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Deep Learning for Salt Body Detection: A Practical Approach
- Publisher: European Association of Geoscientists & Engineers
- Source: Conference Proceedings, 82nd EAGE Annual Conference & Exhibition, Oct 2021, Volume 2021, p.1 - 5
Abstract
Summary
Interpreting salt bodies in the subsurface is a challenging manual task that can take weeks to complete. Obtaining accurate picks of salt is very important, because errors in the placement of salt can result in severe degradation of the seismic image. To meet the challenges of speeding up imaging workflows and retaining accurate salt picks, we evaluate three deep learning approaches: a 2D window-based convolutional neural network, a 3D window-based convolutional neural network, and finally a 2D “U-Net” approach. A 3D seismic volume from the deep-water field Julia in the Gulf of Mexico was used to test these approaches. The Julia field has complex salt structures with overhangs and inclusions, and the thickness of salt can reach up to 5 km. The U-Net architecture proved to be the most accurate of the three methods tested, predicting the placement of salt at 98% accuracy, as compared to the human interpretation. Beyond accuracy, U-Net also proved to be the fastest, requiring only 3.5 hours to predict salt on the 3D seismic volume. The results presented here along with other recent studies of deep learning for salt interpretation represent a clear shift in the seismic interpretation workflow.
© EAGE Publications BV