oa Detecting Stratigraphic Features via Cross-Plotting of Seismic Discontinuity Attributes and Their Volume Visualization

Fold and fault geometries, stratal architecture and large-scale depositional elements (e.g. channels,<br>incised valley-fill and turbidite fan complexes) are often difficult to see clearly on vertical and<br>horizontal slices through the seismic reflection data. Consequently, visualization techniques are used<br>for viewing the data, whether it’s the input seismic data or derived data in terms of seismic attributes.<br>Such visualization helps extract meaningful information, allows for greater interpretation accuracy and<br>improves efficiency. 3D volume rendering is one form of visualization that involves opacity control to<br>view the features of interest ‘inside’ the 3D volume. A judicious choice of opacity applied to edgesensitive<br>attribute sub-volumes such as curvature or coherence co-rendered with the seismic amplitude<br>volume can both accelerate and lend confidence to the interpretation of complex structure and stratigraphy.<br>In addition to co-rendering, we evaluate an interpretation workflow that cross-plots pairs of edgesensitive<br>attributes. By crossploting coherence and an appropriate curvature attribute, we can define a<br>polygon that highlights “clusters” that exhibit low coherence (indicating a discontinuity) and high<br>curvature (indicating folding, flexing, fault drag, or differential compaction). Modern volume<br>interpretation software allows us to link and display these interpreter-defined clusters in the seismic<br>volume for further examination. Once identified interactively, such visual ‘clustering’ can be used to<br>supervise geobody delineation using neural networks and other classification algorithms. This saves the<br>seismic interpreters considerable time and effort. We illustrate this new workflow through application<br>to several 3D seismic surveys recently acquired in western Canada and demonstrate that multiattribute<br>volume co-rendering and clustering provides a powerful tool that leads to a better understanding of the<br>spatial relationships between seismic attributes and the geologic objectives being pursued.