Assessment of Coal Seam Strength Weakening During Carbon Sequestration: An Integrated Learning Approach

Abstract

Carbon sequestration in deep, unmineable coal seams is a viable strategy for carbon reduction. However, the impact of CO₂ on coal mechanical performance poses safety concerns for a reservoir. This study proposes an integrated learning methodology that leverages experimental data involving CO₂ immersion in various phases to evaluate the mechanical performance of coal seams during carbon sequestration. The approach integrates support vector regression (SVR) through the bagging method and employs a novel algorithm to optimize SVR. The model systematically assesses seven key factors, including coal rank, sample size, saturation medium, saturation time, saturation pressure, saturation temperature, and loading rate, to understand their influence on mechanical performance. The study identified saturation temperature, coal rank, and the saturated medium as pivotal elements affecting coal seam weakening. Evaluation metrics such as squared correlation coefficient (R²), mean absolute error, and root mean square error were employed for performance comparison between the polynomial model and the integrated model. The results demonstrate the superior performance of the integrated model, with R² of 0.98, emphasizing its effectiveness in predicting coal seam strength weakening during carbon sequestration. These insights contribute to safety assessment of coalbed carbon sequestration practices.