| Evaluation of steam-assisted gravity drainage (SAGD) performance that involves detailed compositional simulations is usually deterministic, cumbersome, expensive (manpower and time consuming), and not quite suitable for practical decision making and forecasting, particularly when dealing with high-dimensional data space consisting of large number of operational and geological parameters. Data-driven modeling techniques, which entail comprehensive data analysis and implementation of machine learning methods for system forecast, provide an attractive alternative.;In this thesis, Artificial Neural Network (ANN) is employed as a data-driven modeling alternative to predict SAGD production in heterogeneous reservoirs. Numerical flow simulations are performed to construct a training data set consists of various attributes describing characteristics associated with reservoir heterogeneities and other relevant operating parameters. Finally, several case studies are studied to demonstrate the improvements in robustness and accuracy of the prediction when cluster analysis techniques are performed to identify internal data structures and groupings prior to ANN modeling. |
