Comparison of processing speed of NRS-ANN hybrid and ANN models for oil production rate estimation of reservoir under waterflooding

This study compared the predictive performance and processing speed of an artificial neural network (ANN) and a hybrid of a numerical reservoir simulation (NRS) and artificial neural network (NRS-ANN) models in estimating the oil production rate of the ZH86 reservoir block under waterflood recovery. The historical input variables: reservoir pressure, reservoir pore volume containing hydrocarbons, reservoir pore volume containing water and reservoir water injection rate used as inputs for ANN models. To create the NRS-ANN hybrid models, 314 data sets extracted from the NRS model, which included reservoir pressure, reservoir pore volume containing hydrocarbons, reservoir pore volume containing water and reservoir water injection rate were used. The output of the models was the historical oil production rate (HOPR in m 3 per day) recorded from the ZH86 reservoir block. Models were developed using MATLAB R2021a and trained with 25 models in three replicate conditions (2, 4 and 6), each at 1,000 epochs. A comparative analysis indicated that, for all 25 models, the ANN outperformed the NRS-ANN in terms of processing speed and prediction performance. ANN models achieved an average of R 2 and MAE of 0.8433 and 8.0964 m 3 /day values, respectively, while NRS-ANN hybrid models achieved an average of R 2 and MAE of 0.7828 and 8.2484 m 3 /day values, respectively. In addition, ANN models achieved a processing speed of 49 epochs/sec, 32 epochs/sec, and 24 epochs/sec after 2, 4, and 6 replicates, respectively. Whereas the NRS-ANN hybrid models achieved lower average processing speeds of 45 epochs/sec, 23 epochs/sec and 20 epochs/sec. In addition, the ANN optimal model outperforms the NRS-ANN model in terms of both processing speed and accuracy. The ANN optimal model achieved a speed of 336.44 epochs/sec, compared to the NRS-ANN hybrid optimal model, which achieved a speed of 52.16 epochs/sec. The ANN optimal model achieved lower RMSE and MAE values of 7.9291 m 3 /day and 5.3855 m 3 /day in the validation dataset compared with the hybrid ANS optimal model, which achieved 13.6821 m 3 /day and 9.2047 m 3 /day, respectively. The study also showed that the ANN optimal model consistently achieved higher R 2 values: 0.9472, 0.9284 and 0.9316 in the training, test and validation data sets. Whereas the NRS-ANN hybrid optimal yielded lower R 2 values of 0.8030, 0.8622 and 0.7776 for the training, testing and validation datasets. The study showed that ANN models are a more effective and reliable tool, as they balance both processing speed and accuracy in estimating the oil production rate of the ZH86 reservoir block under the waterflooding recovery method. • A hybrid NRS-ANN and ANN models were employed to compare processing speed attained during prediction of the oil production rated of reservoir under waterflooding recovery techniques. • The application of the ANN model outperformed the hybrid NRS-ANN model in processing speed as well as accuracy in oil production forecasting performance. • The ANN model is more accurate than the NRS-ANN hybrid model in predicting oil production rate.