Compilation of Seismic Attributes and Artificial Neural Networks in Identifying Fault Systems in the Hormuz Strait Area

This study has focused on identifying fault systems in the Hormuz Strait area using compilation of seismic attributes and artificial neural networks. Faults and fractures play an important role in creating areas of high porosity and permeability. In addition, they cut off the cap and reservoir rocks along fluid migration pathways. Intense tectonic activities and salt tectonics have resulted in complex structures in the Strait of Hormuz area. Therefore, precise identification of faults and fracture zones and their extensions has special importance in increasing petroleum production traps. In order to identify the geometry and kinematics of faults in the Mishan and Aghajari Formations and in the units under the base-Guri unconformity in the Hormuz Strait area (eastern part of the Persian Gulf), we have used structural imaging and visualization techniques of seismic interpretation. The structural imaging of the fault zones was obtained by this technique based on the integration of input attributes in an artificial neural network system and creating new attributes. First, a set of advanced attributes were introduced as input for the artificial neural network system to train and compile the calculated attributes on fault and non-fault interpreted points. As a powerful exploration tool, finally, the fault cube was obtained to precisely identify fault systems and better detect faults and fractures in quantitative modeling of the area. As a result of integrated attributes, the high correlation between the faults within the fault cube provides more accurate and reliable tracking of fault extensions. Therefore, three types of fault systems were identified in study area, which are thought to be results of the extensional and compressional tectonics of the Oman Orogeny, vertical tectonic movements of the Zagros Orogeny, and syn-sedimentary salt movements.