LITHOLOGY CHARACTERIZATION AND GAS VOLUME PREDICTION USING NEURAL NETWORK IN WEST DELTA DEEP MARINE, NILE DELTA, EGYPT

There are many approaches and concepts for the exploration and development of the hydrocarbon reservoirs. In this study, the aim is predicting the gas volume which is a multiplication of the effective porosity withhydrocarbon saturation. Predicting the gas volume away from the wells is a challenging task because of the nonuniquenessin its relationship with the conventional seismic attributes. A conjunction of a set of seismic attributesobtained from multi-linear regression technique with the non-linearity of artificial neural networks techniques can beutilized to develop effective workflows to explore the reservoirs and evaluate hydrocarbon presence. Insufficient wellsin the studied area led us to develop lithology classification workflow to increase the reliability of predicting gasvolume probability cube over the studied area. Within the studied area, which covers around 660 square km, reservoirsare mainly Pliocene slope channel system and consist of a succession of sandstones and mudstones organized into acomposite upward finning profile. The matching in the presence of gas volume in the sand classes comes up with apossibility of prospect evaluation at each location inside the 3D seismic coverage. Results suggest that the applicationof the proposed neural network method leads to reliable inferences and has a positive impact on the exploration ordevelopment over the area of study