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.

从地震振幅数据预测储层特征是一个主要挑战。特别是在尼罗河三角洲盆地，那里的地下地质情况复杂且储集层高度非均质。现代地震储层表征方法涵盖属性分析，确定性和随机反演方法，带偏移量的振幅变化（AVO）解释以及烟囱旋转。这些方法论在探测气砂储层和定量储层性质方面都取得了良好的成果。但是，当叠前地震数据不可用时，大多数与AVO相关的反演方法将无法实现。此外，地震振幅数据与大多数储层属性（例如烃饱和度，嵌入了许多假设，结果令人怀疑。人工神经网络（ANN）算法预测储层特征是一种新兴的趋势。与其他地震储层表征方法相比，ANN算法的主要优势在于能够在岩石物理测井和地震数据之间建立非线性关系。因此，它可以用于以合理的精度预测3D空间中的各种储层属性。我们在离岸尼罗河三角洲的红杉气田实施了ANN方法，以根据地震振幅数据预测储层的岩石物性。选择的算法是概率神经网络（PNN）。将一口井与分析分开，以后用作盲目质量控制来测试结果。人工神经网络（ANN）算法预测储层特征是一种新兴的趋势。与其他地震储层表征方法相比，ANN算法的主要优势在于能够在岩石物理测井和地震数据之间建立非线性关系。因此，它可以用于以合理的精度预测3D空间中的各种储层属性。我们在离岸尼罗河三角洲的红杉气田实施了ANN方法，以根据地震振幅数据预测储层的岩石物性。选择的算法是概率神经网络（PNN）。将一口井与分析分开，以后用作盲目质量控制来测试结果。人工神经网络（ANN）算法预测储层特征是一种新兴的趋势。与其他地震储层表征方法相比，ANN算法的主要优势在于能够在岩石物理测井和地震数据之间建立非线性关系。因此，它可以用于以合理的精度预测3D空间中的各种储层属性。我们在离岸尼罗河三角洲的红杉气田实施了ANN方法，以根据地震振幅数据预测储层的岩石物性。选择的算法是概率神经网络（PNN）。将一口井与分析分开，以后用作盲目质量控制来测试结果。