Modeling crude oil pyrolysis process using advanced white-box and black-box machine learning techniques
Hadavimoghaddam F. Rozhenko A. Mohammadi M.-R. Mostajeran Gortani M. Pourafshary P. Hemmati-Sarapardeh A.
December 2023Nature Research
Scientific Reports
2023#13Issue 1
Accurate prediction of fuel deposition during crude oil pyrolysis is pivotal for sustaining the combustion front and ensuring the effectiveness of in-situ combustion enhanced oil recovery (ISC EOR). Employing 2071 experimental TGA datasets from 13 diverse crude oil samples extracted from the literature, this study sought to precisely model crude oil pyrolysis. A suite of robust machine learning techniques, encompassing three black-box approaches (Categorical Gradient Boosting—CatBoost, Gaussian Process Regression—GPR, Extreme Gradient Boosting—XGBoost), and a white-box approach (Genetic Programming—GP), was employed to estimate crude oil residue at varying temperature intervals during TGA runs. Notably, the XGBoost model emerged as the most accurate, boasting a mean absolute percentage error (MAPE) of 0.7796% and a determination coefficient (R2) of 0.9999. Subsequently, the GPR, CatBoost, and GP models demonstrated commendable performance. The GP model, while displaying slightly higher error in comparison to the black-box models, yielded acceptable results and proved suitable for swift estimation of crude oil residue during pyrolysis. Furthermore, a sensitivity analysis was conducted to reveal the varying influence of input parameters on residual crude oil during pyrolysis. Among the inputs, temperature and asphaltenes were identified as the most influential factors in the crude oil pyrolysis process. Higher temperatures and oil °API gravity were associated with a negative impact, leading to a decrease in fuel deposition. On the other hand, increased values of asphaltenes, resins, and heating rates showed a positive impact, resulting in an increase in fuel deposition. These findings underscore the importance of precise modeling for fuel deposition during crude oil pyrolysis, offering insights that can significantly benefit ISC EOR practices.
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Key Laboratory of Continental Shale Hydrocarbon Accumulation and Efficient Development, Ministry of Education, Northeast Petroleum University, Daqing, 163318, China
Ufa State Petroleum Technological University, Ufa, 450064, Russian Federation
Plekhanov Russian University of Economics, Moscow, 117997, Russian Federation
Department of Petroleum Engineering, Shahid Bahonar University of Kerman, Kerman, Iran
National Iranian Oil Company, Tehran, Iran
School of Mining and Geosciences, Nazarbayev University, Astana, Kazakhstan
State Key Laboratory of Petroleum Resources and Prospecting, China University of Petroleum (Beijing), Beijing, China
Key Laboratory of Continental Shale Hydrocarbon Accumulation and Efficient Development
Ufa State Petroleum Technological University
Plekhanov Russian University of Economics
Department of Petroleum Engineering
National Iranian Oil Company
School of Mining and Geosciences
State Key Laboratory of Petroleum Resources and Prospecting
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