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

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. From the primary results using the TGS Salt Identification Challenge dataset, the MultiResU-Net outperformed the classic U-Net in identifying salt bodies and showed good agreement with the ground truth. Additionally, in the case of complex salt body geometries, the MultiResU-Net predictions exhibited some intriguing differences with the ground truth interpretation. Although the network validation accuracy is about 95%, some of these occasional discrepancies between the neural network predictions and the ground truth highlighted the subjectivity of the manual interpretation. Consequently, this raises the need to incorporate these neural networks that are prone to random perturbations to QC manual geophysical interpretation. To bridge the gap between the human interpretation and the machine learning predictions, we propose a closed-loop-machine-learning workflow that aims at optimizing the training dataset by incorporating both the consistency of the neural network and the intellect of an experienced geophysical interpreter.