Machine learning framework for estimating CO2 adsorption on coalbed for carbon capture, utilization, and storage applications

For the purpose of carbon capture, utilization, and storage, carbon dioxide (CO2) injection in coal formations can enhance methane recovery and mitigate climate change. However, measuring CO2 adsorption isotherms using experimental or mathematical models can be time-consuming, expensive, and inaccurate. Thus, this study presents a machine-learning framework that predicts CO2 adsorption in coal formations based on various coal properties and testing conditions. Machine-learning (ML) framework was applied using a dataset of 1,064 points collected for different coal samples at different operating conditions to predict the CO2 adsorption in coal surface. The ML techniques include decision tree regression (DT), random forests (RF), gradient boost regression (GBR), K-nearest neighbor (KNN), artificial neural network (ANN), function network (FN), and adaptive neuro-fuzzy inference system (ANFIS). The applied framework determines CO2 adsorption as a function of coal's physical and chemical properties (moisture, ash, volatile matter, and fixed carbon content), the vitrinite reflectance of the coal samples, and testing conditions (pressure and temperature). Classical statical tools such as R2, root mean square error (RMSE), and average absolute percentage error (AAPE) were used to evaluate the model's performance analysis. The results demonstrated the ability to determine CO2 adsorption for varying coal types and at different temperature and pressure conditions. The statistical measures suggested that RF, GBR, and KNN are very reliable ML models, with RF being the best. At low operating pressure (P < 4 MPa), CO2 adsorption is impacted by any pressure changes, while it is stabilized at high-pressure values and becomes more dependent on the rock properties at high operating pressure (P > 4 MPa). The introduced ML framework offers a technique to evaluate the capability of different algorithms and accurately estimate CO2 adsorption without the requirement of additional experimental measurements or complicated mathematical techniques.