Seismic Image Segmentation Based On Deep Learning For The Characterization Of Hydrocarbon Reservoirs In The Oil & Gas Industry : Comparative Study

Résumé: 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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