Comparison of different types of fitness functions to choose the appropriate attributes for porosity prediction
Other Title(s)
مقارنة ببن عدة أنواع من دوال الهدف لاختيار الخواص المناسبة للتنبؤ بالمسامية
Joint Authors
Tumah, Hadil Muhammad
Salih, Muna Hadi
Source
al-Nahrain Journal for Engineering Sciences
Issue
Vol. 20, Issue 3 (30 Sep. 2017), pp.737-743, 7 p.
Publisher
Nahrain University College of Engineering
Publication Date
2017-09-30
Country of Publication
Iraq
No. of Pages
7
Main Subjects
Abstract EN
Porosity is one of the most important reservoir characteristics because it indicates to fluid collection.
Several techniques used to get good porosity prediction, so, in this study we employed seismic attributes and well log data in a genetic algorithm to get the best porosity prediction.
The study attempt to enhance the performance of genetic algorithm for attribute selection and therefore porosity prediction by applying genetic algorithm on different types of fitness functions like average mean square error fitness, average correlation coefficients fitness and performance index fitness.
Also, used two methods to represent attributes in genetic algorithm.
Different witnesses applied to choose the appropriate fitness function that gives high porosity prediction.
American Psychological Association (APA)
Salih, Muna Hadi& Tumah, Hadil Muhammad. 2017. Comparison of different types of fitness functions to choose the appropriate attributes for porosity prediction. al-Nahrain Journal for Engineering Sciences،Vol. 20, no. 3, pp.737-743.
https://search.emarefa.net/detail/BIM-849162
Modern Language Association (MLA)
Salih, Muna Hadi& Tumah, Hadil Muhammad. Comparison of different types of fitness functions to choose the appropriate attributes for porosity prediction. al-Nahrain Journal for Engineering Sciences Vol. 20, no. 3 (2017), pp.737-743.
https://search.emarefa.net/detail/BIM-849162
American Medical Association (AMA)
Salih, Muna Hadi& Tumah, Hadil Muhammad. Comparison of different types of fitness functions to choose the appropriate attributes for porosity prediction. al-Nahrain Journal for Engineering Sciences. 2017. Vol. 20, no. 3, pp.737-743.
https://search.emarefa.net/detail/BIM-849162
Data Type
Journal Articles
Language
English
Notes
Record ID
BIM-849162