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.

The use of artificial intelligence algorithms to solve geophysical problems is a recent development. Neural network analysis is one of these algorithms. It uses the information from multiple wells and seismic data to train a neural network to predict properties away from the well control. Neural network analysis can significantly improve the seismic inversion result when the outputs of the inversion are used as external attributes in addition to regular seismic attributes for training the network. We found that integration of prestack inversion and neural network analysis can improve the characterization of a late Pliocene gas sandstone reservoir. For inversion, the input angle stacks was conditioned to match the theoretical amplitude-variation-with-offset response. The inversion was performed using a deterministic wavelet set. Neural network analysis was then used to enhance the [Formula: see text], [Formula: see text], and density volumes from the inversion. The improvement was confirmed by comparisons with logs from a blind well.

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.

The Prediction of the reservoir characteristics from seismic amplitude data is a main challenge. Especially in the Nile Delta Basin, where the subsurface geology is complex and the reservoirs are highly heterogeneous. Modern seismic reservoir characterization methodologies are spanning around attributes analysis, deterministic and stochastic inversion methods, Amplitude Variation with Offset (AVO) interpretations, and stack rotations. These methodologies proved good outcomes in detecting the gas sand reservoirs and quantifying the reservoir properties. However, when the pre-stack seismic data is not available, most of the AVO-related inversion methods cannot be implemented. Moreover, there is no direct link between the seismic amplitude data and most of the reservoir properties, such as hydrocarbon saturation, many assumptions are imbedded and the results are questionable. Application of Artificial Neural Network (ANN) algorithms to predict the reservoir characteristics is a new emerging trend. The main advantage of the ANN algorithm over the other seismic reservoir characterization methodologies is the ability to build nonlinear relationships between the petrophysical logs and seismic data. Hence, it can be used to predict various reservoir properties in a 3D space with a reasonable amount of accuracy. We implemented the ANN method on the Sequoia gas field, Offshore Nile Delta, to predict the reservoir petrophysical properties from the seismic amplitude data. The chosen algorithm was the Probabilistic Neural Network (PNN). One well was kept apart from the analysis and used later as blind quality control to test the results.

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.