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Porosity estimation by ٣D seismic data using artificial neural networks

Publish Year: 1384
Type: Conference paper
Language: English
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GCI12_044

Index date: 1 January 2006

Porosity estimation by ٣D seismic data using artificial neural networks abstract

Reservoir properties such as porosity could be predicted, precisely, by integration of seismic data and inversion result. In this study, first by using ٣D seismic data, horizon interpretations and well logs, ٣D acoustic impedance model of reservoir was generated. Then some single attributes extracted from seismic data. After that linear step wise regression method was used to generate seismic multi-attributes. Later, the three layered artificial neural network, with three nodes in input layer, eight nodes in hidden layer and one in output designed. Finally, ٣D porosity model estimated applying neural network, seismic multi-attributes and well logs.

Porosity estimation by ٣D seismic data using artificial neural networks authors

Javad Jamali

National Iranian Oil Company

Maryam Sadri

Amir Kabir University of Technology