Detection of stratigraphic heterogeneities at sub-seismic scale: Lessons from wave-dominated depositional environments

Accurate subsurface characterization is critical for emerging energy-transition projects, yet conventional seismic data often fail to resolve metre-scale heterogeneities that strongly influence reservoir behaviour. This thesis develops an integrated workflow that combines stratigraphic forward modelling with seismic forward modelling to improve the detection of sub-seismic stratigraphic features in wave-dominated shoreface systems. Outcrop analysis shows significant variability in petrophysical and acoustic properties, revealing the limitations of lithology-based seismic approaches. By converting grain-size distributions from stratigraphic simulations into acoustic properties, the workflow produces synthetic seismic data that better represent fine-scale stratigraphy. Angle-dependent seismic analysis shows potential for identifying subtle acoustic variations, though current modelling techniques require refinement. The results demonstrate that linking geological, petrophysical, and geophysical data enhances subsurface resolution and point toward future developments involving more complex models, in-well seismic methods, and machine learning.