AbstractDecline curve analysis (DCA), material balance (MB) and numerical simulation are used to predict reservoir performance. However, these methods have peculiar strengths and limitations. While some are based on analytical observations and theories others are purely stochastic. The objective of this study was to investigate the applicability of Time Series analysis to predict oil cumulative production as a measure of reservoir performance. The Box Jenkins method of Time Series analysis utilizing the Autoregressive Integrated Moving Average (ARIMA) models was used to predict and analyse oil cumulative production of wells and reservoirs. The process involves a robust transformation of production datasets into time series. A best fitting model was chosen using maximum absolute deviations as a measure of fit. Predictions of the ARIMA model were validated with historical data and its performance was compared with Decline Curve analysis (DCA).The coefficient of each model was optimized using the simplex optimization technique.The results show that ARIMA (1,1,0) model gives the best match with actual cumulative oil production data. The model had the least maximum deviation of 4.24% compared to 178.95%, 182.32% and 24.45% for ARIMA (1,0,0), ARIMA (2,0,0) AND ARIMA (1,2,0) respectively after predicting for 900 days. Analysis of results after 1097 days of production also shows that the ARIMA (1,1,0) predicts better than the DCA method. The maximum deviation for both ARIMA and DCA were 1.81% and 12.79% respectively. The best model coefficients fall between 1.99 and 1.9999999. A model coefficient too high or too low will definitely lead to erroneous predictions. This study has shown that ARIMA (1,1,0) can be used to model and predict reservoir/well cumulative oil production accurately for short to mid-long term periods. Furthermore, the model performs better than the DCA for medium to mid-long term predictions. The accuracy of the model can be improved as more data become available for history matching to further refine the model coefficients. A model coefficient of 1.995 is recommended when few data are available.
