A reservoir identification method based on rough set and support vector machine. (1st January 2014)

• Record Type: Journal Article
• Authors: Han, Sun; Huan, Zhang; Haixiang, Guo; Jinhua, Cheng
• Is Part Of: International journal of computer applications technology. Volume 50:Number 3/4(2014)
• Page Start: 196
• Page End: 199
• Publication Date: 2014-01-01
• Subjects: rough set -- attributes reduction -- SVMs -- support vector machines -- reservoir identification.; Technology -- Data processing -- Periodicals; 620.00285
• DOI: 10.1504/IJCAT.2014.066726
• Languages: English
• ISSNs: 0952-8091

Abstract

There are many logging parameters that affect identification of oil zones in the course of discrimination. A mass of redundant information exists. The identified precision and speed are impacted. Conventional methods cannot identify oil zones effectively. The method is put forward that information is optimised by rough set combining with support vector machine (SVM), and applied to identify the zones. On the basis of analysing on rough set theory and SVM method, the SVM identifying process of oil zones based on rough set is put forward. The practical research on three wells in an oilfield having testing data has been done. The results with the artificial neural network (ANN) method were compared, and show that the method is effective and feasible, the identified precision rate is 96% over ANN.