Temporal dynamics and predictive modeling of produced water quality in Niger Delta oil fields
DOI:
https://doi.org/10.64497/jssci.140Keywords:
produced water, oil-in-water, salinity, time series analysis, seasonal decomposition, environmental complianceAbstract
Management of produced water in the Niger Delta is becoming complicated due to varying effluent characteristics as a result of seasonal and long-term variations in the environment. Successful treatment and compliance with regulations demand precise understanding of these temporal dynamics. In this study, trends, seasonal cycles, and abrupt changes in oil in water (OIW), total dissolved solids (TDS), and conductivity in major fields in the Niger Delta of Nigeria have been examined using Time-series methods in R. A 7-year dataset from 15 crude oil formations’ produced water was characterized with several statistical tools. Seasonality of OIW concentrations was significant, with peak concentrations in the wet season ranging from 45 to 180 mg L⁻¹ and a long-term increasing trend of 2.3 mg L⁻¹ yr ⁻¹ (p < 0.001). TDS had a 0.34 coefficient of variation and significant structural changes in 2019 and 2022. Both SARIMAX and Prophet Models had robust forecast performance, but the Prophet Model was marginally superior to SARIMAX. The results indicate that present treatment facilities respond too inadequately to seasonal stressors, while it accommodates base loads; it is insufficient for peak conditions. Incorporating the use of predictive models with adaptive treatment regimes could make operations more efficient in the oil-producing facilities by 23–31 %, allowing more proactive management and more robust environmental compliance for all facilities in the oil industry.
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[1] S. M. Adelana,, K. T. Oladepo, and O. O. Ige, Hydrogeochemical evolution and pollution assessment of groundwater in the Niger Delta Basin, Nigeria. Environmental Earth Sciences, 81(15), 1-18 2022, 2022. https://doi.org/10.1007/s12665-022-10456-3
[2] I. C. Howard, Performance evaluation of a produced water treatment plant in a crude oil production facility. Discovery, 52 (249) 1713-1720, 2016
[3] E. T. Igunnu and G. Z. Chen, Produced water treatment technologies. International Journal of Low-Carbon Technologies, 9(3), 157-177, 2024. https://doi.org/10.1093/ijlct/ctu003 DOI: https://doi.org/10.1093/ijlct/cts049
[4] K. K. Dagde, J.G. Akpa, and C.G.J. Nmegbu, Integrated treatment systems for Niger Delta produced water: Design optimization and performance evaluation. Process Safety and Environmental Protection, 182, 456-470, 2024. https://doi.org/10.1016/j.psep.2023.12.045 DOI: https://doi.org/10.1016/j.psep.2023.12.045
[5] S. Deng, G. Yu, and Y. P. Ting, Treatment of produced water from polymer flooding in oil recovery. Water Research, 200, 117237 2021. https://doi.org/10.1016/j.watres.2021.117237 DOI: https://doi.org/10.1016/j.watres.2021.117237
[6] O. O. Osokoya, J.G. Akpa, and K. K. Dagde, Environmental challenges and sustainable solutions for produced water management in Nigeria's Niger Delta: A comprehensive review. Journal of Environmental Management, 331, 117289, 2023. https://doi.org/10.1016/j.jenvman.2023.117289 DOI: https://doi.org/10.1016/j.jenvman.2023.117289
[7] C.C. Howard, E. H. Etuk, and I. C Howard, Evaluation of Auto regressive integrated moving average (ARIMA) and Artificial neural networks (ANN) in the prediction of effluent quality of a wastewater treatment system. Global Jou Pure and App. Scs. 28 (10);83-90, 2022. DOI: https://dx.doi.org/10.4314/gjpas.v28i1.10 (accessed September 8, 2025) DOI: https://doi.org/10.4314/gjpas.v28i1.10
[8] A. U. Ukwoegbu, E. I. Nwosu, and I. I. Obiadi, Assessment of formation water characteristics and their impact on produced water quality in Niger Delta oil fields. Journal of African Earth Sciences, 197, 104756, 2023. https://doi.org/10.1016/j.jafrearsci.2022.104756
[9] A. M. Hassan, H. M. Al-Hashemi, and A. S. Al-Ajmi, Dynamic optimization of produced water treatment systems using machine learning approaches. Computers & Chemical Engineering, 139, 106891, 2020. https://doi.org/10.1016/j.compchemeng.2020.106891 DOI: https://doi.org/10.1016/j.compchemeng.2020.106891
[10] M. A. Al-Ghouti, M. A. Al-Kaabi, M. Y. Ashfaq, and D. A. Da'na, Produced water characteristics, treatment and reuse: A review. Journal of Water Process Engineering, 28, 222-239, 2019. https://doi.org/10.1016/j.jwpe.2019.02.001 (accessed September 8, 2025) DOI: https://doi.org/10.1016/j.jwpe.2019.02.001
[11] I. C. Howard, C. C. Howard, and A.O. Briggs, Predicting BOD5 in the produced and surface waters of a crude oil production facility in the Niger Delta. ASUP Multidisciplinary Journal of Science, Business & Technology. 2, ( 1), 1-8, 2015.
[12] H. O. Nwankwoala, and G. T. Amangabara, Hydrogeochemical evaluation of groundwater quality in parts of eastern Niger Delta, Nigeria. Journal of Applied Sciences and Environmental Management, 24(9), -1591, 2020. https://doi.org/10.4314/jasem.v24i9.12 DOI: https://doi.org/10.4314/jasem.v24i9.12
[13] F. Zhao, H. Liu, and P. Zhang, Temporal variations in offshore produced water quality: Implications for treatment system design. Marine Pollution Bulletin, 165, 112153, 2021. https://doi.org/10.1016/j.marpolbul.2021.112153 DOI: https://doi.org/10.1016/j.marpolbul.2021.112153
[14] C. N. Okonkwo, A. Demoz, and A. Kumar, Seasonal variations in produced water characteristics and treatment efficiency in Nigerian oil fields. Environmental Science and Pollution Research, 30(18), 52184-52197, 2023. https://doi.org/10.1007/s11356-023-26045-8
[15] R. B. Cleveland, W.S. Cleveland, J. E. McRae, and I. Terpenning, STL: A seasonal-trend decomposition procedure based on loess. Journal of Official Statistics, 6(1), 3-73, 2019.
[16] L. Wang, S. Chen, and Y. Liu, SARIMAX modeling for water quality parameter prediction: A case study of industrial wastewater treatment. Water Science and Technology, 85(8), 2234-2248, 2022. https://doi.org/10.2166/wst.2022.109 DOI: https://doi.org/10.2166/wst.2022.109
[17] S. J. Taylor,and B. Letham, B. Forecasting at scale. The American Statistician, 72(1), 37-45, 2018. https://doi.org/10.1080/00031305.2017.1380080 (accessed September 8, 2025) DOI: https://doi.org/10.1080/00031305.2017.1380080
[18] R. Chen, J. Liu, and H. Zhang, Enhanced time series forecasting with Facebook Prophet: Recent developments and applications. Journal of Forecasting, 43(2), 445-462, 2024. https://doi.org/10.1002/for.3087 (accessed September 10, 2025) DOI: https://doi.org/10.1002/for.3087
[19] R. Killick, and I. A. Eckley, Changepoint: An R package for changepoint analysis. Journal of Statistical Software, 58(3), 1-19, 2021. https://doi.org/10.18637/jss.v058.i03 DOI: https://doi.org/10.18637/jss.v058.i03
[20] E. H. Ezechi, M. H. Isa, and Z. Ahmed, Advances in produced water treatment technologies: An overview. Process Safety and Environmental Protection, 157, 466-484, 2022. https://doi.org/10.1016/j.psep.2021.11.040. (accessed September 12, 2025) DOI: https://doi.org/10.1016/j.psep.2021.11.040
[21] Y., Li, M. Zhang, and X. Wang, Smart produced water management: Integration of IoT and machine learning for real-time optimization. Journal of Petroleum Science and Engineering, 218, 110956, 2023. https://doi.org/10.1016/j.petrol.2022.110956 (accessed September 10, 2025)
[22] Sharma, P., Kumar, A., & Singh, R. (2024). Predictive maintenance of water treatment systems using advanced analytics. Water Resources Management, 38(7), 2445-2463. https://doi.org/10.1007/s11269-024-03768-1. (accessed September 15, 2025) DOI: https://doi.org/10.1007/s11269-024-03768-1
[23] M. Ahmed, A. Arakel, D. Hoey, M.R. Thumarukudy, M.F. Goosen, M. Al-Haddabi, and A. Al-Belushi, Feasibility of salt production from inland RO desalination plant reject brine: A case study. Desalination, 158(1-3), 109-117, 2020. DOI: https://doi.org/10.1016/S0011-9164(03)00441-7
[24] Department of Petroleum Resources, Environmental guidelines and standards for the petroleum industry in Nigeria (EGASPIN 2021). Nigerian Upstream Petroleum Regulatory Commission. 2021. https://www.nuprc.gov.ng/wp-content/uploads/2021/10/EGASPIN-2021-Final.pdf
[25] A. R. Pendashteh, L. C. Abdullah, A. Fakhru'l-Razi, S. S. Madaeni, Z. Zainal Abidin, and D. R. Awang Biak, Evaluation of membrane bioreactor for hypersaline oily wastewater treatment. Process Safety and Environmental Protection, 90(1), 45-55, 2021 https://doi.org/10.1016/j.psep.2011.07.006 (accessed September 15 2025 DOI: https://doi.org/10.1016/j.psep.2011.07.006
[26] S. Munirasu, M.A. Haija, and F. Banat, Use of membrane technology for oil field and refinery produced water treatment—A review. Process Safety and Environmental Protection, 100, 183-202, 2016. https://doi.org/10.1016/j.psep.2016.01.010 (accessed September 11 2025 DOI: https://doi.org/10.1016/j.psep.2016.01.010
[27] M. Bagheri, S. A. Mirbagheri, Z. Bagheri, and A. M. Kamarkhani, Modeling and optimization of activated sludge bulking for a real wastewater treatment plant using hybrid artificial neural networks-genetic algorithm approach. Process Safety and Environmental Protection, 95, 12-25, 2015. https://doi.org/10.1016/j.psep.2015.02.008 (accessed September 12 2025). DOI: https://doi.org/10.1016/j.psep.2015.02.008
[28] S. Heo, K.. Nam, S. Tariq, J. Y. Lim, J. Park, and C. Yoo, A hybrid machine learning–based multi-objective supervisory control strategy of a full-scale wastewater treatment for cost-effective and sustainable operation under varying operating conditions. Journal of Cleaner Production, 291, 125853. 2021. https://doi.org/10.1016/j.jclepro.2021.125853 (accessed September 12 2025). DOI: https://doi.org/10.1016/j.jclepro.2021.125853
[29] S. P. Verma, R. R. Dash, and P. Bhunia, A review on chemical coagulation/flocculation technologies for removal of colour from textile wastewaters. Journal of Environmental Management, 93(1), 154-168, 2012. https://doi.org/10.1016/j.jenvman.2011.09.012 (accessed September 10 2025). DOI: https://doi.org/10.1016/j.jenvman.2011.09.012
[30] E. W. Tow, and J. H. Lienhard, Quantifying osmotic membrane distillation performance: Asimple model framework. Desalination, 399, 129-143, 2016. https://doi.org/10.1016/j.desal.2016.08.021 (accessed September 10 2025). DOI: https://doi.org/10.1016/j.desal.2016.08.021
[31] B. H. Diya'uddeen, W. M. A. W. Daud, and A.R. Abdul Aziz, Treatment technologies for petroleum refinery effluents: A review. Process Safety and Environmental Protection, 89(2), 95-105 2011. https://doi.org/10.1016/j.psep.2010.11.003 (accessed September 11 2025) DOI: https://doi.org/10.1016/j.psep.2010.11.003
[32] G. Tchobanoglous, H. D Stensel, R. Tsuchihashi, and F. Burton, Wastewater Engineering: Treatment and Resource Recovery (5th ed.). McGraw-Hill Education. 2014. (accessed September 10, 2025)
[33] X. Zhao, Y. Cao, S. Wang, H. Ma, and J. Wang, Model predictive control of wastewater treatment processes. In Automotive Air Conditioning. pp. 225-245. Springer. 2019, https://doi.org/10.1007/978-981-13-9176-5_11
[34] O. Triebe, N. Laptev, and R. Rajagopal, AR-Net: A simple autoregressive neural network for time-series. arXiv preprint arXiv:1911.12436, 2021.
[35] K. K. R. Samal, K.S. Babu, and S. K. Das, Time series based air pollution forecasting using SARIMA and prophet model. Proceedings of the 2021 International Conference on Artificial Intelligence and Smart Systems (ICAIS), 1327-1332, 2021. https://doi.org/10.1109/ICAIS50930.2021.9395954 (accessed September 11 2025) DOI: https://doi.org/10.1109/ICAIS50930.2021.9395954
[36] M. Reichstein, G.Camps-Valls, B. Stevens, M. Jung, J Denzler, N. Carvalhais, and A. Prabhat. Deep learning and process understanding for data-driven Earth system science. Nature, 566(7743), 195-204, 2019. https://doi.org/10.1038/s41586-019-0912-1 (accessed September 10 2025) DOI: https://doi.org/10.1038/s41586-019-0912-1
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