Ensemble Kalman Filter (EnKF) uses a randomized ensemble of subsurface models for performance estimation. However, the complexity of geological models and the requirement of a large number of simulation runs make routine applications extremely difficult due to expensive computation cost. Grid computing technologies provide a cost-efficient way to combine geographically distributed computing resources to solve large-scale data and computation intensive problems. We design and implement a grid-enabled EnKF solution to ill-posed model inversion problems for subsurface modeling. It has been integrated into the Res-Grid, a problem solving environment aimed at managing distributed computing resources and conducting subsurface-related modeling studies. Two use cases in reservoir studies indicate that the enhanced ResGrid efficiently performs EnKF inversions to obtain relatively accurate, uncertainty-ware predictions on reservoir production. This grid-enabled EnKF solution is also being applied for data assimilation of large-scale groundwater hydrology nonlinear models.