Browsing by Author "Anis MOHAMMEDI"

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    Seismic Image Segmentation based on Deep Learning for the Characterization of Hydrocarbon reservoirs in the oil & Gas Industry : Comparative study
    (ESTIN, 2024) Anis MOHAMMEDI
    Salt Domes are subsurface geological formations critical for hydrocarbon exploration and storage. They often serve as traps for hydrocarbons, significantly contributing to the defining characteristics of reservoirs in the oil and gas industry. Accurately identifying and delineating these structures is essential for efficient resource management. Traditional methods for detecting salt domes rely heavily on manual interpretation, which is timeconsuming and prone to errors. Recent advancements in Deep Learning, particularly in Semantic Segmentation, offer promising solutions to automate and enhance this process. This study explores the application of state-of-the-art Deep Learning models, including Convolutional Neural Networks (CNNs), U-Net architectures, and Transformer-based approaches, for the Semantic Segmentation of Salt Domes in Seismic Images. Generative AI techniques are also examined for data augmentation and enhancing model robustness. The integration of these advanced models aims to improve the precision and reliability of Salt Dome identification, potentially transforming the field of geophysical exploration. Keywords— Salt Domes, Semantic Segmentation, Deep Learning, Generative AI, CNN, UNet, Transformers, Seismic Imaging, Hydrocarbon Exploration
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    Seismic image segmentation based on Deep Learning for the Characterization of Hydrocarbon Reservoirs in the Oil & Gas Industry: Comparative study
    (Tassadit, 2025-01-21) Anis MOHAMMEDI
    Accurate 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