Integrated cluster analysis and artificial neural network modeling for steam-assisted gravity drainage performance prediction in heterogeneous reservoirs
Data-driven modeling provides an attractive alternative to predict SAGD recovery. The modeling approach is applied successfully for heterogeneous reservoirs. Arps parameters are proposed to parameterize production time-series data. A normalized shale indicator is used as a pertinent input attribute. Accuracy of the prediction is greatly enhanced when cluster analyses are performed.
cluster analysis artificial neural networks data-driven thermal recovery heavy oil principal components