Abstract—According to importance and usage of researches of petroleum production, our major goal in this paperis to forecast the oil production by using Data Mining Technique for improving estimation of oil consumption base of History of data. The implement of auto regression, Data cleaning and concept of pre-processing to viewpoint of Time series analysis are traditional concept in intelligent format. We use data cleaning for integration data and auto regression to determine input of model and for pre-processing for upgrade operation of ANFIS. ANFIS algorithm is developed by different data pre-processing methods and the efficiency of ANFIS is examined against auto regression (AR) in Canada. For this purpose, mean absolute percentage error (MAPE) is used to show the efficiency of ANFIS. However, it is concluded that ANFIS provides better results than AR in Canada. This is unlike previous expectations that ANFIS always provides better estimation than conventional approaches.
Index Terms—Time series, neuro-fuzzy system, ANFIS, datamining, oil production forecasting.
Cite: Zahra Mahdavi , Maryam Khademi, "Prediction of Oil Production with: Data Mining, Neuro-Fuzzy and Linear Regression," International Journal of Computer Theory and Engineering vol. 4, no. 3, pp. 446-447, 2012.