16FORCE Bugge Automatic facies classification and horizon tracking in 3D seismic data
Channel: Force Seminars
Date: 2018-10-02

Automatic facies classification and horizon tracking in 3D seismic data
Aina J. Bugge*, Kalkulo AS, Simula Research Laboratory and Jan E. Lie,, Lundin Norway AS.



For our facies classification, we generate training data using a local binary pattern (LBP) texture descriptor together with unsupervised clustering. A supervised machine-learning algorithm then learns from the training data to perform the final seismic facies classification. We assume that each seismic facies represent a seismic sequence, and track seismic horizons within each defined sequence using a dynamic time warping grid. The dynamic time warping grid iteratively compare seismic traces, identifies the corresponding reflective events within each trace in the grid, and outputs all seismic horizons in the given sequence at the same time. The horizon tracker is completely automatic and minimally affected by amplitude changes along a reflector and faulting. We test our method on a structurally complex dataset in the SW Barents Sea, specifically targeting rotated Mesozoic fault blocks on the Polhem Sub-platform.