Poster

[SE-P-01] Enhancing seismic fault extraction through multiple Ant Tracking on cloud environment.

*Mohamed Amrouche¹, Ayako Otakara¹, Kwangho Lee¹, Misa Okada¹ (1. Schlumberger (Japan))

Manual seismic faults interpretation can be one of the most time-consuming tasks in reservoir modeling. To accelerate the fault extraction process, some existing methods based on seismic attributes analysis such as the Ant Tracking method are already well adapted by experienced interpreters. The Ant Tracking algorithm is an innovative workflow that introduces an original paradigm in fault interpretation, by emulating the behavior of ant colonies in nature to track and extract faults and fractures network from the seismic data. This method has already showed good results on improving seismic interpretation workflows. However, some of the limitations of the ant tracking remain the range of uncertainty on properly mapping the fault continuity in lower quality seismic data and at deeper structures, where the seismic noise is more prominent. The Ant Tracking process also requires high spec hardware to properly perform, specially for large 3D seismic volumes. In this study, we proposed a new approach for Ant Tracking interpretation by averaging multiple estimations of the seismic data using different seismic attributes and parameters tunings. Our results show that the proposed combined and iterative strategy enables to effectively reduce the noise level in the seismic data and improve the continuity of the faults network, taking advantage of the powerful cloud environment to enhance the computation speed.