الفهرس 
Artificial intelligence applications for pore pressure and fracture pressure prediction from seismic attributes analysis and well logs data
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Abstract
This study aims to investigate the pore and fracture pressure of sub-surface formations. Eaton{u2019}s method is applied to predict pore and fracture pressure of wells. Inversion process with numerous algorithms are applied to seismic area of the field. Prediction methods are applied to investigate best attributes such as single, multiple seismic attribute analysis and neural network. Well logs and seismic attributes obtained from inversion process and seismic data are used to train ANN. ANN is validated using blind wells which are not included in training process. The correlations of ANN training and validation are good so ANN is applied for prediction of pore and fracture pressure for 3D seismic area of field