Field forecasting and predictive production analysis has tremendous effects on assets planning and allocation and it cannot be over emphasized. For accurate actions to be taken, appropriate forecasts need to be made on each asset, hence the need to develop methods to aid the process. As a tilt from the conventional methodology of forecasting involving use of curve fitting techniques, and multi-level computational analysis, data driven approaches can be employed. This study presents the applications of data driven approaches to forecast production. Deep learning neural network algorithm and statistics- based data driven approach were considered. An LSTM model was developed and for the statistical algorithms, an ARIMA model, and a Holt Winters model was developed. The models were deployed, and the performance of the models were checked to determine more accurate approach for forecasting. Error analysis on the results form the models showed that the deep learning neural network model provided better results in comparison to the statistical models with an MAE of 0.0328. Based on the model performances, LSTM model can be considered for use in forecasting petroleum production overcoming effects of seasonal changes, and production anomalies in the life of the reservoir.

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