REVISTA FUENTES

v. 19 n. 2 (2021): Revista Fuentes, el reventón energético Volumen 19 n° 2
Artigos

Pronóstico de estabilidad de asfaltenos en petróleo crudo con base en análisis SARA mediante redes neuronales artificiales

PDF (Español (España))
  • Tomás Darío Marín-Velásquez
Tomás Darío Marín-Velásquez
Universidad de Oriente, Nucleo de Monagas
DOI: https://doi.org/10.18273/revfue.v19n2-2021003

Publicado 2021-12-10

Palavras-chave

  • Asfaltenos,
  • estabilidad,
  • estadística multivariante,
  • petróleo crudo,
  • SARA

Como Citar

Marín-Velásquez, T. D. (2021). Pronóstico de estabilidad de asfaltenos en petróleo crudo con base en análisis SARA mediante redes neuronales artificiales. REVISTA FUENTES, 19(2), 19–33. https://doi.org/10.18273/revfue.v19n2-2021003

Copyright (c) 2021 Universidad Industrial de Santander

Creative Commons License
Este trabalho está licenciado sob uma licença Creative Commons Attribution 4.0 International License.

Resumo

La estabilidad del petróleo o su tendencia a producir precipitación de asfaltenos debe ser estimada, debido a su importancia para predecir problemas de obstrucciones de tuberías y equipos de procesos. A partir de las fracciones de hidrocarburos componentes del petróleo, denominadas fracciones SARA (Saturados, Aromáticos, Resinas y Asfaltenos) se han generado índices para estimar la condición de estabilidad con base en las relaciones de solubilidad e insolubilidad de los asfaltenos respecto a las otras fracciones a partir de estudios de laboratorio y análisis matemático. En la presente investigación se analiza la aplicabilidad de pruebas estadísticas multivariantes por Redes Neuronales Artificiales (RNA) para pronosticar la condición de estabilidad determinada a partir de dos índices, la Inestabilidad Coloidal (CII) y el Índice de Estabilidad (IE), además se proponen modificaciones de rangos con base en los resultados y un índice basado en la solubilidad/insolubilidad (IIS). Se utilizó como muestra de estudio 193 análisis SARA de petróleos de diferentes países obtenidas de artículos publicados en revistas científicas para la creación de la RNA, con la que se pronosticó el porcentaje de clasificación correcta basada en la interacción y tendencia de relaciones entre las cuatro fracciones en su conjunto. Adicionalmente se utilizaron 11 muestras externas a las utilizadas en el modelo RNA para validar el mismo. Se obtuvo que laRNA clasificó correctamente el 92,75% de la condición de estabilidad determinada con el CII y 88,60% respecto al IE. El ajuste de los rangos de estabilidad mejoró el pronóstico a 97,41% respecto al CII y 96,89% con el IE. El uso del IIS demostró un menor ajuste según la RNA con 98,45% de casos correctamente clasificados. Se demostró la aplicabilidad de la metodología de RNA para clasificar la condición de estabilidad de muestras de petróleo con base en sus fracciones SARA.

PDF (Español (España))

Downloads

Não há dados estatísticos.

Referências

  1. Akbarzadeh, K., Allenson, S., Creek, J., & Jamaluddin, A. (2007). Asphaltenes-Problematic but Rich in Potential. Oilfield Review, 9(2), 22-48
  2. Akmaz, S., Iscan, O., Gurkaynak, M.A., & Yasar, M. (2011). The Structural Characterization of Saturate, Aromatic, Resin, and Asphaltene Fractions of Batiraman Crude Oil. Petroleum Science and Technology, 29,160–17. doi: 10.1080/10916460903330361
  3. Alonso-Ramírez, G., Cuevas-García, R., Sánchez-Minero, F., Ramírez, J., Moreno-Montiel, M., Ancheyta, J., & Carbajal-Vielman, R. (2020). Catalytic hydrocracking of a Mexican heavy oil on a MoS2/Al2O3 catalyst: I. Study of the transformation of isolated saturates fraction obtained from SARA analysis. Catalysis Today, 353, 153-162. doi: 10.1016/j.cattod.2019.07.031
  4. Arya, A., von Solms, N., & Kontogeorgis, G. M. (2015). Determination of Asphaltene Onset Conditions using the Cubic Plus Association Equation of State. Fluid Phase Equilibria, 400, 8-19. doi: 10.1016/j.fluid.2015.04.032
  5. Ashoori, S., Sharifi, M., Masoumi, M., & Salehi, M.M. (2017). The relationship between SARA fractions and crude oil stability. Egyptian Journal of Petroleum, 26, 209-213. doi: 10.1016/j.ejpe.2016.04.002
  6. Aske, N., Kallevik, H., & Sjöblom, J. (2001). Determination of Saturate, Aromatic, Resin, and Asphaltenic (SARA) Components in Crude Oils by Means of Infrared and Near-Infrared Spectroscopy. Energy & Fuels, 15, 1304-1312. doi: 10.1021/ef010088h
  7. Asomaning, S. (2003). Test Methods for Determining Asphaltene Stability in Crude Oils. Petroleum Science and Technology, 21(3-4), 581-590. doi: 10.1081/LFT-120018540
  8. Asomaning, S., & Watkinson, A.P. (2000). Petroleum Stability and Heteroatom Species Effects in Fouling of Heat Exchangers by Asphaltenes. Heat Transfer Engineering, 21(3), 10-16. doi: 10.1080/014576300270852
  9. ASTM D2007. (2011). Standard Test Method for Characteristic Groups in Rubber Extender and Processing Oils and Other Petroleum-Derived Oils by the Clay-Gel Absorption Chromatographic Method. West Conshohocken, PA: American Society for Testing and Materials
  10. Basheer, I.A., & Hajmeer, M. (2000). Artificial neural networks: fundamentals, computing, design, and application. Journal of Microbiological Methods, 43(1), 3–31. doi: 10.1016/s0167-7012(00)00201-3
  11. Bisht, H., Reddy, M., Malvanker, M., Patil, R.C., Gupta, A., Hazarika, B., & Das, A.K. (2013). Efficient and Quick Method for Saturates, Aromatics, Resins, and Asphaltenes Analysis of Whole Crude Oil by Thin-Layer Chromatography−Flame Ionization Detector. Energy & Fuels, 27, 3006-3013. doi: 10.1021/ef4002204
  12. Brahma, K.K., Bendedouch, D., Bouhadda, Y., Bouanani, F., Bounaceur, B., & Sardi, A. (2019). Stability of Hassi-messaoud Asphaltenes in Media of Different Polarities. Petroleum Chemistry, 59(11), 1190–1194. doi: 10.1134/s0965544119110094
  13. Campen, S., Moorhouse, S.J., & Wong, J.S.S. (2019). Mechanism of an asphaltene inhibitor in different depositing environments: Influence of colloid stability. Journal of Petroleum Science and Engineering, 106502. doi: 10.1016/j.petrol.2019.106502.
  14. Carnahan, N., Salager, J-L., & Antón, R. (2007, April 30-May 3). Effect of Resins on Stability of Asphaltenes [Conference presentation]. Offshore Technology Conference, Houston, Texas, United States. doi: 10.4043/19002-ms
  15. Chamkalani, A., Mohammadi, A.H., Eslamimanesh, A., Gharagheizi, F., & Richon, D. (2012). Diagnosis of asphaltene stability in crude oil through ‘‘two parameters’’ SVM model. Chemical Engineering Science, 81, 202–208. doi: 10.1016/j.ces.2012.06.060
  16. Cheshkova, T.V., Kovalenko, E.Y., Sergun, V.P., Gerasimova, N.N., Sagachenko, T.A., & Min, R.S. (2019). Oil Resins and Asphaltenes of Different Chemical Nature. Chemistry for Sustainable Development, 1, 78-85. doi: 10.15372/CSD20190113
  17. Codină, G.G., Dabija, A., & Oroian, M. (2019). Prediction of Pasting Properties of Dough from Mixolab Measurements Using Artificial Neuronal Networks. Foods, 8(10), 447-459. doi: 10.3390/foods8100447
  18. Fan, T., & Buckley, J. S. (2002). Rapid and Accurate SARA Analysis of Medium Gravity Crude Oils. Energy & Fuels, 16, 1571-1575. doi: 10.1021/ef0201228
  19. Fan, T., Wang, J., & Buckley, J. S. (2002). Evaluating Crude Oils by SARA Analysis. [Conference presentation]. SPE Annual Technical Conference and Exhibition, Tulsa, Oklahoma, USA. doi: 10.2118/75228-MS
  20. Galvis, L.V., Ochoa, C.A., Arguello, H., Carvajal, J.M., & Calderón, Z.H. (2011). Estimación de propiedades mecánicas de roca utilizando inteligencia artificial. Ingeniería y Ciencia, 7(14), 83-103
  21. García, P., y Sancho, J. (2010). Estimación de densidad de probabilidad mediante ventanas de Parzen. Jornadas de introducción a la investigación de la UPCT, 3, 68-70. Recuperado de: https://dialnet.unirioja.es/servlet/articulo?codigo=3709476
  22. Gaspar, A., Zellermann, E., Lababidi, S., Reece, J., & Schrader, W. (2012). Characterization of Saturates, Aromatics, Resins, and Asphaltenes Heavy Crude Oil Fractions by Atmospheric Pressure Laser Ionization Fourier Transform Ion Cyclotron Resonance Mass Spectrometry. Energy & Fuels, 26, 3481-3487. doi: 10.1021/ef3001407
  23. Gestal, M. (2013). Introducción a las redes neuronales Recuperado de: https://tinyurl.com/yywe7338
  24. Guzmán, R., Ancheyta, J., Trejo, F., & Rodríguez, S. (2017). Methods for determining asphaltene stability in crude oils. Fuel, 188, 530–543. doi: 10.1016/j.fuel.2016.10.012
  25. Hannisdal, A., Hemmingsen, P.V., & Sjöblom, J. (2005). Group-Type Analysis of Heavy Crude Oils Using Vibrational Spectroscopy in Combination with Multivariate Analysis. Industrial & Engineering Chemistry Research, 44, 1349-1357. doi: 10.1021/ie0401354
  26. Hascakir, B. (2017, October 9-11). A New Approach to Determine Asphaltenes Stability [Conference presentation]. SPE Annual Technical Conference and Exhibition, San Antonio, Texas, USA. doi: 10.2118/187278-ms
  27. Kök, M. V., Karacan, Ö., & Pamir, R. (1998). Kinetic Analysis of Oxidation Behavior of Crude Oil SARA Constituents. Energy & Fuels, 12, 580-588. doi: 10.1021/ef970173i
  28. Kok, M.V., & Gul, K.G. (2013). Thermal characteristics and kinetics of crude oils and SARA fractions. Thermochimica Acta, 569, 66-70. doi: 10.1016/j.tca.2013.07.014
  29. Kök, M.V., Varfolomeev, M.A., & Nurgaliev, D.K. (2019). Determination of SARA fractions of crude oils by NMR technique. Journal of Petroleum Science and Engineering, 179, 1-6. doi: 10.1016/j.petrol.2019.04.026
  30. Lamus, C., Guzmán, A., Murcia, B., Cabanzo, R., & Mejía-Ospino, E. (2011). Uso de Análisis Multivariado En La Determinación SARA De Crudos Por Espectroscopia NIR. Revista Colombiana de Física, 43(3), 635-642.
  31. Likhatsky, V.V., & Syunyaev, R.Z. (2010). New Colloidal Stability Index for Crude Oils Based on Polarity of Crude Oil Components. Energy & Fuels, 24(12), 6483–6488. doi: 10.1021/ef101033p
  32. Liu, P., Shi, Q., Chung, K.H., Zhang, Y., Pan, N., Zhao, S., & Xu, C. (2010). Molecular Characterization of Sulfur Compounds in Venezuela Crude Oil and Its SARA Fractions by Electrospray Ionization Fourier Transform Ion Cyclotron Resonance Mass Spectrometry. Energy & Fuels, 24, 5089–5096. doi: 10.1021/ef100904k
  33. Madh, M., Kharrat, R., & Hamoule, T. (2017). Screening of inhibitors for remediation of asphaltene deposits: Experimental and modeling study. Petroleum. doi: 10.1016/j.petlm.2017.08.001.
  34. Mahmoud, M.B., & Aboujadeed, A.A. (2017). Compatibility Assessment of Crude Oil Blends Using Different Methods. Chemical Engineering Transactions, 57, 1705-1710. doi: 10.3303/CET1757285
  35. Mansoori, G. (2009). A unified perspective on the phase behaviour of petroleum fluids. International Journal Oil, Gas and Coal Technology, 2(2), 141-167
  36. Mateus, S.P., González, N., y Branch, J.W. (2014). Aplicación de Redes Neuronales Artificiales en Entornos Virtuales Inteligentes. Información Tecnológica, 25(5), 103-112. doi: 10.4067/S0718-07642014000500015
  37. Meléndez, L.V., Lache, A., Orrego-Ruiz, J.A., Pachón, Z., & Mejía-Ospino, E. (2012). Prediction of the SARA analysis of Colombian crude oils using ATR–FTIR spectroscopy and chemometric methods. Journal of Petroleum Science and Engineering, 91, 56-60. doi: 10.1016/j.petrol.2012.04.016
  38. Mohammadi, M., Khorrami, M.K., Vatani, A., Ghasemzadeh, H., Vatanparast, H., Bahramian, A., & Fallah, A. (2021). Genetic algorithm based support vector machine regression for prediction of SARA analysis in crude oil samples using ATR-FTIR spectroscopy. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 245, 118945. doi: 10.1016/j.saa.2020.118945
  39. Morantes, L.R., Percebom, A.M., & Mejía-Ospino, E. (2019). On the molecular basis of aggregation and stability of Colombian asphaltenes and their subfractions. Fuel, 241, 542-549. doi: 10.1016/j.fuel.2018.12.028
  40. Pérez, M.F., Rozo, M.A., Ulloa, R., Enrique, F., & Calderón, Z. (2002). Aplicación de las redes neuronales al estudio de yacimientos de petróleo. Fuentes, el reventón energético, 2(2), 76-90
  41. Prakoso, A., Punase, A., & Klock, K. (2016). Determination of the Stability of Asphaltenes Through Physicochemical Characterization of Asphaltenes. [Conference presentation]. SPE Annual Technical Conference and Exhibition, Anchorage, Alaska, USA. doi: 10.2118/180422-MS
  42. Punase, A., & Hascakir, B. (2017). Stability Determination of Asphaltenes through Dielectric Constant Measurements of Polar Oil Fractions. Energy & Fuels, 31(1), 65-72. doi: 10.1021/acs.energyfuels.6b01045
  43. Recknagel, F., & Wilson, H. (2000). Elucidation and Prediction of Aquatic Ecosystems by Artificial Neuronal Networks. In Lek, S., & Guégan, J.F. (Eds.), Artificial Neuronal Networks. Environmental Science. Berlin: Springer. doi: 10.1007/978-3-642-57030-8_10
  44. Rezaee, S., Tavakkoli, M., Doherty, R., & Vargas, F.M. (2020). A new experimental method for a fast and reliable quantification of saturates, aromatics, resins, and asphaltenes in crude oils. Petroleum Science and Technology, 38(21), 955-961. doi: 10.1080/10916466.2020.1790598
  45. Riveros, L., Jaimes, B., Ranaudo, M.A., Castillo, J., & Chirinos, J. (2006). Determination of Asphaltene and Resin Content in Venezuelan Crude Oils by Using Fluorescence Spectroscopy and Partial Least Squares Regression. Energy & Fuels, 20, 227-230. doi: 10.1021/ef0501243
  46. Romero, J.F., Feitosa, F.X., Do Carmo, F.R., & De Sant’Ana, H.B. (2018). Paraffin effects on the stability and precipitation of crude oil asphaltenes: Experimental onset determination and phase behavior approach. Fluid Phase Equilibria, 474, 116-125. doi: 10.1016/j.fluid.2018.07.017
  47. Safaie, K., & Naza, A. (2014). Evaluation of Asphaltene Inhibitors Effect on Aggregation Coupled Sedimentation Process. Journal of Dispersion Science and Technology, 35(3), 329-337
  48. Sánchez-Minero, F., Ancheyta, J., Silva-Oliver, G., & Flores-Valle, S. (2013). Predicting SARA composition of crude oil by means of NMR. Fuel, 110, 318-321.doi: 10.1016/j.fuel.2012.10.027
  49. Santos, D.C., Filipakis, S.D., Rolemberg, M.P., Lima, E.R.A., Paredes, M.L.L. (2017). Asphaltene flocculation parameter in Brazilian crude oils and synthetic polar and nonpolar mixtures: Experimental and modeling. Fuel, 199, 606–615.doi: 10.1016/j.fuel.2017.03.024
  50. Santos, J.M., Vetere, A., Wisniewski, A., Eberlin, M.N., & Schrader, W. (2020). Modified SARA Method to Unravel the Complexity of Resin Fraction(s) in Crude Oil. Energy & Fuels, 34(12), 16006–16013. doi: 10.1021/acs.energyfuels.0c02833
  51. Sepúlveda, J., Bonilla, J., y Medina, Y. (2010). Predicción de la Estabilidad de los Asfaltenos Mediante la Utilización del Análisis SARA para Petróleos Puros. Revista Ingeniería y Región, 7(1), 103-110.
  52. Solaimany, A.R., & Bayandory, L. (2008). Investigation of Asphaltene Stability in the Iranian Crude Oils. Iranian Journal of Chemical Engineering, 5(1), 3-12.
  53. Speight, J.G. (2004). Petroleum Asphaltenes Part 1. Asphaltenes, Resins and the Structure of Petroleum. Oil & Gas Science and Technology – Rev. IFP, 59(5), 467-477.
  54. Sulaimon, A.A., & Govindasamy, K. (2015, October 20-22). New Correlation for Predicting Asphaltene Deposition [Conference presentation]. SPE/IATMI Asia Pacific Oil & Gas Conference and Exhibition, Nusa Dua, Bali, Indonesia. doi: 10.2118/176436-ms
  55. Sulaimon, A.A., De Castro, J.K.M., & Vatsa, S. (2019). New correlations and deposition envelopes for predicting asphaltene stability in crude oils. Journal of Petroleum Science and Engineering, 106782. doi: 10.1016/j.petrol.2019.106782.
  56. Syunyaev, R.Z., & Likhatsky, V.V. (2010). Effects of Temperature and Pressure on the Phase State of Oils and Asphaltene Solutions Observed Using Dielectric Spectroscopy. Energy & Fuels, 24, 2233–2239. doi: 10.1021/ef900819p
  57. Tatar, A., Shokrollahi, A., Halali, M.A., Azari, V., & Safari, H. (2015). A Hybrid Intelligent Computational Scheme for Determination of Refractive Index of Crude Oil Using SARA Fraction Analysis. The Canadian Journal of Chemical Engineering, 93, 1547–1555. doi: 10.1002/cjce.22257
  58. Torkaman, M., Bahrami, M., & Dehghani, M. (2017). Influence of Temperature on Aggregation and Stability of Asphaltenes. I. Perikinetic Aggregation. Energy & Fuels, 31(10), 11169-11180. doi: 10.1021/acs.energyfuels.7b00417
  59. Torkaman, M., Bahrami, M., & Dehghani, M.R. (2018). Influence of Temperature on Aggregation and Stability of Asphaltenes: II. Orthokinetic Aggregation. Energy & Fuels, 32(5), 6144-6154. doi: 10.1021/acs.energyfuels.7b03601
  60. Torres-Faurrieta, L.K., Dreyfus-León, M.J., & Rivas, D. (2016). Recruitment forecasting of yellowfin tuna in the eastern Pacific Ocean with artificial neuronal networks. Ecological Informatics, 36, 106–113. doi: 10.1016/j.ecoinf.2016.10.005
  61. Villada, F., Arroyave, D., & Villada, M. (2014). Pronóstico del Precio del Petróleo mediante Redes Neuronales Artificiales. Información tecnológica, 25(3), 145-154. doi: 10.4067/S0718-07642014000300017
  62. Wei, B., Zou, P., Shang, J., Gao, K., Li, Y., Sun, L., & Pu, W. (2018). Integrative determination of the interactions between SARA fractions of an extra-heavy crude oil during combustion. Fuel, 234, 850-857. doi: 10.1016/j.fuel.2018.07.127
  63. Xiong, R., Guo, J., Kiyingi, W., Feng, H., Sun, T., Yang, X., & Li, Q. (2020). Method for Judging the Stability of Asphaltenes in Crude Oil. ACS Omega, 5, 21420−2142. doi: 10.1021/acsomega.0c01779
  64. Yuan, C-D., Varfolomeev, M.A., Emelianov, D.A., Eskin, A.A., Nagrimanov, R.N., Kok, M.V., Afanasiev, I.S., Fedorchenko, G.D., & Kopylova, E.V. (2017). Oxidation Behavior of Light Crude Oil and Its SARA Fractions Characterized by TG and DSC Techniques: Differences and Connections. Energy & Fuels, 32(1), 801–808. doi: 10.1021/acs.energyfuels.7b02377
  65. Zhao, S., Pu, W., Sun, B., Gu, F., & Wang, L. (2019a). Comparative evaluation on the thermal behaviors and kinetics of combustion of heavy crude oil and its SARA fractions. Fuel, 239, 117-125. doi: 10.1016/j.fuel.2018.11.014
  66. Zhao, S., Pu, W., Yuan, C., Peng, X., Zhang, J., Wang, L., & Emelianov, D.A. (2019b). Thermal Behavior and Kinetic Triplets of Heavy Crude Oil and Its SARA Fractions during Combustion by High-Pressure Differential Scanning Calorimetry. Energy & Fuels, 33(4), 3176−3186.doi: 10.1021/acs.energyfuels.9b00399
  67. Zheng, F., Shi, Q., Salvato, G., Giusti, P., & Bouyssiere, B. (2020). Fractionation and Characterization of Petroleum Asphaltene: Focus on Metalopetroleomics. Processes, 8, 1504-1535. doi: 10.3390/pr8111504