P-392 Interpreting fractures through 3D seismic discontinuity attributes and their visualization Satinder Chopra, Arcis Corporation, Calgary Summary Fractures can enhance permeability in reservoirs and hence impact the productivity and recovery efficiency from them. Fold and fault geometries, stratal architecture and large-scale depositional elements (e.g. channels, incised valley-fill and turbidite fan complexes) are often difficult to see clearly on vertical and horizontal slices through seismic reflection data. Seismic discontinuity attributes help us in characterizing stratigraphic features that may comprise reservoirs and form integral part of most interpretation projects completed today. Coherence and curvature are two important seismic attributes that are used for such analysis. However, for extracting accurate information from seismic attributes, the input seismic data needs to be conditioned optimally. This includes noise removal, using robust dipsteering options and superior algorithms for computation of seismic attributes. Curvature attributes in particular exhibit detailed patterns for fracture networks that can be correlated with image logs and production data to ascertain their authenticity. One way to do this correlation is to manually pick the lineaments seen on the curvature displays for a localized area around the borehole, and then transform these lineaments into rose diagrams to compare with similar rose diagrams obtained from image logs. Favourable comparison of these rose diagrams lends confidence in the interpretation of fractures. Another way is to generate automated 3D rose diagrams from seismic attributes and correlate them with other lineaments seen on the coherence attribute for example. Finally, volume visualization of stratigraphic features is a great aid in 3D seismic interpretation and can be greatly aided by adopting cross-plotting of seismic discontinuity attributes in the interpretation workflow. Introduction Characterization of fractures is essentially the understanding of fracture patterns, so that appropriate ways can be devised for effectively draining out fractured reservoirs. The presence of naturally occurring fracture networks can lead to unpredictable heterogeneity within many reservoirs. Alternatively, fractures provide high permeability pathways that can be exploited to extract reserves stored in otherwise low permeability matrix rock. Consequently, the detection and characterization of fractures is of great interest which is driving significant improvements in azimuthal AVO, image-log breakout interpretation, and seismic attribute analysis. Surface seismic data has long been used for detecting faults and large fractures, but recent developments in seismic attribute analysis have shown promise in identifying groups of closely spaced fractures or interconnected fracture networks. Coherence and curvature attributes for fracture detection The coherence attribute has been used for detection of faults and fractures over the last several years. With the evolution of the eigen-decomposition algorithms, coherence is able to further improve the lateral resolution and produce relatively sharp and crisp definition for faults and fractures. However, volume curvature attributes have shown promise in helping us with fracture characterization (AlDossary and Marfurt, 2006; Chopra and Marfurt, 2007a and b, 2008). By first estimating the volumetric reflector dip and azimuth that represents the best single dip for each sample in the volume, followed by computation of curvature from adjacent measures of dip and azimuth, a full 3D volume of curvature values is produced. There are many curvature measures that can be computed, but the most-positive and most-negative curvature measures are the most useful in that mapping subtle flexures and folds associated with fractures in deformed strata. In addition to