LAUSR

Abstract The uncertainty of reservoir and operating parameters challenges the accuracy of risk assessment, as well as the efficiency of optimization in carbon capture and storage (CCS) operation. To quantitatively analyze the role of uncertainty parameters on the response of CO2 injection, the effects of geomechanical and hydrogeological parameters on CCS are investigated using the approaches of distance correlation and machine learning support vector regression (SVR). In addition, a risk factor is introduced as a combination of brittleness and stress increment to assess the potential risk of caprock integrity. Using quantitative analysis, the order of importance of the parameters on the fluid pressure in the reservoir and the caprock, and the formation deformation at the ground surface and the bottom and top of the caprock are obtained. Compared to formation deformation, the pressure change can provide more valuable information regarding the assessment of the integrity of the caprock. The trained SVR surrogate model based on SVR can predict both the pressure change as well as formation deformation with reliable accuracy.