@article{10.11648/j.pse.20240802.12,
  author = {Uduak Koffi Umana and Anietie Ndarake Okon and Okorie Ekwe Agwu},
  title = {Simplified Neural Network-Based Models for Oil Flow Rate Prediction
},
  journal = {Petroleum Science and Engineering},
  volume = {8},
  number = {2},
  pages = {70-99},
  doi = {10.11648/j.pse.20240802.12},
  url = {https://doi.org/10.11648/j.pse.20240802.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.pse.20240802.12},
  abstract = {Available neural network-based models for predicting the oil flow rate (qo) in the Niger Delta are not simplified and are developed from limited data sources. The reproducibility of these models is not feasible as the models’ details are not published. This study developed simplified and reproducible three, five, and six-input variables neural-based models for estimating qo using 283 datasets from 21 wells across fields in the Niger Delta. The neural-based models were developed using maximum-minimum (max.-min.) normalized and clip-normalized datasets. The performances and the generalizability of the developed models with published datasets were determined using some statistical indices: coefficient of determination (R2), mean square error (MSE), root mean square error (RMSE), average relative error (ARE) and average absolute relative error (AARE). The results indicate that the 3-input-based neural models had overall R2, MSE, and RMSE values of 0.9689, 9.6185x10-4 and 0.0310, respectively, for the max.-min. normalizing method and R2 of 0.9663, MSE of 5.7986x10-3 and RMSE of 0.0762 for the clip scaling approach. The 5-input-based models resulted in R2 of 0.9865, MSE of 5.7790×10-4 and RMSE of 0.0240 for the max.-min. scaling method and R2 of 0.9720, MSE of 3.7243x10-3 and RMSE of 0.0610 for the clip scaling approach. Also, the 6-input-based models had R2 of 0.9809, MSE of 8.7520x10-4 and RMSE of 0.0296 for the max.-min. normalizing approach and R2 of 0.9791, MSE of 3.8859 x 10-3 and RMSE of 0.0623 for the clip scaling method. Furthermore, the generality performance of the simplified neural-based models resulted in R2, RMSE, ARE, and AAPRE of 0.9644, 205.78, 0.0248, and 0.1275, respectively, for the 3-input-based neural model and R2 of 0.9264, RMSE of 2089.93, ARE of 0.1656 and AARE of 0.2267 for the 6-input-based neural model. The neural-based models predicted qo were more comparable to the test datasets than some existing correlations, as the predicted qo result was the lowest error indices. Besides, the overall relative importance of the neural-based models’ input variables on qo prediction is S>GLR>Pwh>T/Tsc>γo>BS&W>γg. The simplified neural-based models performed better than some empirical correlations from the assessment indicators. Therefore, the models should apply as tools for oil flow rate prediction in the Niger Delta fields, as the necessary details to implement the models are made visible.
},
 year = {2024}
}

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TY  - JOUR
T1  - Simplified Neural Network-Based Models for Oil Flow Rate Prediction

AU  - Uduak Koffi Umana
AU  - Anietie Ndarake Okon
AU  - Okorie Ekwe Agwu
Y1  - 2024/09/23
PY  - 2024
N1  - https://doi.org/10.11648/j.pse.20240802.12
DO  - 10.11648/j.pse.20240802.12
T2  - Petroleum Science and Engineering
JF  - Petroleum Science and Engineering
JO  - Petroleum Science and Engineering
SP  - 70
EP  - 99
PB  - Science Publishing Group
SN  - 2640-4516
UR  - https://doi.org/10.11648/j.pse.20240802.12
AB  - Available neural network-based models for predicting the oil flow rate (qo) in the Niger Delta are not simplified and are developed from limited data sources. The reproducibility of these models is not feasible as the models’ details are not published. This study developed simplified and reproducible three, five, and six-input variables neural-based models for estimating qo using 283 datasets from 21 wells across fields in the Niger Delta. The neural-based models were developed using maximum-minimum (max.-min.) normalized and clip-normalized datasets. The performances and the generalizability of the developed models with published datasets were determined using some statistical indices: coefficient of determination (R2), mean square error (MSE), root mean square error (RMSE), average relative error (ARE) and average absolute relative error (AARE). The results indicate that the 3-input-based neural models had overall R2, MSE, and RMSE values of 0.9689, 9.6185x10-4 and 0.0310, respectively, for the max.-min. normalizing method and R2 of 0.9663, MSE of 5.7986x10-3 and RMSE of 0.0762 for the clip scaling approach. The 5-input-based models resulted in R2 of 0.9865, MSE of 5.7790×10-4 and RMSE of 0.0240 for the max.-min. scaling method and R2 of 0.9720, MSE of 3.7243x10-3 and RMSE of 0.0610 for the clip scaling approach. Also, the 6-input-based models had R2 of 0.9809, MSE of 8.7520x10-4 and RMSE of 0.0296 for the max.-min. normalizing approach and R2 of 0.9791, MSE of 3.8859 x 10-3 and RMSE of 0.0623 for the clip scaling method. Furthermore, the generality performance of the simplified neural-based models resulted in R2, RMSE, ARE, and AAPRE of 0.9644, 205.78, 0.0248, and 0.1275, respectively, for the 3-input-based neural model and R2 of 0.9264, RMSE of 2089.93, ARE of 0.1656 and AARE of 0.2267 for the 6-input-based neural model. The neural-based models predicted qo were more comparable to the test datasets than some existing correlations, as the predicted qo result was the lowest error indices. Besides, the overall relative importance of the neural-based models’ input variables on qo prediction is S>GLR>Pwh>T/Tsc>γo>BS&W>γg. The simplified neural-based models performed better than some empirical correlations from the assessment indicators. Therefore, the models should apply as tools for oil flow rate prediction in the Niger Delta fields, as the necessary details to implement the models are made visible.

VL  - 8
IS  - 2
ER  - 

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