ARTIFICIAL INTELLIGENCE APPLICATIONS FOR PORE PRESSURE AND FRACTURE PRESSURE PREDICTION FROM SEISMIC ATTRIBUTES ANALYSIS AND WELL LOGS DATA
Mohamed Atta Farahat Mohamed;
Abstract
Summary:
This study aims to investigate the pore and fracture pressure of sub-surface formations. Eaton’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.
This study aims to investigate the pore and fracture pressure of sub-surface formations. Eaton’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.
Other data
| Title | ARTIFICIAL INTELLIGENCE APPLICATIONS FOR PORE PRESSURE AND FRACTURE PRESSURE PREDICTION FROM SEISMIC ATTRIBUTES ANALYSIS AND WELL LOGS DATA | Authors | Mohamed Atta Farahat Mohamed | Issue Date | 2018 |
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