Abstract: Reservoir numerical simulation is a core tool in petroleum engineering, with history matching essential to predictive accuracy and development decisions. Conventional history‑matching methods are often inefficient, labor‑intensive, and computationally expensive. Although the ensemble smoother with multiple data assimilation (ES‑MDA) can accelerate automatic history matching, practical use still suffers from filter divergence and spurious correlations. We propose a covariance‑localized ES‑MDA that limits long‑range, dynamically irrelevant correlations during updates and apply it to the PUNQ benchmark. Compared with conventional ES‑MDA, the localized variant, for production‑parameter inversion, reduces fitting error by 21.23%, increases convergence speed by 25%, and improves stability; for model‑parameter inversion, it yields estimates closer to the reference model, lowering parameter error by 4.78%. It also reduces cumulative post‑iteration divergence by 91.72%, indicating stronger data‑assimilation capability and better resistance to overfitting. Overall, the covariance‑localized ES‑MDA enhances the accuracy and stability of reservoir history matching and offers a practical route to efficient, reliable automatic calibration of reservoir models.