Research on Export Oil and Gas Concentration Prediction Based on Machine Learning Methods

Xiaochuan Wang, Baikang Zhu, Huajun Zheng, Jiaqi Wang, Zhiwei Chen () and Bingyuan Hong ()
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Xiaochuan Wang: National & Local Joint Engineering Research Center of Harbor Oil & Gas Storage and Transportation Technology, Zhejiang Key Laboratory of Petrochemical Environmental Pollution Control, School of Petrochemical Engineering & Environment, Zhejiang Ocean University, Zhoushan 316022, China
Baikang Zhu: National & Local Joint Engineering Research Center of Harbor Oil & Gas Storage and Transportation Technology, Zhejiang Key Laboratory of Petrochemical Environmental Pollution Control, School of Petrochemical Engineering & Environment, Zhejiang Ocean University, Zhoushan 316022, China
Huajun Zheng: Zhejiang Oil Storage and Transportation Co., Ltd., Hangzhou 311227, China
Jiaqi Wang: Zhejiang Oil Storage and Transportation Co., Ltd., Hangzhou 311227, China
Zhiwei Chen: National & Local Joint Engineering Research Center of Harbor Oil & Gas Storage and Transportation Technology, Zhejiang Key Laboratory of Petrochemical Environmental Pollution Control, School of Petrochemical Engineering & Environment, Zhejiang Ocean University, Zhoushan 316022, China
Bingyuan Hong: National & Local Joint Engineering Research Center of Harbor Oil & Gas Storage and Transportation Technology, Zhejiang Key Laboratory of Petrochemical Environmental Pollution Control, School of Petrochemical Engineering & Environment, Zhejiang Ocean University, Zhoushan 316022, China

Energies, 2025, vol. 18, issue 5, 1-18

Abstract: With the oil industry’s increasing focus on environmental protection and the growing implementation of oil and gas recovery devices in depots, it is crucial to investigate the outlet concentrations of oil and gas from these devices. This research aims to reduce energy consumption while enhancing the efficiency of oil and gas recovery processes. This paper investigates the prediction of outlet oil and gas concentration based on the process parameters of oil and gas recovery devices in oil depots. This study employs both regression and classification machine learning models. Most regression models achieve a goodness-of-fit of approximately 0.9 and an accuracy error of about 30%. Additionally, most classification models attain over 90% accuracy, with predictions of high oil and gas concentrations reaching up to 84.5% accuracy. Both models demonstrate that the Random Forest method is more effective in predicting the exported oil and gas concentration with multiple-parameter inputs, providing a relevant basis for subsequent control of exported oil and gas concentration.

Keywords: oil and gas recovery; concentration; prediction; correlation analysis; machine learning method (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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