Data assimilation for real-time subsurface flow modeling with dynamically adaptive meshless node adjustments

Over the past few decades, various inverse modeling and data assimilation techniques have been proposed to integrate observed data into subsurface flow models for optimal parameter estimation. In practice, subsurface flow models are often constructed based only on preliminary hydrogeological surveys. Additional data will be collected from supplementary hydrogeological surveys afterward and will therefore be difficult, if not impossible, to incorporate into predefined numerical nodes. Grid refinement or model reconstruction is repetitively required whenever new or additional data are assimilated into physical models. A novel data assimilation method that uses a dynamically adaptive node adjustment (DANA) scheme was proposed in this paper. DANA avoids laborious remeshing to assimilate real-time data. It combines the meshless method, interpolation method, and fast-node-placement algorithm to automatically update the layouts of computational nodes according to the newly available data over time. The meshless generalized finite difference was chosen to develop the DANA framework, and the ensemble Kalman filter (EnKF) was used as the data assimilation approach. The accuracy and computational efficiency of the proposed methods were investigated, and the applicability of DANA was demonstrated by solving a hypothetical assimilation problem. The results indicate that DANA can efficiently cooperate with the EnKF to achieve real-time updating for subsurface modeling. The DANA-based assimilation model can flexibly handle randomly distributed additional data, efficiently reduce parameter uncertainty, and provide versatile dynamical modeling.

Automated summary

This paper proposes a novel data assimilation method, called dynamically adaptive node adjustment (DANA), which achieves real-time updating of data. The method effectively cooperates and improves the accuracy and computational efficiency of subsurface modeling.