Gas channels and chimneys prediction using artificial neural networks and multi-seismic attributes, offshore West Nile Delta, Egypt

Machine learning techniques combined with multi-seismic attributes and well logs datasets have been successfully used in reducing the risk of drilling operations and petroleum exploration by providing precise petrophysical and seismic information extracted from the hydrocarbon reservoir rocks. For this purpose, Artificial Neural Networks (ANNs) work as a multi-channel processing system with a high degree of interconnection to classify various faces and predict the reservoir properties through the seismic profile by involving multi-seismic attributes and optionally well logs to the inputs. The main aim of this study is to use both supervised and unsupervised neural networks for the first time in the West Delta Deep Marine (WDDM) concession to identify the spatial dimensions of the gas-bearing channels and the detection of gas chimneys across the seismic profiles. We use back-error propagation algorithms of the Multilayer Perceptron (MLP) and self-organizing Unsupervised Vector Quantizer (UVQ) as supervised and unsupervised neural network methods, respectively, to detect the gas zones and channels, and to classify the gas chimneys and non-gas chimneys zones, as well as classification of the seismic reflections and lithologies. The output acquires a detailed analysis of the distribution pattern of gas channels and accurate information to image the gas chimneys. In the current study, the approach adopted is beneficial to image the gas chimneys and channels in different basins in any region of the world with similar geological settings.