Super Resolution Seismic Imaging through Deep Information Maximization: Optimizing Exploration and Development in the Chandon Field

This study introduces a novel generative deep learning-based methodology that enhances seismic resolution. This is crucial for optimizing field exploration and development as it enables more accurate geological interpretations. By integrating geological and geophysical knowledge with data-driven models, the methodology significantly improves subsurface imaging and inversion. Applied to the Chandon Field, offshore NW Australia, this approach has achieved substantial resolution enhancements, allowing for the detailed interpretation of previously unresolved stratigraphic and structural features. The improved seismic data and acoustic impedance models have enabled precise mapping of facies distribution and strati-structural plays, particularly within the Mungaroo and Brigadier Formations. The high-resolution seismic imaging has resulted in clearer visualization of structural and depositional features, facilitating confident fault interpretation and the identification of key geological structures, such as a strike-slip corridor. This work highlights the potential of deep learning combined with domain expertise to drive advancements in reservoir characterization and overall field management.