Faults and fractures detection using a combination of seismic attributes by the MLP and UVQ artificial neural networks in an Iranian oilfield-Reference-Cited by-同舟云学术

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Faults and fractures detection using a combination of seismic attributes by the MLP and UVQ artificial neural networks in an Iranian oilfield

Published:2022-09-02 Issue: Volume: Page:1-29
ISSN:1091-6466
Container-title:Petroleum Science and Technology
language:en
Short-container-title:Petroleum Science and Technology

Author:

Mohammadi Reza¹²^ORCID,Bakhtiari Mohammad Reza¹³

Affiliation:

1. Department of Petroleum Engineering, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran

2. Department of Well Logging, National Iranian Drilling Company (NIDC), Ahvaz, Iran

3. Department of Geophysics, National Iranian Oil Company-Exploration Directorate (NIOC-EXP), Tehran, Iran

Publisher

Informa UK Limited

Subject

Geotechnical Engineering and Engineering Geology,Energy Engineering and Power Technology,Fuel Technology,General Chemical Engineering,General Chemistry

Link

https://www.tandfonline.com/doi/pdf/10.1080/10916466.2022.2117380

Reference56 articles.

1. Integrated Seismic Interpretation, 2D Modeling along with Petrophysical and Seismic Attribute Analysis to Decipher the Hydrocarbon Potential of Missakeswal Area, Pakistan

2. Lithofacies prediction in non-cored wells from the Sif Fatima oil field (Berkine basin, southern Algeria): A comparative study of multilayer perceptron neural network and cluster analysis-based approaches

3. Aminzadeh, F. 2003. Meta-attributes: A new concept for reservoir characterization and seismic anomaly detection.

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