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Faults and Fractures Detection Using a Combination of Seismic Attributes by the MLP Artificial Neural Network in an Iranian Oilfield
  • Reza Mohammadi,
  • Mohammadreza Bakhtiari
Reza Mohammadi
Amirkabir University of Technology (Tehran Polytechnic)-National Iranian Oil Company (NIOC)/National Iranian Drilling Company (NIDC)

Corresponding Author:rmohammadi@aut.ac.ir

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Mohammadreza Bakhtiari
Amirkabir University of Technology- NIOC (EXP)
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Faults and fractures play a significant role in drilling operations, trapping hydrocarbon, and reservoir development in oilfields; exploring faults quickly and accurately can help to reach the target more manageable. In this approach, to improve faults and fractures detection, applicable seismic attributes have been combined using a Multilayer Perceptron (MLP) neural network and applied to a 3-D seismic cube of the Changuleh oil field. First of all, high probabilistic faulted areas, as an interesting area, have been identified using a hand-picking method on a single seismic section. It is used as a pattern and one input set for the MLP neural network. Then, some single seismic attributes (e.g., Similarity, Coherency, Curvature, Instantaneous, etc.) were applied to the data. Next, the Multilayer Perceptron (MLP) neural network has been used to assess and determine the most contributed attributes. The less contributed ones are eliminated and the best seismic attributes, as another input set, combined using the MLP. Finally, the outputs of the MLP network will be two cubes named ‘faulted cube’ and ‘non-faulted cube’. Differences between faulted zones and non-faulted zones on each cube were conspicuous, and there was no need to be interpreted manually. By comparing initial seismic sections and the MLP network’s outputs, it is easy to see where the faulted and fractured zones are.