A Hybrid of Functional Networks and Support Vector Machine Models for the Prediction of Petroleum Reservoir Properties

Fatai Adesina, Anifowose and Jane, Labadin and Abdulazeez, Abdulraheem (2011) A Hybrid of Functional Networks and Support Vector Machine Models for the Prediction of Petroleum Reservoir Properties. In: 11th International Conference on Hybrid Intelligent Systems (HIS), 2011, 5-8 Dec. 2011, Melacca.

[img]
Preview
PDF
A Hybrid of Functional Networks and Support Vector Machine models for the prediction of petroleum reservoir properties (abstract).pdf

Download (175kB) | Preview

Abstract

This paper presents an innovative hybrid of Functional Networks and Support Vector Machines (FN-SVM) as an improvement over an existing Functional Networks and Type-2 Fuzzy Logic (FN-T2FL) hybrid model. The former is more promising as it combines two existing techniques that are very close in performance and well known for their computational stability and fast processing. This proposed FNSVM hybrid model benefits from the excellent performance of the least-square-based model-selection algorithm of Functional Networks and the non-linear high-dimensional feature transformation capability that is based on structural risk minimization and Tikhonov regularization properties of SVM. Training and testing the SVM component of the hybrid model with the best and dimensionally-reduced variables from the input data resulted in better performance with higher correlation coefficients, lower root mean square errors and further less execution time than the standard SVM model. A comparison of FN-SVM with the existing FN-T2FL, using the same data and operating environment, showed that the FNSVM is more accurate and consumes less time.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Computational modeling, Permeability, Petroleum, Predictive models, Reservoirs, Support vector machines, research, Universiti Malaysia Sarawak, unimas, university, universiti, Borneo, Malaysia, Sarawak, Kuching, Samarahan, ipta, education
Subjects: T Technology > T Technology (General)
Divisions: Academic Faculties, Institutes and Centres > Faculty of Computer Science and Information Technology
Depositing User: Karen Kornalius
Date Deposited: 05 Aug 2015 02:23
Last Modified: 05 Aug 2015 02:23
URI: http://ir.unimas.my/id/eprint/8480

Actions (For repository members only: login required)

View Item View Item