Data-Driven Analytics for Production Forecasting
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In this work, comprehensive data set from polymer flooding and SAGD experimental and field implementations are employed as starting points to build blocks and foundation of analysis, workflow, modeling and decision making in data-driven analytics. Then data-mining and machine learning are used to obtain the models for production forecasting. Various pertinent operational parameters are employed. Predictive modeling (Data-Driven Analytics) for performance forecasting of various recovery processes is implemented. From this work, we can see great potential of data-driven analytics to be integrated into existing reservoir management and decision-making routines.