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Machine learning methods for predicting CO2 solubility in hydrocarbons

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The application of carbon dioxide(CO2)in enhanced oil recovery(EOR)has increased significantly,in which CO2 solubility in oil is a key parameter in predicting CO2 flooding performance.Hydrocarbons are the major constituents of oil,thus the focus of this work lies in investigating the solubility of CO2 in hydrocarbons.However,current experimental measurements are time-consuming,and equations of state can be computationally complex.To address these challenges,we developed an artificial intelligence-based model to predict the solubility of CO2 in hydrocarbons under varying conditions of temperature,pressure,molecular weight,and density.Using experimental data from previous studies,we trained and predicted the solubility using four machine learning models:support vector regression(SVR),extreme gradient boosting(XGBoost),random forest(RF),and multilayer perceptron(MLP).Among four models,the XGBoost model has the best predictive performance,with an R2 of 0.9838.Additionally,sensitivity analysis and evaluation of the relative impacts of each input parameter indicate that the prediction of CO2 solubility in hydrocarbons is most sensitive to pressure.Furthermore,our trained model was compared with existing models,demonstrating higher accuracy and applicability of our model.The developed machine learning-based model provides a more efficient and accurate approach for predicting CO2 solubility in hydrocarbons,which may contribute to the advancement of CO2-related applications in the petroleum industry.

CO2 solubilityMachine learningSupport vector regressionExtreme gradient boostingRandom forestMulti-layer perceptron

Yi Yang、Binshan Ju、Guangzhong Lü、Yingsong Huang

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School of Energy Resources,China University of Geosciences(Beijing),Beijing,100083,China

Key Laboratory of Marine Reservoir Evolution and Hydrocarbon Enrichment Mechanism,Ministry of Education,Beijing,100083,China

Key Laboratory of Geological Evaluation and Development Engineering of Unconventional Natural Gas Energy,Beijing,100083,China

Shengli Oilfield Company,SINOPEC Dongying,257015,Shandong,China

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2024

10.1016/j.petsci.2024.04.018

石油科学(英文版)
中国石油大学（北京）

石油科学(英文版)

EI
影响因子：0.88
ISSN：1672-5107
年,卷(期)：2024.21(5)