Abstract
In-situ combustion (ISC) offers a compelling solution for enhancing oil recovery, particularly for heavy crude oils. This process involves the oxidation and pyrolysis of hydrocarbons, generating heat and depositing fuel in the combustion front. In this work, the thermo-oxidative profiles and residue formation of crude oils during thermogravimetric analysis (TGA) were modeled using 3075 experimental data points from 18 crude oils with API gravities ranging from 5 to 42. Four advanced tree-based machine learning algorithms comprising gradient boosting with categorical features support (CatBoost), light gradient boosting machine (LightGBM), random forest (RF), and extreme gradient boosting (XGBoost) were utilized to develop accurate predictive models. The results indicated that CatBoost outperformed the other models, achieving mean absolute relative error (MARE) values of 4.95% for the entire dataset, 5.92% for the testing subset, and 4.71% for the training subset. Moreover, it recorded a determination coefficient (R(2)) value of 0.9993, highlighting its exceptional predictive capability. Furthermore, temperature was identified as the most influential factor affecting residual crude oil content, exhibiting a significant negative correlation, while API gravity also showed a negative impact. Conversely, asphaltene, resin, and heating rate positively correlated with residual content. Finally, the leverage method demonstrated that only 2.14% of the data were identified as suspected, with no out-of-leverage points detected, underscoring the reliability of the CatBoost model and the gathered experimental data. Effective management of fuel consumption and residue formation is crucial for maintaining the ISC process, and the CatBoost model has demonstrated strong predictive capabilities that support this objective.