R., Darke, G. and Ekroll, M., 1997, Carbonate buildup flank deposits: an
example from the Permian (Barents Sea, northern Norway) challenges classical facies models:
Sedimentary Geology, 112, p. 89-103.
Carrillat, A., Randen, T., Sønneland, L. and Elvebakk, G., 2002, Seismic stratigraphic mapping of carbonate
mounds using 3D texture attributes: EAGE 64th Conference & Exhibition, Florence, Italy. Extended
abstract.
Elvebakk, G., Hunt, D. W. and Stemmerik, L., 2002, From isolated buildups to buildup mosaics: 3D seismic
shed new light on the upper Carboniferous-Permian fault controlled carbonate buildups, Norwegian
Barents Sea: Sedimentary Geology, 152, p. 7-17.
Hunt, D., Elvebakk, G., Rafaelsen, B., Pajchel, J., Hogstad, K., Robak, H. and Randen, T., 2003, Palaeokarst
Recognition & 3D Distribution: New Insights From The Upper Palaeozoic, Loppa High, Barents Sea:
Extended abstract, 65th EAGE conference & exhibition, Stavanger, June 2nd-5th 2003.
Larssen, G.B., Elvebakk, G., Henriksen, L.B., Kristensen, S-E., Nilsson, I., Samuelsberg, T.J., Svånå, T.A.,
Stemmerik, L., Worsley, D., 2002, Upper Palaeozoic lithostratigraphy of the Southern Norwegian
Barents Sea: NPD Bulletin, v. 9, 76 pp., 63 figs., 1 tbl.
http://www.npd.no/Norsk/Produkter+og+tjenester/Publikasjoner/Oversikt+sokkelpublikasjoner/npd+bul
letin.htm.
Stemmerik, L., Elvebakk, G. and Worsley, D., 1999, Upper Palaeozoic carbonate reservoirs on the Norwegian
Arctic Shelf: delineation of reservior models with application to the Loppa High: Petroleum
Geoscience, 5, 173-187.