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Neural network use ability in Well Log Data Analysis

Publish Year: 1390
Type: Conference paper
Language: English
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ICHEC07_649

Index date: 14 April 2015

Neural network use ability in Well Log Data Analysis abstract

Well log data analysis plays an important task in petroleum exploration. It is used to identify the potential for oil production at a given source and so forms the basis for the estimation of financial returns and economic benefits. In recent years, many computational intelligence techniques such as backpropagation neural networks (BPNN) and fuzzy systems have been applied to perform the task. Support vector machines (SVMs) are new techniques and very few reports have been published in this application area. This paper presents the study and comparison of BPNN model with a SVM model on a set of practical well log data. Future directions of exploring of the use of SVM for improved results will also be discussed.

Neural network use ability in Well Log Data Analysis Keywords:

well log data analysis , reservoir characterization , backpropagation neural networks (BPNN) , support vector machine (SVM)

Neural network use ability in Well Log Data Analysis authors

Mohammad Ali Mohammadi

Department of petroleum engineering ,Omidiyeh Branch ,Islamic Azad University , Omidiyeh ,Iran

Ali Mohammadi

Department of petroleum engineering ,Omidiyeh Branch ,Islamic Azad University , Omidiyeh ,Iran

Jamshid Moghadasi

Department of petroleum engineering ,Omidiyeh Branch ,Islamic Azad University , Omidiyeh ,Iran

Mohammad Javad Mohammadi

Department of petroleum engineering ,Omidiyeh Branch ,Islamic Azad University , Omidiyeh ,Iran