Journal of Oil, Gas and Petrochemical Technology

Journal of Oil, Gas and Petrochemical Technology

Experimental Investigation and Prediction of Pour Point Using Artificial Intelligence

Document Type : Research Paper

Authors
Maryam Mahmoudi Kouhi 1
Elnaz Khodapanah 2
1 MSc Petroleum Engineering, Faculty of Petroleum and Natural Gas Engineering, Sahand University of Technology
2 Associate Professor of Petroleum Engineering, Faculty of Petroleum and Natural Gas Engineering, Sahand University of Technology, Sahand New Town, Tabriz, Iran, P.O.Box: 5331817634
10.22034/jogpt.2023.268812.1085
Abstract
Pour point as an important physical property of crude oil is a measure of its low temperature fluidity. The accurate determination of this property is of significance as the temperature decrease below the pour point of the crude oil causes severe production and transportation problems. In this study, for the first time, two types of artificial neural networks (ANNs), including multilayer perceptron (MLP) and radial basis function (RBF), were proposed to predict the pour point. First, the MLP network was modeled and evaluated using different methods. To this end, the optimal number of the input parameters and the best activation function were examined. The results showed that the best predictive MLP network model is constructed using two parameters of wax content and cloud point and SoftMax-SoftMax activation function. The regression of training, validation, and testing datasets of the constructed MLP were 79.5%, 74.1%, and 76.8%, respectively. To validate the constructed MLP-ANN, some experiments were conducted on an oil sample. The dataset obtained from the experiments was then used to predict the pour point. The absolute error of 0.94 °C indicated the great performance of the MLP-ANN in predicting the pour point. Finally, the prediction performance of the MLP-ANN was compared to the RBF-ANN. The results showed the higher predictive accuracy of the MLP-ANN in comparison with the RBF-ANN. Based on the obtained results, the proposed MLP-ANN can be used with confidence in lieu of the expensive and time-consuming laboratory measurements to determine the pour point of crude oils.
Keywords
Pour point
Multilayer perceptron neural network
Radial basis neural network
Wax content
Cloud point
[1]           O. Emmanuel, C. Wisdom, and O. Moses, "Determination of Cloud and Pour Point of Crude Oil with Reference to Crude Transportation."
[2]           T. J. Behbahani, A. A. M. Beigi, Z. Taheri, and B. Ghanbari, "Investigation of wax precipitation in crude oil: Experimental and modeling," Petroleum, vol. 1, no. 3, pp. 223-230, 2015.
[3]           A. S. Kasumu, "An Investigation of Solids Deposition from Two-Phase Wax–Solvent–Water Mixtures," doctoral thesis, University of Calgary, http://dx.doi.org/10.11575/PRISM/24916, 2014.
[4]           M. F. A. Zolkifli, "Crude Oil Pour Point Measurement By Using Rotational Method," Universiti Teknologi PETRONAS, URI:       http://utpedia.utp.edu.my/id/eprint/13887, 2014.
[5]           Y. K. Anaya Jaimes, "Flow assurance of waxy crude oil= physical and chemical characterization, critical temperatures and rheological behavior for the restart problem-experimental study= Garantia de escoamento de óleos parafínicos: caracterização físico-química, temperaturas críticas e comportamento reológico em problemas de repartida-estudo experimental," 2016.
[6]           F. D. Santos et al., "Improvement on pour point of heavy oils by adding organic solvents," Revista Virtual de Química, vol. 9, no. 6, 2017.
[7]           J. Taheri-Shakib, A. Shekarifard, and H. Naderi, "Characterization of the wax precipitation in Iranian crude oil based on Wax Appearance Temperature (WAT): Part 1. The influence of electromagnetic waves," Journal of Petroleum Science and Engineering, vol. 161, pp. 530-540, 2018.
[8]           A. Aiyejina, D. P. Chakrabarti, A. Pilgrim, and M. Sastry, "Wax formation in oil pipelines: A critical review," International journal of multiphase flow, vol. 37, no. 7, pp. 671-694, 2011.
[9]           M. Al-Marhoun, S. Ali, A. Abdulraheem, S. Nizamuddin, and A. Muhammadain, "Prediction of bubble point pressure from composition of black oils using artificial neural network," Petroleum science and technology, vol. 32, no. 14, pp. 1720-1728, 2014.
[10]         F. Alnaimat, M. Ziauddin, and B. Mathew, "Wax Deposition in Crude Oil Transport Lines and Wax Estimation Methods," in Intelligent System and Computing: IntechOpen, 2019.
[11]         A. Tatar, S. Naseri, N. Sirach, M. Lee, and A. Bahadori, "Prediction of reservoir brine properties using radial basis function (RBF) neural network," Petroleum, vol. 1, no. 4, pp. 349-357, 2015.
[12]         M. Narvekar and P. Fargose, "Daily weather forecasting using artificial neural network," International Journal of computer applications, vol. 121, no. 22, 2015.
[13]         Y. Wei, M. Wu, and L. Zhao, "Wax deposition rate model for crude oil pipeline based on neural network," in 2010 Sixth International Conference on Natural Computation, 2010, vol. 2, pp. 760-762: IEEE.
[14]         M. I. Jahirul, R. J. Brown, W. Senadeera, I. M. O'Hara, and Z. D. Ristovski, "The use of artificial neural networks for identifying sustainable biodiesel feedstocks," Energies, vol. 6, no. 8, pp. 3764-3806, 2013.
[15]         C. Benamara, K. Gharbi, M. N. Amar, and B. Hamada, "Prediction of Wax Appearance Temperature Using Artificial Intelligent Techniques," Arabian Journal for Science and Engineering, vol. 45, no. 2, pp. 1319-1330, 2020.
[16]         P. Chidambaram, "Development and testing of an artificial neural network based history matching protocol to characterize reservoir properties," doctoral thesis, Department of Energy and Mineral Engineering, The Pennsylvania State University, 2009.
[17]         A. H. Fath, F. Madanifar, and M. Abbasi, "Implementation of multilayer perceptron (MLP) and radial basis function (RBF) neural networks to predict solution gas-oil ratio of crude oil systems," Petroleum, vol. 6, no. 1, pp. 80-91, 2020.
[18]         Y. Xie and Y. Xing, "A prediction method for the wax deposition rate based on a radial basis function neural network," Petroleum, vol. 3, no. 2, pp. 237-241, 2017.
[19]         A. S. Maheshwari and J. G. Chellani, "Correlations for Pour Point and Cloud Point of middle and heavy distillates using density and distillation temperatures," Fuel, vol. 98, pp. 55-60, 2012.
[20]         K. Hu et al., "Application of bayesian regularized artificial neural networks to predict pour point of crude oil treated by pour point depressant," Petroleum Science and Technology, vol. 35, no. 13, pp. 1349-1354, 2017.
[21]         L. A. Alcazar-Vara and E. Buenrostro-Gonzalez, "Characterization of the wax precipitation in Mexican crude oils," Fuel processing technology, vol. 92, no. 12, pp. 2366-2374, 2011.
[22]         S. ALI, "SYNTHESIS, CHARACTERIZATION AND EVALUATION OF POUR POINT DEPRESSANTS FOR MALAYSIAN WAXY CRUDES," Universiti Teknologi PETRONAS, 2013.
[23]         S. Allenson and A. Johnston, "Comparison of different experimental techniques used for wax deposition testing," 2008.
[24]         R. Bansal, R. Burla, K. Afzal, and S. S. Sharma, "Dynamic simulation for Optimizing Pigging Frequency for Dewaxing," in SPE Oil and Gas India Conference and Exhibition, 2012: Society of Petroleum Engineers.
[25]         R. Capitello and L. Sirieix, "Consumers’ perceptions of sustainable wine: an exploratory study in France and Italy," Economies, vol. 7, no. 2, p. 33, 2019.
[26]         L. V. Castro and F. Vazquez, "Fractionation and characterization of Mexican crude oils," Energy & fuels, vol. 23, no. 3, pp. 1603-1609, 2009.
[27]         A. Gupta and A. Sircar, "Wax deposition modelling and comparison with field data for some Indian oil fields," STM. J., vol. 4, pp. 1-15, 2017.
[28]         A. Japper-Jaafar, P. Bhaskoro, and Z. Mior, "A new perspective on the measurements of wax appearance temperature: Comparison between DSC, thermomicroscopy and rheometry and the cooling rate effects," Journal of petroleum science and Engineering, vol. 147, pp. 672-681, 2016.
[29]         P. Jokuty, M. Fingas, E. Meyer, C. Knobel, and S. Whiticar, "Hydrocarbon groups and their relationships to oil properties and behaviour," in ARCTIC AND MARINE OILSPILL PROGRAM TECHNICAL SEMINAR, 1995, pp. 1-20: MINISTRY OF SUPPLY AND SERVICES, CANADA.
[30]         M. C. Khalil De Oliveira and M. A. Gonçalves, "An effort to establish correlations between Brazilian crude oils properties and flow assurance related issues," Energy & fuels, vol. 26, no. 9, pp. 5689-5701, 2012.
[31]         S. Kumar and V. Mahto, "Emulsification of Indian heavy crude oil in water for its efficient transportation through offshore pipelines," Chemical Engineering Research and Design, vol. 115, pp. 34-43, 2016.
[32]         N. Makwashi, K. Sarkodie, S. Akubo, D. Zhao, and P. Diaz, "Investigation of the Severity of Wax Deposition in Bend Pipes Under Subcooled Pipelines Conditions," in SPE Europec featured at 81st EAGE Conference and Exhibition, 2019: Society of Petroleum Engineers.
[33]         H. M. Meighani, C. Ghotbi, T. J. Behbahani, and K. Sharifi, "A new investigation of wax precipitation in Iranian crude oils: Experimental method based on FTIR spectroscopy and theoretical predictions using PC-SAFT model," Journal of Molecular Liquids, vol. 249, pp. 970-979, 2018.
[34]         P. L. Perez et al., "Mitigating wax deposition from crude oils: Correlations between physical-chemical properties of crude oils and the performance of wax inhibitors," in Offshore Technology Conference, 2016: Offshore Technology Conference.
[35]         R. Sharma, V. Mahto, and H. Vuthaluru, "Synthesis of PMMA/modified graphene oxide nanocomposite pour point depressant and its effect on the flow properties of Indian waxy crude oil," Fuel, vol. 235, pp. 1245-1259, 2019.
[36]         H. P. Soni, K. Agrawal, A. Nagar, and D. Bharambe, "Designing maleic anhydride-α-olifin copolymeric combs as wax crystal growth nucleators," Fuel Processing Technology, vol. 91, no. 9, pp. 997-1004, 2010.
[37]         P. Swivedi, C. Sarica, and W. Shang, "Experimental study on wax-deposition characteristics of a waxy crude oil under single-phase turbulent-flow conditions," Oil and Gas Facilities, vol. 2, no. 04, pp. 61-73, 2013.
[38]         M. Theyab and P. Diaz, "Experimental Study on the Effect of Polyacrylate Polymer (C16-C22) on Wax Deposition," in 7th International Conference on Chemical Engineering and Applications (CCEA 2016), 2016: London South Bank University.
[39]         M. Theyab and P. Diaz, "Experimental study of wax deposition in pipeline–effect of inhibitor and spiral flow," International Journal of Smart Grid and Clean Energy, vol. 5, no. 3, pp. 174-181, 2016.
[40]         L. C. Vieira, M. B. Buchuid, and E. F. Lucas, "Effect of pressure on the crystallization of crude oil waxes. II. Evaluation of crude oils and condensate," Energy & Fuels, vol. 24, no. 4, pp. 2213-2220, 2010.
[41]         B. Yao et al., "Organically modified nano-clay facilitates pour point depressing activity of polyoctadecylacrylate," Fuel, vol. 166, pp. 96-105, 2016.
[42]         M. Riazi, Characterization and properties of petroleum fractions. ASTM international, 2005.
[43]         T. Ahmed, Reservoir engineering handbook. Gulf professional publishing, 2018.
[44]         D. K. Gold, W. D. McCain, Jr., and J. W. Jennings, "An Improved Method for the Determination of the Reservoir-Gas Specific Gravity for Retrograde Gases (includes associated papers 20006 and 20010 )," Journal of Petroleum Technology, vol. 41, no. 07, pp. 747-752, 1989.
Journal of Oil, Gas and Petrochemical Technology
Volume 9, Number 1
May 2022
Pages 49-74