DOI: https://doi.org/10.4043/31169-msAug 9, 2021Proceedings Paper

MultiResU-Net: Neural Network for Salt Bodies Delineation and QC Manual Interpretation

Yesser HajNasser

Abstract

Accurate delineation of salt bodies is essential for the characterization of hydrocarbon accumulation and seal efficiency in offshore reservoirs. The interpretation of these subsurface features is heavily dependent on visual picking. This in turn could introduce systematic bias into the task of salt body interpretation. In this study, we introduce a novel machine learning approach of a deep neural network to mimic an experienced geophysical interpreter's intellect in interpreting salt bodies. Here, the benefits of using machine learning are demonstrated by implementing the MultiResU-Net network. The network is an improved form of the classic U-Net. It presents two key architectural improvements. First, it replaces the simple convolutional layers with inception-like blocks with varying kernel sizes to reconcile the spatial features learned from different seismic image contexts. Second, it incorporates residual convolutional layers along the skip connections between the downsampling and the upsampling paths. This aims at compensating for the disparity between the lower-level features coming from the early stages of the downsampling path and the much higher-level features coming from the upsampling path.

References

Sep 20, 2019·Neural Networks : the Official Journal of the International Neural Network Society·Nabil Ibtehaz, M Sohel Rahman

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