Mendeley
TY  _  JOUR
ID  -  13980216179131
TI  -   Compilation of artificial neural networks and the thinned Fault likelihood auto-tracking algorithm, for identification, interpretation and extraction of faults 
JO  -  Iranian Journal of  Petroleum Geology
JA  -  ES
LA  -  fa
SN  -  2251-8738
AU  -  Ghazanfari Alireza
AU  -  Mohammadrezaei Hoseyn
AU  -  Ansari Hamidreza
AD  -  دانشگاه صنعتی امیرکبیر
AD  -  شرکت ملی نفت ایران
AD  -  شرکت نفت کیش
Y1  -  2019
PY  -  2019
VL  -  15
IS  -  
SP  -  63
EP  -  81
KW  -  Fault Interpretation
 Fault Auto-tracking
 Fault Likelihood
Artificial Neural Networks
KW  -  
 Seismic Attributes.


DO  -  
N2  -  Fault identification and investigating their evolution is of special importance in the exploration and development of hydrocarbon resources. Success in exploration and development of hydrocarbon fields, need to recognition of petroleum systems and in this regard one of the most important topics is identifying faults and their extension condition as a main fluid migration path, specially in deeper zones. Faults and fractures have crucial role in making high permeable and porous segments and cut reservoir and cap rock in the fluid migration path. In addition, for maximizing the production of hydrocarbon from reservoirs and also for reducing the risk of drilling, it is necessary to gain information about geometry and nature of faults of reservoirs. In this paper, the purpose is investigating the performance of combination of neural networks and Fault Likelihood auto-tracking algorithm for identification and interpretation of faults in seismic data. At first using the Dip-steering feature of software, the early filter for accurate identification of dip of structures in the data, have been designed and applied. Then with designing and applying the appropriate filters, the seismic data have been improved. After that proper seismic attributes for fault identification have been calculated from seismic data. With picking fault and non-fault points from data, a supervised neural network using the selected attributes was formed and after training the network, the appropriate output achieved. Then the output of neural network has been used as a input for Thinned Fault Likelihood auto-tracking algorithm. The output of this part contains a volume of tracked faults. Finally using sub-tools of TFL and optimal setting of parameters, 3D fault planes has been interpreted and extracted.
UR  - rimag.ir/en/Article/33886
L1  - rimag.ir/en/Article/Download/33886
ER  -  
