High-resolution water-saturation prediction using geostatistical inversion and neural network methods

Abstract

One of the main challenges that we face is the accurate prediction of pore-fluid properties with the highest possible resolution. The seismic resolution is the most limiting factor, especially in our case, in which the main reservoirs are deepwater turbidite channels and their thin beds typically fall below the seismic tuning thickness. Therefore, we designed a new workflow that combines the geostatistical inversion and the neural network analysis with the aim of predicting a 3D high-resolution water saturation (sampled every 1 ms), overcoming the limitation of seismic detectability and providing better reservoir characterization. The power of the geostatistical inversion is that it provides multiple model realizations, and each realization honors the well data (statistical information and logs) and the seismic data. These realizations are more reliable and high-resolution versions of the elastic parameters. On the other hand, the main advantage of the neural network is that it establishes a stable nonlinear link between the input seismic and inversion results and the target water saturation. The available data set for this study includes three angle stacks and seven wells from Scarab field, offshore Nile Delta. The resulted high-resolution saturation volume was tested using blind-well analysis and revisit post the drilling of a new well later on. It gave spectacular results in both cases. The normalized correlations between the predicted saturation volume and the real saturation logs are 0.87 and 0.89, respectively. The results prove the validity of the workflow to accurately predict water saturation with a higher resolution than ever before.