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Hydrocarbon detection using seismic data involves computationally intensive stages and domain expertise. The large and complex nature of seismic data presents challenges in data transfer, memory usage, and processing time, particularly with pre-stack data. This study introduces an optimized I/O approach to leverage Shell’s Delve deep-learning algorithm for rapid hydrocarbon characterization. Overcoming previous barriers, this approach enables efficient resource exploration and decision-making in the energy sector. The method details the methodology, theory, and conclusions, highlighting the potential of deep learning in analyzing pre-stack seismic data.