Many Attributes Shine Light on Inversion
Conventional seismic reflectivity compared to a realization of our K-means clusters (images displayed in Petrel).
We are integrating multi-attribute analysis of seismic data within our cooperative inversion workflows. Seismic attributes can characterise volumes by, for example, common seismic amplitude distribution, frequency content, texture, coherency or dip angle. We select sets of seismic attributes then use methods such as K-means clustering to create spatial groupings that are expected to have common macroscopic rock properties. These clusters then help form a volumetric framework that can be integrated into our cooperative inversion workflows with the outcome being new subsurface conductivity distributions that reveal detail that may otherwise remain hidden.