Integration of data-driven models for dynamic prediction of the SAGD production performance with field data

Steam-assisted gravity drainage (SAGD) is a successful thermal recovery process and has been widely applied to oil sands production. The prediction of oil production of the SAGD process plays a significant role in decision -making, where numerical simulation is one of the tools which support engineers. However, the traditional nu-merical simulation process for the SAGD production prediction, such as history matching, sensitivity analysis, and predictive runs, is a time-consuming process that is always associated with heavy computational costs. Engineers require reliable alternative modeling tools for the management of SAGD production. In this study, a data-driven model-based workflow is formulated and tested for the prediction of the SAGD production perfor-mance. After a series of data processing and integration of real field data, the machine learning algorithms are successfully utilized to predict future production performance based on past production information and oper-ational conditions. A comparison of different machine learning algorithms shows that a Gated Recurrent Unit (GRU) based model has the best predictive ability.