Cheng Zhan

Deep Semi-Supervised Learning Approach in Characterizing Salt on Seismic Images

Licheng Zhang, Zhenzhen Zhong, Meng Zhang, Tianxia Zhao, Varun Tyagi, Cheng Zhan

The salt body characterization is crucial in exploration and drilling. Due to its mobility, salt can move extensively to create diapirs, which generate significant traps for hydrocarbons, meanwhile, they present drilling hazards, as salt intrusion distorts the stress field making wellbore stability challenging in the geomechanical models. Here we utilized deep learning to identify salt body based on seismic images. Many techniques from the domains of geophysics and data science, have been successfully incorporated into the work-flow. The seismic images are produced from various locations. Here we use convolutional neural network that is the main methodology to process images segmentations. The underlying architecture is dedicated to restoring pixel position. In addition, the highlight here is Semi-Supervised learning, and we utilized the large unlabeled test set to gain more understanding of the data distribution, and the pseudo labeling of unlabeled test set comes from prediction. The metric implemented is “IOU”, Intersection over Union, which fundamentally measures how much area the predicted salt body overlay with the true answer. Our IOU score is 0.849, equivalent to 95% of the predicted salt body is correct. Challenges still exist as geology varies across locations, and the corresponding features might not share similar statistical properties.