Coal permeability alteration prediction during CO2 geological sequestration in coal seams: a novel hybrid artificial intelligence approach

The technology of CO2 geological storage while enhancing coalbed methane recovery has attracted significant attention. In this technology, the permeability alteration of coal after the injection of CO2 is directly related to the injectability of CO2, which is known as the CO2 geological storage effect. Currently, laboratory tests are often carried out to obtain coal permeability, however, this method has several drawbacks due to the complicated testing procedure, high cost and complex sample preparation. To efficiently forecast the permeability alteration of coal in the process of CO2 geological storage, this paper proposes an integrated new hybrid intelligent model of firefly algorithm (FA), sparrow search algorithm (SSA), and support vector machines (SVM). This FA-SSA-SVM hybrid intelligent model is trained and tested by a total of 154 data samples retrieved from the literature. The input variables include CO2 injection pressure, effective stress, coal rank, coal temperature and coal seam buried depth. The output variable is coal permeability. The evaluation indicators are R, MAE, RMSE, and MAPE. The results show that the FA-SSA-SVM prediction model have good potential for predicting the permeability alteration of coal during CO2 geological storage. In addition, by comparing and analysing the evaluation indicators among the FA-SSA-SVM, SSA-SVM and L-MRA models, the model FA-SSA-SVM shows the highest accuracy, while the L-MRA model has the lowest accuracy. These research results can provide important guidance for promoting and applying CO2 storage technology in coal seams.