Abstract: During oilfield development, a comprehensive model for assessing inter-well connectivity and connected volume within reservoirs is crucial. Traditional capacitance (TC) models, widely used in inter-well data analysis, face challenges when dealing with rapidly changing reservoir conditions over time. Additionally, TC models struggle with complex, random noise primarily caused by measurement errors in production and injection rates. To address these challenges, this study introduces a dynamic capacitance (SV-DC) model based on state variables. By integrating the extended Kalman filter (EKF) algorithm, the SV-DC model provides more flexible predictions of inter-well connectivity and time-lag efficiency compared to the TC model. The robustness of the SV-DC model is verified by comparing relative errors between preset and calculated values through Monte Carlo simulations. Sensitivity analysis was performed to compare the model performance with the benchmark, using the Qinhuangdao Oilfield as a case study. The results show that the SV-DC model accurately predicts water breakthrough times. Increases in the liquid production index and water cut in two typical wells indicate the development time of ineffective circulation channels, further confirming the accuracy and reliability of the model. The SV-DC model offers significant advantages in addressing complex, dynamic oilfield production scenarios and serves as a valuable tool for the efficient and precise planning and management of future oilfield developments.