Table 5. Training and Testing Performances of Different Neural-Network Architectures for the Prediction of Oil Recovery and Cumulative Steam–Oil Ratio.

		training				testing
		oil rec. (%)		CSOR		oil rec. (%)		CSOR
no. of hidden layers	no. of neurons	R2	RMSE	R2	RMSE	R2	RMSE	R2	RMSE
1	10	0.99	26.0	0.97	7.1	0.98	14.9	0.90	3.2
	20	0.99	18.6	0.97	6.3	0.99	13.0	0.93	2.7
	30	0.99	27.1	0.96	8.1	0.99	13.6	0.88	3.5
	40	0.99	20.3	0.98	5.1	0.99	13.8	0.95	2.3
2	20–10	0.99	17.8	0.98	6.2	0.99	13.8	0.92	2.9
	30–20	1.00	16.5	0.97	6.7	0.99	13.9	0.91	3.1
	40–25	0.99	26.1	0.97	6.4	0.98	16.8	0.89	3.3
	50–30	1.00	15.5	0.99	4.6	0.99	11.1	0.93	2.7
3	15–10–5	1.00	15.4	0.99	4.4	0.99	10.5	0.94	2.4
	30–20–10	0.99	17.9	0.98	5.3	0.99	11.4	0.94	2.5
	40–30–20a	1.00	16.9	0.98	5.2	0.99	11.2	0.95	2.3
	50–35–20	1.00	14.9	0.99	4.0	0.99	9.9	0.93	2.6

^aSelected model.