Forecasting oil production by adaptive neuro fuzzy inference system

Author

Saberi, Morteza ; Azadeh, Ali ; Ghorbani, Sara

Author_Institution

Dept. of Ind. Eng., Azad Univ. of Tafresh, Tafresh

fYear

2008

fDate

June 30 2008-July 2 2008

Firstpage

1035

Lastpage

1043

Abstract

In this paper, the efficiency of neuro fuzzy network (ANFIS) is examined against auto regression (AR). Mean absolute percentage error (MAPE) is applied for this purpose. After applying different data preprocessing methods, the models are developed. A method for calculating ANFIS performance is also proposed. Due to various seasonal and monthly changes in oil production and difficulties in modeling it with conventional methods, we consider a case study in four countries for oil production estimation. Finally, analysis of variance (ANOVA) and Duncan Multiple Range Test (DMRT) is conducted for each country to evaluate the most efficient method.

Keywords

forecasting theory; fuzzy neural nets; inference mechanisms; petroleum industry; production engineering computing; statistical analysis; ANOVA; Duncan Multiple Range Test; adaptive neuro fuzzy inference system; analysis of variance; auto regression; data preprocessing methods; mean absolute percentage error; neuro fuzzy network; oil production forecasting; Adaptive systems; Analysis of variance; Biological neural networks; Fuzzy logic; Fuzzy neural networks; Fuzzy systems; Industrial engineering; Petroleum; Predictive models; Production systems; ANFIS; ANOVA; Oil Production Estimation; Time Series Analysis;

fLanguage

English

Publisher

ieee

Conference_Titel

Industrial Electronics, 2008. ISIE 2008. IEEE International Symposium on

Conference_Location

Cambridge

Print_ISBN

978-1-4244-1665-3

Electronic_ISBN

978-1-4244-1666-0

Type

conf

DOI

10.1109/ISIE.2008.4676919

Filename

4676919