In complex geologic settings with a great degree of heterogeneity in reservoir properties, such as submarine channel complexes as in the Nile Delta province, we face the challenge of characterizing the reservoir based on availability of different seismic attributes. Amplitude variation with offset (AVO) analysis and prestack inversion techniques show impressive results in delineating the gas-bearing reservoirs, especially in clastic systems. However, a shortage of available wells and/or seismic data leads to a challenge in applying AVO and any prestack seismic inversion approaches. In addition, quantitative prediction of water saturation (Sw) from seismic is ambiguous because of its independent nonlinear relationship with conventional seismic attributes and inversion products. Water-saturation prediction away from the well is essential in order to characterize the reservoirs effectively. Therefore, probabilistic neural network (PNN) analysis has been implemented to predict Sw 3D volume using full-stack seismic data and Sw logs. In this case study, we applied the proposed neural network workflow over one of the late-Pliocene gas-sandstone reservoirs, Sequoia Field, in the West Delta Deep Marine (WDDM) concession, offshore Nile Delta, Egypt. The resulting volume then was tested using two blind wells that haven't been used in the analysis. The predicted Sw volume contains fine details that were used with variance and spectral-decomposed volumes to understand the reservoir's internal architecture in terms of sand body geometries and connectivity. The resulting volumes were used to better define the reservoir and optimize a new development well location.

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