| dc.contributor.author | Quintero Arias, Leider | |
| dc.contributor.author | Ruiz Diaz, Carlos Mauricio | |
| dc.contributor.author | Gómez Camperos, July | |
| dc.contributor.author | Hernandez Rodriguez, Oscar Mauricio | |
| dc.contributor.author | Pardo Garcia, Aldo | |
| dc.coverage.spatial | Colombia | en_US |
| dc.date.accessioned | 2026-04-23T16:14:47Z | |
| dc.date.available | 2026-04-23T16:14:47Z | |
| dc.date.issued | 2026-01-07 | |
| dc.identifier.issn | 2297-3079 | en_US |
| dc.identifier.uri | https://repositorioinstitucional.ufpso.edu.co/xmlui/handle/20.500.14167/6234 | |
| dc.description.abstract | La identificación de los patrones de flujo en sistemas multifásicos es fundamental en la ingeniería de producción de hidrocarburos, ya que determinan el comportamiento del transporte de petróleo crudo a través de oleoductos y tuberías de flujo en los yacimientos petrolíferos. La clasificación adecuada de estos patrones contribuye a mejorar el diseño hidráulico, a la selección óptima de los equipos de separación y a estrategias de operación de campo eficaces. Este estudio propone un modelo basado en una red neuronal Transformer para identificar patrones de flujo en sistemas bifásicos líquido-líquido (agua-petróleo) en tuberías horizontales. Se utilizó una base de datos que contenía 2146 puntos de datos, incluyendo variables como el diámetro de la tubería, la velocidad de la mezcla, las velocidades superficiales de cada fase y la viscosidad del petróleo. Los resultados muestran un excelente rendimiento del modelo, con precisiones del 95,55 % durante el entrenamiento, del 91,28 % en la validación y del 90 % en la prueba final. Estos hallazgos demuestran la capacidad del modelo para captar relaciones complejas entre variables hidrodinámicas y topologías de flujo, lo que lo convierte en una herramienta alternativa prometedora para el análisis, la monitorización y la optimización del transporte multifásico en la industria petrolera. | en_US |
| dc.language.iso | eng | en_US |
| dc.relation | https://www.frontiersin.org/journals/mechanical-engineering/articles/10.3389/fmech.2025.1710934/full | en_US |
| dc.title | Aplicación de redes neuronales Transformer para la clasificación de patrones de flujo bifásico de petróleo y agua en tuberías horizontales | en_US |
| dc.title.translated | Application of Transformer neural networks for the classification of two-phase oil–water flow patterns in horizontal pipelines | en_US |
| dc.contributor.cvlac | Leider Quintero Arias [0002080778] | en_US |
| dc.contributor.cvlac | Carlos Mauricio Ruiz Diaz [0001772117] | en_US |
| dc.contributor.cvlac | July Gómez Camperos [0001164287] | en_US |
| dc.contributor.cvlac | Oscar Mauricio Hernandez Rodriguez [0000316504] | en_US |
| dc.contributor.cvlac | Aldo Pardo Garcia [0000173878] | en_US |
| dc.contributor.googlescholar | Carlos Mauricio Ruiz Diaz [x10qI0UAAAAJ] | en_US |
| dc.contributor.googlescholar | Oscar Mauricio Hernandez Rodriguez [i48N_sEAAAA] | en_US |
| dc.contributor.orcid | Leider Quintero Arias [0009-0008-8691-364X] | en_US |
| dc.contributor.orcid | Carlos Mauricio Ruiz Diaz [0000-0003-3836-9332] | en_US |
| dc.contributor.orcid | July Gómez Camperos [0000-0003-3894-144X] | en_US |
| dc.contributor.orcid | Oscar Mauricio Hernandez Rodriguez [0000-0003-1578-7996] | en_US |
| dc.contributor.researchgate | Carlos Mauricio Ruiz Diaz [Carlos-Mauricio-Ruiz-Diaz] | en_US |
| dc.contributor.scopus | Carlos Mauricio Ruiz Diaz [57326268000] | en_US |
| dc.contributor.scopus | July Gómez Camperos [57220550902] | en_US |
| dc.contributor.scopus | Oscar Mauricio Hernandez Rodriguez [7103218937] | en_US |
| dc.contributor.scopus | Aldo Pardo Garcia [57884427900] | en_US |
| dc.description.abstractenglish | The identification of flow patterns in multiphase systems is crucial in hydrocarbon production engineering, as they determine the behavior of crude oil transport through pipelines and flowlines in oil fields. Proper classification of these patterns contributes to improved hydraulic design, optimal selection of separation equipment, and effective field operation strategies. This study proposes a model based on a Transformer neural network for identifying flow patterns in two-phase liquid–liquid (water–oil) systems in horizontal pipelines. A database containing 2,146 data points was used, including variables such as pipe diameter, mixture velocity, superficial velocities of each phase, and oil viscosity. The results show excellent model performance, with accuracies of 95.55% during training, 91.28% in validation, and 90% in the final test. These findings demonstrate the model’s ability to capture complex relationships between hydrodynamic variables and flow topologies, making it a promising alternative tool for the analysis, monitoring, and optimization of multiphase transport in the oil industry. | en_US |
| dc.subject.proposal | Red neuronal transformadora | en_US |
| dc.subject.proposal | Identificación de patrones de flujo | en_US |
| dc.subject.proposal | Flujo bifásico | en_US |
| dc.subject.proposal | Flujo líquido-líquido | en_US |
| dc.subject.proposal | Tuberías horizontales | en_US |
| dc.subject.keywords | Transformer neural network | en_US |
| dc.subject.keywords | Flow pattern identification | en_US |
| dc.subject.keywords | Two-phase flow | en_US |
| dc.subject.keywords | Liquid–liquid flow | en_US |
| dc.subject.keywords | Horizontal pipelines | en_US |
| dc.publisher.faculty | Facultad ingenierías | en_US |
| dc.type.redcol | http://purl.org/redcol/resource_type/ART | en_US |
| dc.relation.references | Al-Naser, M., Elshafei, M., and Al-Sarkhi, A. (2016). Artificial neural network application for multiphase flow patterns detection: a new approach. J. Pet. Sci. Eng. 145, 548–564. doi:10.1016/J.PETROL.2016.06.029 | en_US |
| dc.relation.references | Al-Sarkhi, A., Pereyra, E., Mantilla, I., and Avila, C. (2017). Dimensionless oil-water stratified to non-stratified flow pattern transition. J. Pet. Sci. Eng. 151, 284–291. doi:10. 1016/J.PETROL.2017.01.016 | en_US |
| dc.relation.references | Al-Wahaibi, T., Al-Wahaibi, Y., Al-Ajmi, A., Al-Hajri, R., Yusuf, N., Olawale, A., et al. (2014). Experimental investigation on flow patterns and pressure gradient through two pipe diameters in horizontal oil-water flows. J. Pet. Sci. Eng. 122, 266–273. doi:10.1016/J. PETROL.2014.07.019 | en_US |
| dc.relation.references | Álvarez-Pacheco, C. E., Ruiz-Diaz, C. M., and Hernandez-Rodriguez, O. M. (2024). Chordal measurement of phase fraction distribution in a static gas-liquid system using collimated gamma-ray densitometer and artificial neural networks. Rev. Ingenio 21 (1), 29–35. doi:10.22463/2011642x.4098 | en_US |
| dc.relation.references | Cai, J., Li, C., Tang, X., Ayello, F., Richter, S., and Nesic, S. (2012). Experimental study of water wetting in oil-water two phase flow-horizontal flow of model oil. Chem. Eng. Sci. 73, 334–344. doi:10.1016/J.CES.2012.01.014 | en_US |
| dc.relation.references | Camperos, J. A. G., Diaz, C. M. R., and Cely, M. M. H. (2023). Specialist system in flow pattern identification using artificial neural networks. J. Appl. Eng. Sci. 21 (1), 285–299. doi:10.5937/JAES0-40309 | en_US |
| dc.relation.references | Cely, M., Cavalheiro, H. L., Camperos, J., Garcia, A. P., Pagliarini, S., Diaz, C., et al. (2024). Identificação de padrões de escoamento bifásico água-óleo em tubulações horizontais da indústria óleo-gás através de técnicas de inteligência artificial. Ed. Científica Digit. doi:10.37885/978-65-5360-640-1 | en_US |
| dc.relation.references | Chen, H., Dang, Z., Park, S. S., and Hugo, R. (2023). Robust CNN-based flow pattern identification for horizontal gas-liquid pipe flow using flow-induced vibration. Exp. Therm. Fluid Sci. 148, 110979. doi:10.1016/J.EXPTHERMFLUSCI.2023.110979 | en_US |
| dc.relation.references | Çolak, A. B. (2025a). Investigating a machine learning algorithm’s applicability for simulating the apparent viscosity of waxy crude oil in a pipeline. Int. J. Oil, Gas Coal Technol. 37 (3), 321–337. doi:10.1504/IJOGCT.2025.145438 | en_US |
| dc.relation.references | Çolak, A. B. (2025b). Performance analysis of different artificial intelligence algorithms in estimating wax appearance distance in thick-walled crude oil pipeline. Model Earth Syst. Environ. 11 (6), 1–14. doi:10.1007/S40808-025-02627-1/METRICS | en_US |
| dc.relation.references | Dasari, A., Desamala, A. B., Dasmahapatra, A. K., and Mandal, T. K. (2013). Experimental studies and probabilistic neural network prediction on flow pattern of viscous oil–water flow through a circular horizontal pipe. Ind. Eng. Chem. Res. 52 (23), 7975–7985. doi:10.1021/IE301430M | en_US |
| dc.relation.references | dos Santos Ambrosio, J., Lazzaretti, A. E., Pipa, D. R., and da Silva, M. J. (2022). Two- phase flow pattern classification based on void fraction time series and machine learning. Flow Meas. Instrum. 83, 102084. doi:10.1016/J.FLOWMEASINST.2021.102084 | en_US |
| dc.relation.references | Gómez-Camperos, J. A., R․ Diaz, C. M., Hernandez-Cely, M. M., Rodriguez, O. M. H. ․, and Pardo-Garcia, A. (2026). Dense-gas/liquid two-phase flow pattern recognition using convolutional neural networks and transfer learning. Int. J. Multiph. Flow 194, 105479. doi:10.1016/J.IJMULTIPHASEFLOW.2025.105479 | en_US |
| dc.relation.references | Grassi, B., Strazza, D., and Poesio, P. (2008). Experimental validation of theoretical models in two-phase high-viscosity ratio liquid–liquid flows in horizontal and slightly inclined pipes. Int. J. Multiph. Flow 34 (10), 950–965. doi:10.1016/j.ijmultiphaseflow. 2008.03.006 | en_US |
| dc.relation.references | Huang, Y., Wang, X., Lu, K., and Hu, J. (2025). Carbon steel pipeline CO2 erosion- corrosion damage prediction model and numerical simulation research. Geoenergy Sci. Eng. 246, 213558. doi:10.1016/J.GEOEN.2024.213558 | en_US |
| dc.relation.references | Ibarra, R., Markides, C., and Matar, O. (2015). Towards a universal dimensionless map of flow regime transitions in horizontal liquid-liquid flows. | en_US |
| dc.relation.references | Izwan Ismail, A. S., Ismail, I., Zoveidavianpoor, M., Mohsin, R., Piroozian, A., Misnan, M. S., et al. (2015). Experimental investigation of oil–water two-phase flow in horizontal pipes: pressure losses, liquid holdup and flow patterns. J. Pet. Sci. Eng. 127, 409–420. doi:10.1016/J.PETROL.2015.01.038 | en_US |
| dc.relation.references | Li, Z. C., and Fan, C. L. (2020). A novel method to identify the flow pattern of oil–water two-phase flow. J. Pet. Explor Prod. Technol. 10 (8), 3723–3732. doi:10.1007/ S13202-020-00987-1 | en_US |
| dc.relation.references | Li, C., Liu, K., and Liu, S. (2025). A Survey of loss functions in deep learning. Mathematics 13 (15), 2417. doi:10.3390/MATH13152417 | en_US |
| dc.relation.references | Liu, Z., Yin, P., Mou, C., Wang, S., Shang, L., Cao, X., et al. (2024). Unraveling the corrosion behavior and mechanism of DLC film on the inner surface of N80 lengthy pipeline in CO2-H2S-Cl− environment. Surf. Coat. Technol. 494, 131350. doi:10.1016/J. SURFCOAT.2024.131350 | en_US |
| dc.relation.references | Liu, Z., Liu, J., Mou, C., Zhang, L., Cao, X., Chen, L., et al. (2025). Anti-sediment wear coupled corrosion protective coating for oil and gas exploitation pipeline protection. Chem. Eng. J. 505, 159867. doi:10.1016/J.CEJ.2025.159867 | en_US |
| dc.relation.references | Meng, S., Yue, P., Zhang, S., Li, H., Zhao, Y., Hua, Y., et al. (2024). Synergistic effects of CO2 and H2S on stress corrosion cracking of stainless steel 254SMo in extremely aggressive oilfield environment. Corros. Commun. 16, 81–95. doi:10.1016/J.CORCOM. 2024.03.003 | en_US |
| dc.relation.references | Nädler, M., and Mewes, D. (1997). Flow induced emulsification in the flow of two immiscible liquids in horizontal pipes. Int. J. Multiph. Flow 23 (1), 55–68. doi:10.1016/ s0301-9322(96)00055-9 | en_US |
| dc.relation.references | OuYang, L., Jin, N., Ren, W., and Zong, Y. (2023). FPRnet: a lightweight multi-domain multi-stream network for complex horizontal oil-water two-phase flow pattern recognition. Flow Meas. Instrum. 94, 102471. doi:10.1016/J.FLOWMEASINST.2023. 102471 | en_US |
| dc.relation.references | Perera, K., Pradeep, C., Mylvaganam, S., and Time, R. W. (2017). Imaging of oil-water flow patterns by Electrical Capacitance tomography. Flow Meas. Instrum. 56, 23–34. doi:10.1016/J.FLOWMEASINST.2017.07.002 | en_US |
| dc.relation.references | Rodriguez, O. M. H., and Oliemans, R. V. A. (2006). Experimental study on oil–water flow in horizontal and slightly inclined pipes. Int. J. Multiph. Flow 32 (3), 323–343. doi:10.1016/J.IJMULTIPHASEFLOW.2005.11.001 | en_US |
| dc.relation.references | Ruiz-Díaz, C. M., Quispe-Suarez, B., and González-Estrada, O. A. (2024a). Two-phase oil and water flow pattern identification in vertical pipes applying long short-term memory networks. Emergent Mater 7 (5), 1983–1995. doi:10.1007/s42247-024-00631-2 | en_US |
| dc.relation.references | Ruiz-Díaz, C. M., Perilla-Plata, E. E., and González-Estrada, O. A. (2024b). Two-phase flow pattern identification in vertical pipes using transformer neural networks. Inventions 9 (1), 15. doi:10.3390/inventions9010015 | en_US |
| dc.relation.references | Ruiz-Diaz, C. M., Pacheco, C. E. A., da Silva, E. O., Bochio, G., Postal, A. T., and Rodriguez, O. M. H. (2026). Experimental study on dense-gas/oil flow in horizontal and slightly upward inclined pipes. Int. J. Multiph. Flow 194, 105477. doi:10.1016/J. IJMULTIPHASEFLOW.2025.105477 | en_US |
| dc.relation.references | Shi, J., and Yeung, H. (2017). Characterization of liquid-liquid flows in horizontal pipes. AIChE J. 63 (3), 1132–1143. doi:10.1002/AIC.15452 | en_US |
| dc.relation.references | Shi, J., Lao, L., and Yeung, H. (2017). Water-lubricated transport of high-viscosity oil in horizontal pipes: the water holdup and pressure gradient. Int. J. Multiph. Flow 96, 70–85. doi:10.1016/J.IJMULTIPHASEFLOW.2017.07.005 | en_US |
| dc.relation.references | Shi, X., Dong, F., and Tan, C. (2021). Horizontal oil-water two-phase flow characterization and identification with pulse-wave ultrasonic Doppler technique. Chem. Eng. Sci. 246, 117015. doi:10.1016/J.CES.2021.117015 | en_US |
| dc.relation.references | Shi, S., Liu, J., Hu, H., and Zhou, H. (2023). A research on a GA-BP neural network based model for predicting patterns of oil-water two-phase flow in horizontal wells. Geoenergy Sci. Eng. 230, 212151. doi:10.1016/J.GEOEN.2023.212151 | en_US |
| dc.relation.references | Tan, J., Jing, J., Hu, H., and You, X. (2018). Experimental study of the factors affecting the flow pattern transition in horizontal oil–water flow. Exp. Therm. Fluid Sci. 98, 534–545. doi:10.1016/J.EXPTHERMFLUSCI.2018.06.020 | en_US |
| dc.relation.references | Tan, J., Hu, H., Vahaji, S., Jing, J., and Tu, J. (2022). Effects of drag-reducing polymers on the flow patterns, pressure gradients, and drag-reducing rates of horizontal oil–water flows. Int. J. Multiph. Flow 153, 104136. doi:10.1016/J.IJMULTIPHASEFLOW.2022.104136 | en_US |
| dc.relation.references | Trallero, J. L., and of Tulsa, U. (1995). Oil-water flow patterns in horizontal pipes. Tulsa, Oklahoma: Department of Petroleum, Engineering, University of Tulsa. Ph.D. Thesis. | en_US |
| dc.relation.references | Trallero, J. L., Sarica, C., and Brill, J. P. (1997). A study of Oil/water flow patterns in horizontal pipes. SPE Prod. & Facil. 12 (03), 165–172. doi:10.2118/36609-PA | en_US |
| dc.relation.references | Vargas, R. E. V., Munaro, C. J., Ciarelli, P. M., Medeiros, A. G., Amaral, B. G. d., Barrionuevo, D. C., et al. (2019). A realistic and public dataset with rare undesirable real events in oil wells. J. Pet. Sci. Eng. 181, 106223. doi:10.1016/J. PETROL.2019.106223 | en_US |
| dc.relation.references | Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., et al. (2017). Attention is all you need. Adv. Neural Inf. Process Syst. 30. Available online at: https:// proceedings.neurips.cc/paper_files/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa- Paper.pdf. | en_US |
| dc.relation.references | Wang, W., Cheng, W., Li, K., Lou, C., and Gong, J. (2013). Flow patterns transition law of oil-water two-phase flow under a wide range of oil phase viscosity condition. J. Appl. Math. 2013 (SI14), 1–8. doi:10.1155/2013/291217 | en_US |
| dc.relation.references | Wegmann, A., and Rudolf von Rohr, P. (2006). Two phase liquid-liquid flows in pipes of small diameters. Int. J. Multiph. Flow 32 (8), 1017–1028. doi:10.1016/j. ijmultiphaseflow.2006.04.001 | en_US |
| dc.relation.references | Wu, Y., Guo, H., Song, H., and Deng, R. (2022). Fuzzy inference system application for oil-water flow patterns identification. Energy 239, 122359. doi:10.1016/J.ENERGY.2021. 122359 | en_US |
| dc.relation.references | Yang, J., Li, P., Zhang, X., Lu, X., Li, Q., and Mi, L. (2021). Experimental investigation of oil–water flow in the horizontal and vertical sections of a continuous transportation pipe. Sci. Rep. 11 (1), 20092. doi:10.1038/S41598-021-99660-8 | en_US |
| dc.relation.references | Zhang, A., Lipton, Z. C., Li, Mu., and Smola, A. J. (2024). Dive into deep learning, 548. | en_US |
| dc.identifier.DOI | 10.3389/fmech.2025.1710934 | en_US |
