In recent decades, shale gas has emerged as a major unconventional natural gas resource (CH₄) due to its distinctive reservoir characteristics. Carbon dioxide (CO₂) injection has been identified as a promising technique to enhance shale gas recovery and production, while simultaneously providing a long-term CO₂ storage option and mitigating the risks of induced seismicity. However, accurate estimation of shale gas reserves remains challenging because of complex flow dynamics, including gas slippage, desorption, and multiphase transport within intricate fracture networks. Decline curve analysis is widely applied for estimating recoverable reserves owing to its simplicity and efficiency, yet it is limited by the assumption that only wells with decreasing production are considered, neglecting those exhibiting constant production behavior. In this study, a material balance equation framework was employed to develop a novel calibration method for estimating recoverable reserves under dual-porosity conditions. The key innovation of this work lies in the development of a model that distinctively integrates the combined effects of gas adsorption, matrix pore deformation induced by desorption, and the dynamics of the fracture system within a single, cohesive material balance framework. Gas production data from well B1 in the Sichuan Basin, China, were analyzed. Results demonstrate that abandonment pressure exerts a critical influence on both recoverable reserves and recovery efficiency. Free gas recovery from fractures was significantly higher compared to that from the matrix, where both adsorbed and free gas contributions remained relatively small. Furthermore, recovery rates were strongly controlled by the fracture compressibility coefficient, adsorption phase density, and matrix porosity. These findings indicate that the proposed analytical technique provides a more robust and comprehensive approach for shale gas reserve estimation, as it accounts for both decline and constant production wells, thereby improving reliability in reservoir evaluation.