Prediction of wax disappearance temperature: a review
Predição da temperatura de desaparecimento de parafina: uma revisão
DOI:
https://doi.org/10.53660/CLM-3218-24G10Palavras-chave:
Wax deposition, Artificial Neural Network, Wax disappearance temperatureResumo
Wax deposition in pipelines is a recurring problem in the oil industry. Therefore, several studies have analyzed the phenomenon and predicted the wax disappearance temperature (WDT). This variable represents the exact solid-liquid equilibrium point. Such information is an effective aid in decision-making regarding pipelines and production facilities. However, the study of paraffin deposition is highly dependent on conducting experiments, which are typically costly and can make this type of analysis impractical. The increasing progress of models based on machine learning techniques has been an alternative to experimental and thermodynamic methods to predict this phenomenon. In the present study, a bibliographic review of the main authors on models for predicting kerosene deposition has been compiled.
Downloads
Referências
AFTAB, S.; JAVANMARDI, J.; NASRIFAR, K. Experimental investigation and thermodynamic modeling of wax disappearance temperature for n-undecane+ n-hexadecane+ n-octadecane and n-tetradecane+ n-hexadecane+ n-octadecane ternary systems. Fluid Phase Equilibria, v. 403, p. 70-77, 2015.
ALVES, K. C. M., Intensificação do Processo de Cristalização de Parafina por Ultra-Som, 1999, Dissertação (Mestrado) – Centro de Ciências Exatas e de Tecnologia, Universidade Federal de São Carlos, São Carlos, SP, Brasil.
CBIE. Quais as diferenças entre os tipos de produção de petróleo? Centro Brasileiro de Infraestrutura, 03 abr. 2020. Disponível em: https://cbie.com.br/artigos/quais-as-diferencas-entre-os-tipos-de-producao-de-petroleo/. Acesso em 30 jul. 2022.
BALABIN, Roman M.; LOMAKINA, Ekaterina I. Neural network approach to quantum-chemistry data: Accurate prediction of density functional theory energies. The journal of chemical physics, v. 131, n. 7, p. 074104, 2009.
BELL, Elijah et al. Thermal methods in flow assurance: A review. Journal of Natural Gas Science and Engineering, v. 88, p. 103798, 2021.
BENAMARA, Chahrazed et al. Modeling wax disappearance temperature using advanced intelligent frameworks. Energy & Fuels, v. 33, n. 11, p. 10959-10968, 2019.
BHAT, Nitin V.; MEHROTRA, Anil K. Measurement and prediction of the phase behavior of wax− solvent mixtures: Significance of the wax dis appearance temperature. Industrial & engineering chemistry research, v. 43, n. 13, p. 3451-3461, 2004.
BROWN, T. S.; NIESEN, V. G.; ERICKSON, D. D. Measurement and prediction of the kinetics of paraffin deposition. Journal of Petroleum Technology, v. 47, n. 4, p. 328-329, 1995.
DARIDON, Jean-Luc; PAULY, Jérôme; MILHET, Michel. High pressure solid–liquid phase equilibria in synthetic waxes. Physical Chemistry Chemical Physics, v. 4, n. 18, p. 4458-4461, 2002.
DAUPHIN, C. et al. Wax content measurements in partially frozen paraffinic systems. Fluid Phase Equilibria, v. 161, n. 1, p. 135-151, 1999.
DEHAGHANI, Saeedi Amir Hossein. An intelligent model for predicting wax deposition thickness during turbulent flow of oil. Petroleum Science and Technology, v. 35, n. 16, p. 1706-1711, 2017.
EGHTEDAEI, Reza et al. Estimation of wax deposition in the oil production units using RBF-ANN strategy. Petroleum Science and Technology, v. 35, n. 17, p. 1737-1742, 2017.
FOGLER, H. Scott; HUANG, Zhenyu; ZHENG, Sheng. Wax deposition: experimental characterizations, theoretical modeling, and field practices. 2016.
FOGLER, H. Scott; HUANG, Zhenyu; ZHENG, Sheng. A fundamental model of wax deposition in subsea oil pipelines. AIChE Journal, v. 57, n. 11, p. 2955-2964, 2011.
FOGLER, H. S; Singh, P.; Venkatesan, R.; & Nagarajan, N. (2000). Formation and aging of incipient thin film wax‐oil gels. AIChE journal, 46(5), 1059-1074.
GHANAEI, E.; ESMAEILZADEH, F.; KALJAHI, J. Fathi. A new predictive thermodynamic model in the wax formation phenomena at high pressure condition. Fluid phase equilibria, v. 254, n. 1-2, p. 126-137, 2007.
GHANAEI, Ehsan; ESMAEILZADEH, Feridun; FATHIKALAJAHI, Jamshid. High pressure phase equilibrium of wax: A new thermodynamic model. Fuel, v. 117, p. 900-909, 2014.
HAMMAMI, Ahmed; RAINES, M. A. Paraffin deposition from crude oils: Comparison of laboratory results with field data. SPE Journal, v. 4, n. 01, p. 9-18, 1999.
HAYKIN, S. O.; NEURAIS, Redes. Princípios e Práticas-2ª. Edição (reimpressão). 2008.
JALALNEZHAD, Mohammad Javad; KAMALI, Vahid. Development of an intelligent model for wax deposition in oil pipeline. Journal of petroleum exploration and production technology, v. 6, n. 1, p. 129-133, 2016.
JAVADIAN, Hamedreza et al. Using fuzzy inference system to predict Pb (II) removal from aqueous solutions by magnetic Fe3O4/H2SO4-activated Myrtus Communis leaves carbon nanocomposite. Journal of the Taiwan Institute of Chemical Engineers, v. 91, p. 186-199, 2018.
JI, Hong-Yan et al. Wax phase equilibria: developing a thermodynamic model using a systematic approach. Fluid Phase Equilibria, v. 216, n. 2, p. 201-217, 2004.
KAMARI, Arash et al. A reliable model for estimating the wax deposition rate during crude oil production and processing. Petroleum Science and Technology, v. 32, n. 23, p. 2837-2844, 2014.
KAMARI, Arash et al. Evaluation of wax disappearance temperatures in hydrocarbon fluids using soft computing approaches. Petroleum Science and Technology, v. 37, n. 7, p. 829-836, 2019.
KELECHUKWU, Ekeh Modesty; AL-SALIM, Hikmat Said; SAADI, Ahmmed. Prediction of wax deposition problems of hydrocarbon production system. Journal of Petroleum Science and Engineering, v. 108, p. 128-136, 2013.
MAJEED, A.; BRINGEDAL, B.; OVERA, S. Model calculates wax deposition for N. Sea oils. Oil & Gas Journal, v. 88, n. 25, p. 63-69, 1990.
MANSOURPOOR, M. et al. Experimental measurement and modeling study for estimation of wax disappearance temperature. Journal of Dispersion Science and Technology, v. 40, n. 2, p. 161-170, 2019.
MANSHAD, K. A. et al. The prediction of wax precipitation by neural network and genetic algorithm and comparison with a multisolid model in crude oil systems. Petroleum science and technology, v. 30, n. 13, p. 1369-1378, 2012.
MENAD, Nait Amar; NOUREDDINE, Zeraibi. An efficient methodology for multi-objective optimization of water alternating CO2 EOR process. Journal of the Taiwan Institute of Chemical Engineers, v. 99, p. 154-165, 2019.
MENAD, Nait Amar et al. Modeling temperature dependency of oil-water relative permeability in thermal enhanced oil recovery processes using group method of data handling and gene expression programming. Engineering Applications of Computational Fluid Mechanics, v. 13, n. 1, p. 724-743, 2019.
MENAD, Nait Amar et al. Predicting solubility of CO2 in brine by advanced machine learning systems: Application to carbon capture and sequestration. Journal of CO2 Utilization, v. 33, p. 83-95, 2019.
MILHET, Michel et al. Liquid–solid equilibria under high pressure of tetradecane + pentadecane and tetradecane + hexadecane binary systems. Fluid Phase Equilibria, v. 235, n. 2, p. 173-181, 2005.
MORADI, Gholamreza; MOHADESI, Majid; MORADI, Mohammad Reza. Prediction of wax disappearance temperature using artificial neural networks. Journal of Petroleum Science and Engineering, v. 108, p. 74-81, 2013.
OBANIJESU, E. O.; OMIDIORA, E. O. Artificial neural network's prediction of wax deposition potential of Nigerian crude oil for pipeline Safety. Petroleum Science and Technology, v. 26, n. 16, p. 1977-1991, 2008.
PAULY, Jérôme; COUTINHO, Joao; DARIDON, Jean-Luc. High pressure phase equilibria in methane+ waxy systems: 1. Methane+ heptadecane. Fluid phase equilibria, v. 255, n. 2, p. 193-199, 2007.
PAULY, Jérôme; COUTINHO, João AP; DARIDON, Jean-Luc. High pressure phase equilibria in methane+ waxy systems. 2. Methane+ waxy ternary mixture. Fluid phase equilibria, v. 297, n. 1, p. 149-153, 2010.
PAULY, Jérôme; COUTINHO, João AP; DARIDON, Jean-Luc. High pressure phase equilibria in methane+ waxy systems. 3. Methane+ a synthetic distribution of paraffin ranging from n-C13 to n-C22. Fluid phase equilibria, v. 313, p. 32-37, 2012.
RAHIMPOUR, M. R. et al. Wax formation assessment of condensate in South Pars gas processing plant sea pipeline (a case study). Journal of Natural Gas Science and Engineering, v. 10, p. 25-40, 2013.
REISTLE, Carl Ernest. Paraffin and congealing-oil problems. US Government Printing Office, 1932.
VAZ, Célio Eduardo Martins; MARTINS, Célio Eduardo; DOS MAIA, Santos Walmir Gomes. Tecnologia da indústria de gás natural. Edgard Blucher, 2008.
WARDAUGH, L. T.; BOGER, D. V. The measurement and description of the yielding behaviour of waxy crude oils. J. Rheol, v. 35, n. 6, p. 1121-1156, 1991.
XIE, Ying; XING, Yu. A prediction method for the wax deposition rate based on a radial basis function neural network. Petroleum, v. 3, n. 2, p. 237-241, 2017.
ZHANG, Wan et al. Comprehensive overview on computational intelligence techniques for machinery condition monitoring and fault diagnosis. Chinese Journal of Mechanical Engineering, v. 30, n. 4, p. 782-795, 2017.
