Predicting permeability changes with injecting CO₂ in coal seams during CO₂ geological sequestration: A comparative...

CO₂ geological sequestration and enhanced coal bed methane extraction is a significant CO₂ utilization approach with dual-meaning of energy and environment, and coal permeability is considered as one of the critical parameters for evaluating this method. To better predict permeability changes with injecting CO₂ in coal seams, six SVM-based hybrid models integrating support vector machine (SVM) with intelligent optimization algorithms are proposed and compared, SVM is used for the relationship modelling between CO₂ permeability and its influencing variables, and six intelligent optimization algorithms, including artificial bee colony (ABC), cuckoo search (CS), particle swarm optimization (PSO), differential evolution (DE), gray wolf optimizer (GWO), DE-GWO, are used for the hyper-parameters tuning. A total of 125 data samples for CO₂ permeability are retrieved from the reported studies to train and verify the proposed models. The input variables for the predictive models include CO₂ injection pressure, effective stress, temperature, buried depth and coal rank, and the corresponding output variable is CO₂ permeability. The predictive model performance is evaluated and compared by correlation coefficient (R), root mean square error (RMSE) and mean absolute error (MAE). The predictive results denote that the prediction performance of the six hybrid models from high to low is DEGWO-SVM, GWO-SVM, PSO-SVM, CS-SVM, DE-SVM, ABC-SVM, and the DEGWO-SVM hybrid model is recommended to predict permeability changes with injecting CO₂ in coal seams. At the same time, the mean impact value (MIV) is used to investigate the relative importance of each input variable. The relative importance scores of CO₂ injection pressure, effective stress, temperature, buried depth and coal rank are 0.0248, 0.4617, 0.0211, 0.1102, and 0.3822, respectively. The research results have important guiding significance for CO₂ permeability prediction and CO₂ sequestration in coal seams.