Improve the detection of buried channel, using Artificial Neural Networks and seismic attributes
Subject Areas : Geoscience Fields in relation with Petroleum Geology
Alireza
Ghazanfari
1
Abdolrahim
Javaherian
2
Mojtaba
Seddigh Arabani
3
Keywords: Channel Detection Seismic Attributes Artificial Neural Network Meta Attributes,
Abstract :
Channels are one of the most important stratigraphic and morphological events. If channels place in a suitable position such as enclosed in impermeable place can make suitable oil and gas reservoir; So identifying channels are crucial. Different tools such as filters, seismic attributes, artificial neural networks, and meta-attributes have played an important role in this regard. In this paper dip-steering cube, dip-steer median filter, dip-steer diffusion filter, and fault enhancement filter, have been used. Then, various seismic attributes such as similarity, texture, spectral decomposition, energy and polar dip have been defined and studied. Therefore, work on F3 real seismic data of Dutch part of the North sea for detecting channels has been started by detecting suitable attributes. For identifying the channel in data, it has been used from compilation and combination of seismic attributes using supervised ANN (multi-layer perceptron), and development of mata-attributes, then recombine meta-attributes created along the channel, and using different interpretation point, for eliminating the impact of facies and lithology changes along the channel. Among the advantages and the reasons for using this kind of neural network (supervised), which increases the effect of the neural network and improves the result, is the ability to train the network by specifying the channel and non-channel points used in this paper. Finally, using the above methods, the identification of the channel examined in the above seismic data has been improved, and the channel has been properly detected and extracted throughout its entire length.
غضنفری بروجنی، ع.، 1395، شناسایی کانالهای مدفون با استفاده از تلفیق نشانگرهای لرزهای توسط شبکههای عصبی مصنوعی، پایاننامه کارشناسی ارشد مهندسی نفت- اکتشاف، دانشکده مهندسی نفت، دانشگاه صنعتی امیرکبیر.
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