Seismic image segmentation based on Deep Learning for the Characterization of Hydrocarbon Reservoirs in the Oil & Gas Industry: Comparative study

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dc.contributor.authorAnis MOHAMMEDI
dc.date.accessioned2025-01-21T21:06:02Z
dc.date.available2025-01-21T21:06:02Z
dc.date.issued2025-01-21
dc.description.abstractAccurate identification and delineation of Salt Domes from seismic images play a critical role in geological studies and resource exploration. How ever, traditional methods often struggle with complex geological structures and require extensive manual intervention. This work addresses these chal lenges by proposing a deep learning-based approach for semantic segmen tation of Salt Domes. We introduce novel techniques including Transform ers for spatial context aggregation, U-Net for precise feature extraction, and VAE-Liquid layers for enhanced representation learning. Through rigorous experimentation and evaluation on real-world datasets, we demonstrate the effectiveness of our approach in automating and improving the accuracy of Salt Dome identification. This work contributes to advancing automated ge ological analysis, offering insights into subsurface structures vital for both exploration and hazard assessment
dc.identifier.urihttps://dspace.estin.dz/handle/123456789/29
dc.language.isoen
dc.publisherTassadit
dc.subjectSemantic Segmentation
dc.subjectSeismic Images
dc.subjectSalt Domes
dc.subjectResource Exploration
dc.subjectDeep Learning
dc.subjectTransformers
dc.subjectU-Net
dc.subjectVAE-Liquid Layers
dc.titleSeismic image segmentation based on Deep Learning for the Characterization of Hydrocarbon Reservoirs in the Oil & Gas Industry: Comparative study
dc.typeThesis

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