On the Evaluation of Coal Strength Alteration Induced by CO2 Injection Using Advanced Black-Box and White-Box Machine Learning Algorithms

The injection of carbon dioxide (CO2) into coal seams is a prominent technique that can provide carbon sequestration in addition to enhancing coalbed methane extraction. However, CO2 injection into the coal seams can alter the coal strength properties and their long-term integrity. In this work, the strength alteration of coals induced by CO2 exposure was modeled using 147 laboratory-measured unconfined compressive strength (UCS) data points and considering CO2 saturation pressure, CO2 interaction temperature, CO2 interaction time, and coal rank as input variables. Advanced white-box and black-box machine learning algorithms including Gaussian process regression (GPR) with rational quadratic kernel, extreme gradient boosting (XGBoost), categorical boosting (CatBoost), adaptive boosting decision tree (AdaBoost-DT), multivariate adaptive regression splines (MARS), K-nearest neighbor (KNN), gene expression programming (GEP), and group method of data handling (GMDH) were used in the modeling process. The results demonstrated that GPR-Rational Quadratic provided the most accurate estimates of UCS of coals having 3.53%, 3.62%, and 3.55% for the average absolute percent relative error (AAPRE) values of the train, test, and total data sets, respectively. Also, the overall determination coefficient (R2) value of 0.9979 was additional proof of the excellent accuracy of this model compared with other models. Moreover, the first mathematical correlations to estimate the change in coal strength induced by CO2 exposure were established in this work by the GMDH and GEP algorithms with acceptable accuracy. Sensitivity analysis revealed that the Spearman correlation coefficient shows the relative importance of the input parameters on the coal strength better than the Pearson correlation coefficient. Among the inputs, coal rank had the greatest influence on the coal strength (strong nonlinear relationship) based on the Spearman correlation coefficient. After that, CO2 interaction time and CO2 saturation pressure have shown relatively strong nonlinear relationships with model output, respectively. The CO2 interaction temperature had the smallest impact on coal strength alteration induced by CO2 exposure based on both Pearson and Spearman correlation coefficients. Finally, the leverage technique revealed that the laboratory database used for modeling CO2-induced strength alteration of coals was highly reliable, and the suggested GPR-Rational Quadratic model and GMDH correlation could be applied for predicting the UCS of coals exposed to CO2 with high statistical accuracy and reliability.