Machine learning enhanced interpretation of wellbore data for underground storage

The Illinois Basin has an excellent history of pilot- and demonstration-scale CO₂ injection projects, positioning it as a promising potential hub for carbon capture and storage.Since the presence of fractures increases risk of an induced seismic response and CO₂ leakage, it is essential that potential injection sites are properly characterized before project deployment.Accurately measured distribution of seismic waves velocities is essential for prediction of fracture locations and monitoring of the injected CO2.Site investigation for the pilot-scale CO2 storage projects yielded abundant high-quality data, especially in terms of geophysical wellbore measurements.This information is utilized to create a dataset specific to the Illinois Basin aimed at assistance during the wellbore and seismic data interpretation, which (as any inverse problem) heavily relies on physical constraints to limit the range of potential solutions.The developed machine learning algorithms allow predicting P- and S-wave velocities based on mineralogical composition from ELAN log with accuracy superior to conservative effective media approaches.Three deployed algorithms include random forest, gradient boosting, and neural network - all of them yielded significantly more accurate estimations of seismic velocities compared to the sonic log.Additionally, this technique reconstructs complex patterns in variations of velocities associated with heterogeneities of geologic formations.It opens ways for further enhancement of datasets to evaluate involved hydromechanical parameters (e.g., poroelastic properties and permeability) to refine the velocity models and make identification of subsurface features and monitoring of the fluid migration processes more accurate.