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Oil Content Prediction of Oilfield Reinjection Water Based on Residual Neural Networks
Abstract
Predicting the oil content of reinjection water is a crucial challenge in the advancement of digital oilfield technologies. To effectively address this challenge, this study investigates rapid and accurate prediction methods for oil content in reinjection water. A total of 146 samples of reinjection water were collected from a sewage treatment station in the Daqing oilfield. Using UV-visible transmission spectra ranging from 190 to 900 nm, three residual neural networks (ResNet) models with different network structures and several layers were constructed for predicting oil content. Comparative analysis was performed using the joint interval partial least squares method (siPLS). Results showed that the mean absolute errors of the three ResNet models were 1.23, 0.76, and 0.29 mg/L, respectively, all demonstrating lower values than those obtained with the siPLS model, notably, increasing the number of layers in the ResNet model enhanced detection accuracy. Consequently, the ResNet model proves to be suitable for predicting oily sewage content within the 20.0 mg/L range as mandated by industry specifications.
About the Authors
X. Zhang
Heilongjiang Provincial Key Laboratory of Thermal Utilization and Disaster Reduction of New Energy in Cold Regions; School of Architecture and Civil Engineering, Northeast Petroleum University; International Joint Laboratory on Low-Carbon and New-Energy Nexus, Northeast Petroleum University
China
China
Xiaoxue Zhang
Daqing
S. Liang
Heilongjiang Provincial Key Laboratory of Thermal Utilization and Disaster Reduction of New Energy in Cold Regions; School of Architecture and Civil Engineering, Northeast Petroleum University; International Joint Laboratory on Low-Carbon and New-Energy Nexus, Northeast Petroleum University
Russian Federation
Russian Federation
Shujuan Liang
Daqing
H. Zhu
Heilongjiang Provincial Key Laboratory of Thermal Utilization and Disaster Reduction of New Energy in Cold Regions; School of Architecture and Civil Engineering, Northeast Petroleum University; International Joint Laboratory on Low-Carbon and New-Energy Nexus, Northeast Petroleum University
China
China
Hang Zhu
Daqing
H. Li
Heilongjiang Provincial Key Laboratory of Thermal Utilization and Disaster Reduction of New Energy in Cold Regions; School of Architecture and Civil Engineering, Northeast Petroleum University; International Joint Laboratory on Low-Carbon and New-Energy Nexus, Northeast Petroleum University
China
China
Huaizhi Li
Daqing
H. Qi
Heilongjiang Provincial Key Laboratory of Thermal Utilization and Disaster Reduction of New Energy in Cold Regions; School of Architecture and Civil Engineering, Northeast Petroleum University; International Joint Laboratory on Low-Carbon and New-Energy Nexus, Northeast Petroleum University
China
China
Hanbing Qi
Daqing
B. Tian
State Pipe Network Group Beijing Pipeline Company
China
China
Boyu Tian
Chaoyang
References
1. Y. Liang, Y. Ning, L. Liao, et al., Formation Damage During Improved Oil Recovery, Gulf Professional Publishing, 515–586 (2018).
2. A. Al Moajil, T. Almubarak, F. Alotaibi, et al., Middle East Oil, Gas and Geosciences Show, OnePetro (2023).
3. A. E. Ite, T. A. Harry, C. O. Obadimu, et al., J. Environ. Poll. and Human Health, 6, No. 2, 51–61 (2018).
4. P. Lv, X. Wang, Y. Chen, et al., Petroleum Sci. and Technol., 35, No. 12, 1285–1289 (2017).
5. A. Fakhru’l-Razi, A. Pendashteh, L. C. Abdullah, et al., J. Hazard. Mater., 170, No. 2-3, 530–551 (2009).
6. Y. Chen, W. Duan, Y. Yang, et al., Spectrochim. Acta A: Mol. and Biomolec. Spectrosc., 272, 120967 (2022).
7. D. Omanović, C. Santinelli, S. Marcinek, et al., Comp. Geosci., 133, 104334 (2019).
8. X. Zhu, L. Chen, J. Pumpanen, et al., Talanta, 224, 121919 (2021).
9. P. V. Ajayakumar, D. Chanda, A. Pal, et al., J. Pharm. and Biomed. Analysis, 58, 157–162 (2012).
10. C. Quintelas, D. P. Mesquita, E. C. Ferreira, et al., Talanta, 194, 507–513 (2019).
11. S. Chakraborty, D. C. Weindorf, B. Li, et al., Sci. Total Environ., 514, 399–408 (2015).
12. S. Chakraborty, D. C. Weindorf, Y. Zhu, et al., Geoderma, 177, 80–89 (2012).
13. M Mohammadi, M K Khorrami, A Vatani, et al., Spectrochim. Acta A: Mol. and Biomolec. Spectrosc., 245, 118945 (2021).
14. Y. T. Wang, Y. Zhang, F. K. Shang, et al., Spectrosc. and Spectral Analysis, 39, No. 11, 3420–3425 (2019).
15. P. F. Cheng, Y. P. Zhu, C. J. Cui, et al., Measurement & Control., 55, No. 9-10, 1078–1087 (2022).
16. L. M. He, L. L. Kear-Padilla, S. H. Lieberman, et al., Anal. Chim. Acta, 478, No. 2, 245–258 (2003).
17. P. Khatri, K. K. Gupta, R. K. Gupta, Modeling Earth Systems and Environ., 7, 703–714 (2021).
18. J. R. Etheridge, M. Randolph, C. Humphrey, J. Environ. Quality, 48, No. 2, 531–536 (2019).
19. P. Li, J. Qu, Y. He, et al., RSC Adv., 10, No. 35, 20691–20700 (2020).
20. A. Radzevičius, M. Dapkienė, N. Sabienė, et al., Appl. Sci., 10, No. 24, 9072 (2020).
21. C. Yan, X. Shen, F. Guo, Structural and Multidisciplinary Optimization, 57, 2431–2445 (2018).
22. Z. Xing, J. Chen, X. Zhao, et al., Peer J., 7, e8255 (2019).
23. H. A. Mokhtari, S. A. Mirbagheri, N. Gisou, et al., Desalination and Water Treatment, 198, 108–118 (2020).
24. K. He, X. Zhang, S. Ren, et al., IEEE Computer Society. Conference on Computer Vision and Pattern Recognition, CVPR 2016, June 26, 2016 July 1, 2016, Las Vegas, NV, United States, F (2016).
25. D. Chen, F. Hu, G. Nian, et al., Entropy, 22, No. 2, 193 (2020).
26. Q. Wang, H. Li, H. Zhao, et al., Water Science & Technology, 87, No. 7, 1779–1790 (2023).
27. F. S. Rocha, A. J. Gomes, C. N. Lunardi, et al., Canad. J. Chem. Eng., 96, No. 12, 2512–2517 (2018).
Review
For citations:
Zhang X., Liang S., Zhu H., Li H., Qi H., Tian B. Oil Content Prediction of Oilfield Reinjection Water Based on Residual Neural Networks. Zhurnal Prikladnoii Spektroskopii. 2025;92(3):407.
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