Egypt prioritises Miocene hydrocarbon exploitation in the eastern Mediterranean West Offshore Nile Delta (WOND) licence. This study integrates Amplitude Versus Offset (AVO) analysis with machine learning to improve prospect identification and reduce exploration risk. Deterministic geophysical interpretation and data-driven modelling using Gradient Boosted Decision Trees (GBDT) and neural networks identify seismic abnormalities and predict subsurface facies. AVO analysis interprets amplitude and polarity shifts to find direct hydrocarbon indicators (DHIs) and classify anomalies into AVO Classes II, IIp, and III. Additionally, machine learning can discover subtle gas-charged sands by collecting complicated, non-linear connections between seismic properties and well log data. Rock physics study, including 85% gas substitution scenarios, proves the workflow’s robustness. The findings identify gas sands with AVO Class II responses and good porosity and low acoustic impedance contrasts. These traits appear as soft amplitude anomalies in near-angle stacks and increasing amplitude brightness in far-angle stacks. Gas sand probability volumes using machine learning match seismic amplitude readings, boosting interpretive confidence. Application to WOND Miocene and Pliocene case studies confirms the workflow’s ability to define prospective reservoirs and reduce uncertainty. This combined AVO and machine learning system strengthens subsurface characterisation in geologically complicated environments, aiding offshore basin exploration and development decisions.
Fahmy et al. (Wed,) studied this question.