Multi-attribute K-means Cluster Analysis for Salt Boundary Detection
H. Di, M. Shafiq and G. AlRegib
Event name: 79th EAGE Conference and Exhibition 2017
Session: Seismic Interpretation - Analytics and Machine Learning for Interpretation
Publication date: 12 June 2017
Info: Extended abstract, PDF ( 612.31Kb )
Price: € 20
Robust detection of salt bodies has been the recent focus of hydrocarbon exploration and production from 3D seismic surveying in the last decade. This study presents a new salt-boundary detection method based on multi-attribute k-means cluster analysis, which consists of two major components. First, a suite of seismic attributes is selected and computed from the seismic volume, from which the salt boundaries can be readily differentiated from the surrounding non-boundary features in various ways. Second, the k-means cluster analysis is performed in the attribute domain and generates a probability volume, which depicts the boundaries of salt domes observed in the original seismic amplitude. The proposed method is verified through applications to the F3 seismic dataset of multiple salt bodies over the Netherlands North Sea. The results demonstrate not only good match between the detected salt boundaries and the seismic images, but also great potential for accurate salt surface/body extraction to assist structural framework modeling in the zones of geologic complexities due to salt domes.