Integrated cluster analysis and artificial neural network modeling for steam-assisted gravity drainage performance prediction in heterogeneous reservoirs

Highlights：

• 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.