Data-Driven Method for Predicting the Transportable Maximum Gas–Oil Ratio
Publication: Journal of Pipeline Systems Engineering and Practice
Volume 15, Issue 4
Abstract
In the process of oil–gas mixing transportation, a too-high gas–oil ratio (GOR) will lead to instable flow pattern and high pipeline pressure, which has great safety risks. Therefore, it is important to determine the maximum GOR. At present, it relies mainly on commercial software to simulate the operation of the mixture transportation pipeline and the hydrothermal operating parameters to determine the maximum GOR under certain condition. This enumeration method is time-consuming and does not apply to continuously parameters. To solve this problem, a data-driven predictive model is developed. The new features are constructed by analyzing the factors influencing the transportable maximum gas–oil ratio (TMGOR), and the highly correlated features are selected from them as the new features. After analyzing the characteristics of the target variables, data mapping is performed, and the processed data set is fed into a neural network for training to obtain a data-driven predictive model of TMGOR. Finally, the validation is carried out with field data from an oilfield block in northwest China. The results showed that the average relative error of the model does not exceed 8.2% compared with the simulation results of commercial software, which has a high accuracy and can provide a rationale for the decision-making of mixed transfer in the field.
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Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
This work was partially supported by the National Natural Science Foundation of China (52202405) and the Science Foundation of China University of Petroleum, Beijing (2462021BJRC009).
References
Abusahmin, B. S., R. R. Karri, and B. B. Maini. 2017. “Influence of fluid and operating parameters on the recovery factors and gas oil ratio in high viscous reservoirs under foamy solution gas drive.” Fuel 197 (Jun): 497–517. https://doi.org/10.1016/j.fuel.2017.02.037.
Agwu, O. E., E. E. Okoro, and S. E. Sanni. 2022. “Modelling oil and gas flow rate through chokes: A critical review of extant models.” J. Pet. Sci. Eng. 208 (Jan): 109775. https://doi.org/10.1016/j.petrol.2021.109775.
Al Dhaif, R., A. F. Ibrahim, S. Elkatatny, and D. Al Shehri. 2021. “Prediction of oil rates using machine learning for high gas oil ratio and water cut reservoirs.” Flow Meas. Instrum. 82 (Dec): 102065. https://doi.org/10.1016/j.flowmeasinst.2021.102065.
Ballesteros Martínez, M., E. Pereyra, and N. Ratkovich. 2020. “CFD study and experimental validation of low liquid-loading flow assurance in oil and gas transport: Studying the effect of fluid properties and operating conditions on flow variables.” Heliyon 6 (12): e05705. https://doi.org/10.1016/j.heliyon.2020.e05705.
Beloglazov, I., V. Morenov, and E. Leusheva. 2021. “Flow modeling of high-viscosity fluids in pipeline infrastructure of oil and gas enterprises.” Egypt. J. Pet. 30 (4): 43–51. https://doi.org/10.1016/j.ejpe.2021.11.001.
Eghbalian, M., M. Pouragha, and R. Wan. 2023. “A physics-informed deep neural network for surrogate modeling in classical elasto-plasticity.” Comput. Geotech. 159 (Jul): 105472. https://doi.org/10.1016/j.compgeo.2023.105472.
Jiao, Y., R. Qiu, Y. Liang, Q. Liao, R. Tu, X. Wei, and H. Zhang. 2022. “Integration optimization of production and transportation of refined oil: A case study from China.” Chem. Eng. Res. Des. 188 (Dec): 39–49. https://doi.org/10.1016/j.cherd.2022.09.037.
Jing, J., M. Du, R. Yin, Y. Wang, and Y. Teng. 2020. “Numerical study on two-phase flow characteristics of heavy oil-water ring transport boundary layer.” J. Pet. Sci. Eng. 191 (Aug): 107173. https://doi.org/10.1016/j.petrol.2020.107173.
Li-Hui, F., and D. Junfeng. 2023. “Mixed oil detection method based on tapered fiber SPR sensor.” Opt. Fiber Technol. 78 (Jul): 103322. https://doi.org/10.1016/j.yofte.2023.103322.
Mao, G., L. Xie, K. Wang, and Z. Li. 2023. “Flow pattern analysis of the oil-water batch transportation using a wheel flow loop.” Geoenergy Sci. Eng. 223 (Apr): 211534. https://doi.org/10.1016/j.geoen.2023.211534.
Meriem-Benziane, M., B. Bou-Saïd, and B. Abdelkader. 2021. “A CFD modeling of oil-water flow in pipeline: Interaction analysis and identification of boundary separation.” Pet. Res. 6 (2): 172–177. https://doi.org/10.1016/j.ptlrs.2020.10.004.
Morais, L. B. S., G. Aquila, V. A. D. de Faria, L. M. M. Lima, J. W. M. Lima, and A. R. de Queiroz. 2023. “Short-term load forecasting using neural networks and global climate models: An application to a large-scale electrical power system.” Appl. Energy 348 (Oct): 121439. https://doi.org/10.1016/j.apenergy.2023.121439.
Olabode, O., C. Nnorom, S. Ajagunna, K. Awelewa, P. Umunna, and O. Uzodinma. 2022. “Dataset on modelling a synthetic oil rim reservoirs for optimizing oil production during gas cap blow down strategy.” Data Brief 45 (Dec): 108652. https://doi.org/10.1016/j.dib.2022.108652.
Olmez Turan, M., and T. Flamand. 2023. “Optimizing investment and transportation decisions for the European natural gas supply chain.” Appl. Energy 337 (May): 120859. https://doi.org/10.1016/j.apenergy.2023.120859.
Oulmelk, A., M. Srati, L. Afraites, and A. Hadri. 2023. “An artificial neural network approach to identify the parameter in a nonlinear subdiffusion model.” Commun. Nonlinear Sci. Numer. Simul. 125 (Oct): 107413. https://doi.org/10.1016/j.cnsns.2023.107413.
Sheng, S., and X. Wang. 2023. “Network traffic anomaly detection method based on chaotic neural network.” Alexandria Eng. J. 77 (Aug): 567–579. https://doi.org/10.1016/j.aej.2023.07.019.
Wang, G., Q. Cheng, W. Zhao, Q. Liao, and H. Zhang. 2022a. “Review on the transport capacity management of oil and gas pipeline network: Challenges and opportunities of future pipeline transport.” Energy Strategy Rev. 43 (Sep): 100933. https://doi.org/10.1016/j.esr.2022.100933.
Wang, Z., Z. Fan, X. Zhang, B. Liu, and X. Chen. 2022b. “Status, trends and enlightenment of global oil and gas development in 2021.” Pet. Explor. Dev. 49 (5): 1210–1228. https://doi.org/10.1016/S1876-3804(22)60344-6.
Yang, J., and J. Zhao. 2023. “A novel parallel merge neural network with streams of spiking neural network and artificial neural network.” Inf. Sci. 642 (Sep): 119034. https://doi.org/10.1016/j.ins.2023.119034.
Zhang, Y., S. Li, X. Dou, S. Wang, Y. He, and Q. Feng. 2023. “Molecular insights into the natural gas regulating tight oil movability.” Energy 270 (May): 126895. https://doi.org/10.1016/j.energy.2023.126895.
Information & Authors
Information
Published In
Journal of Pipeline Systems Engineering and Practice
Volume 15 • Issue 4 • November 2024
Copyright
© 2024 American Society of Civil Engineers.
History
Received: Nov 30, 2023
Accepted: Feb 20, 2024
Published online: Jun 21, 2024
Published in print: Nov 1, 2024
Discussion open until: Nov 21, 2024
ASCE Technical Topics:
- Artificial intelligence and machine learning
- Computer programming
- Computer software
- Computing in civil engineering
- Data analysis
- Engineering fundamentals
- Errors (statistics)
- Field tests
- Mathematics
- Methodology (by type)
- Model accuracy
- Models (by type)
- Neural networks
- Parameters (statistics)
- Research methods (by type)
- Statistics
- Tests (by type)
Authors
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