From fault likelihood attributes to automatic fault building
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Author: subsurfaceAI

Dr. Rongfeng Zhang presented the latest development of attribute at GeoConvention 2018

From fault likelihood attributes to automatic fault building

At the GeoConvention in Calgary, Alberta May 2018 Dr. Rongfeng Zhang presented the latest development of attribute. This talk address from fault likelihood attributes to automatic fault building.

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Summary
Fault interpretation is a key step in seismic structural interpretation and it also lays the basis for proper quantitative interpretation, inversion and modeling. If the working area has lots of faults, the structural interpretation can be very time-consuming. Because of noise, and different background experiences of the interpreter, the results can also be subjective. In this abstract, based on a real 3D seismic data set with lots of different faults, I will demonstrate a workflow that calculates an attribute (fault likelihood) to better delineate faults for automatic extraction. By replacing most of the manual work with the computer algorithm, we not only dramatically improve efficiency and save time, but also generate objective results. Some challenges to this will be discussed.

Read the full paper here.