Automated 3D seismic facies mapping of Upper Paleozoic carbonates in the southwestern Norwegian Barents Sea

1Rafaelsen, B., 2Elvebakk, G., 3Hunt, D., 1Andreassen, K., 4Randen, T.

1Department of Geology, University of Tromsø 2Norsk Hydro ASA, Harstad 3Norsk Hydro ASA, Bergen 4Schlumberger Stavanger Research, Stavanger

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 (IST-1999-20500), 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 two-

headed 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 build-ups 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

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