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Prediction Model for the Viscosity of Heavy Oil Diluted with Light Oil Using Machine Learning Techniques

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  • Xiaodong Gao

    (College of Petroleum Engineering, China University of Petroleum (Beijing), Beijing 102200, China
    State Key Laboratory of Petroleum Resources and Prospecting, China University of Petroleum (Beijing), Beijing 102200, China)

  • Pingchuan Dong

    (College of Petroleum Engineering, China University of Petroleum (Beijing), Beijing 102200, China
    State Key Laboratory of Petroleum Resources and Prospecting, China University of Petroleum (Beijing), Beijing 102200, China)

  • Jiawei Cui

    (College of Petroleum Engineering, China University of Petroleum (Beijing), Beijing 102200, China
    State Key Laboratory of Petroleum Resources and Prospecting, China University of Petroleum (Beijing), Beijing 102200, China)

  • Qichao Gao

    (Sinopec Research Institute of Petroleum Engineering, Beijing 100083, China)

Abstract

Due to the presence of asphaltene, the flow assurance of high viscosity crude oil becomes more challenging and costly to produce in wellbores and pipelines. One of the most effective ways to reduce viscosity is to blend heavy oil with light oil. However, the viscosity measurement of diluted heavy crude is either time-consuming or inaccurate. This work aims to develop a more accurate viscosity model of diluted heavy crude based on machine learning techniques. A multilayer neural network is used to predict the viscosity of heavy oil diluted with lighter oil. The input data used in the training include temperature, light oil viscosity, heavy oil viscosity, and dilution ratio. In this modeling process, 156 datasets were retrieved from the available iterature of various heavy-oil fields in China. Part of the data (80%) is used to train the developed models using Adam optimizer algorithms, while the other part of the data (20%) is used to predict the viscosity of heavy oil diluted with lighter. The performance and accuracy of the machine learning models were tested and compared with the existing viscosity models. It was found that the new model can predict the viscosity of diluted heavy oil with higher accuracy, and it performs better than other models. The absolute average relative error is 10.44%, the standard deviation of the relative error is 8.45%, and the coefficient of determination is R 2 = 0.95. The viscosity predicted by the neural network outperformed existing correlations by the statistical analysis used for the datasets available in the literature. Therefore, the method proposed in this paper can better estimate the viscosity of diluted heavy crude oil and has important promotion value.

Suggested Citation

  • Xiaodong Gao & Pingchuan Dong & Jiawei Cui & Qichao Gao, 2022. "Prediction Model for the Viscosity of Heavy Oil Diluted with Light Oil Using Machine Learning Techniques," Energies, MDPI, vol. 15(6), pages 1-15, March.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:6:p:2297-:d:776241
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    References listed on IDEAS

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    1. Piotr Wróblewski & Wojciech Lewicki, 2021. "A Method of Analyzing the Residual Values of Low-Emission Vehicles Based on a Selected Expert Method Taking into Account Stochastic Operational Parameters," Energies, MDPI, vol. 14(21), pages 1-24, October.
    2. Olalekan Alade & Dhafer Al Shehri & Mohamed Mahmoud & Kyuro Sasaki, 2019. "Viscosity–Temperature–Pressure Relationship of Extra-Heavy Oil (Bitumen): Empirical Modelling versus Artificial Neural Network (ANN)," Energies, MDPI, vol. 12(12), pages 1-13, June.
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