Prestack seismic inversion versus neural-network analysis: A case study in the Scarab field offshore Nile Delta, Egypt

Abstract In clastic depositional systems such as those encountered in the Nile Delta Basin, simultaneous prestack seismic-amplitude inversion is an effective method for detecting and appraising gas-bearing sandstone reservoirs. However, the method has limitations concerning the requirement of a reliable set of wavelets, suitable wireline logs, and a sufficiently dense initial model. The neural-network analysis method is an alternative technique which sometimes can provide similar or better results and does not require significant volumes of data. Simultaneous prestack inversion was applied over the Scarab field, West Delta Deep Marine concession, offshore Egypt. The field comprises submarine channel-based gas reservoirs that extend laterally over 20 km2. Six wells were analyzed in a rock-physics study prior to performing inversion. Three angle gathers (near: 0–15°; mid: 15–30°; far: 30–45°) were inverted for P-wave impedance (ZP), S-wave impedance (ZS), P-wave velocity (VP), S-wave velocity (VS), VP/VS, and density (ρ) using the prestack inversion method. Neural-network analysis was performed using full-stack seismic data along with well logs in the training stage, followed by cross-validation of results and rendering of VP, VS, VP/VS, and density volumes. The VP/VS volumes produced from the two methods were used to infer water saturation (Sw). Direct comparisons were made between neural-network and prestack inversion results at a blind-well location to assess the relative quality of each method. Results suggest that application of the proposed neural-network method leads to reliable inferences. Hence, using the neural-network method alone or along with the prestack inversion method has a positive impact on reserves growth and increased production.