In this paper, we tackle an old problem – production forecast - using techniques that are relatively new to the reservoir engineer toolbox. The problem at hand consists of forecasting oil production in a mature onshore field simultaneously driven by water and steam injection. However, instead of turning to traditional methods, we deploy machine-learning algorithms which will feed on a plethora of historical data to extract hidden patterns and underlying relationships with a view to forecasting oil rate. No geological model and/or numerical reservoir simulators will be needed, only 3 sets of time-series: injection history, production history and number of producers. Two Machine-Learning algorithms are used: linear-regression and recurrent neural networks.
Number of Pages
CAO, Q., BANERJEE, R., GUPTA, S., LI, J., ZHOU, W., & JEYACHANDRA, B. (2016, JUNE 1). DATA DRIVEN PRODUCTION FORECASTING USING MACHINE LEARNING. SOCIETY OF PETROLEUM ENGINEERS. DOI:10.2118/180984-MS.
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