![](/images/graphics-bg.png)
Synthesis of missing open hole well log data through artificial neural networks
Other Title(s)
تصنيع المجسات المفقودة للآبار المفتوحة باستخدام تقنية الشبكات العصبية الصناعية
Author
al-Kanani, Aminah Mal Allah Hanzal
Source
Issue
Vol. 9, Issue 2 (31 Dec. 2017), pp.56-63, 8 p.
Publisher
University of Kufa Faculty of Science Department of Physics
Publication Date
2017-12-31
Country of Publication
Iraq
No. of Pages
8
Main Subjects
Earth Sciences, Water and Environment
Abstract EN
A methodology is presented for deducing missing intervals of well logs data through applying artificial neural networks (ANNs) models.
Three ANNs were performed for synthesizing sonic, neutron, and density logs.
An example from Mishrif Formation of Nasyria oil field in southern Iraq was used to reveal the capability of ANNs model to synthesis missing intervals for these logs.
Basically, ANNs models developed in this study were based on commonly multilayer perceptron and trained with backpropagation algorithm.
Two statistical errors, namely, root mean squared error and correlation of determination were employed to assess the accuracy of the ANN models.
Results indicated the capability of ANNs model to recreation of missing well interval with high accuracy.
American Psychological Association (APA)
al-Kanani, Aminah Mal Allah Hanzal. 2017. Synthesis of missing open hole well log data through artificial neural networks. Journal of Kufa-Physics،Vol. 9, no. 2, pp.56-63.
https://search.emarefa.net/detail/BIM-832697
Modern Language Association (MLA)
al-Kanani, Aminah Mal Allah Hanzal. Synthesis of missing open hole well log data through artificial neural networks. Journal of Kufa-Physics Vol. 9, no. 2 (2017), pp.56-63.
https://search.emarefa.net/detail/BIM-832697
American Medical Association (AMA)
al-Kanani, Aminah Mal Allah Hanzal. Synthesis of missing open hole well log data through artificial neural networks. Journal of Kufa-Physics. 2017. Vol. 9, no. 2, pp.56-63.
https://search.emarefa.net/detail/BIM-832697
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references : p.62-63
Record ID
BIM-832697