Grid-enabled ensemble subsurface modeling
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.
Publication Source (Journal or Book title)
Proceedings of the IASTED International Conference on Parallel and Distributed Computing and Systems
Li, X., Lei, Z., White, C., Allen, G., Qin, G., & Tsai, F. (2007). Grid-enabled ensemble subsurface modeling. Proceedings of the IASTED International Conference on Parallel and Distributed Computing and Systems, 67-72. Retrieved from https://digitalcommons.lsu.edu/eecs_pubs/817