Bridging Multiple-point Geostatistics and Parameter Estimation for Better Flow and Transport Modeling

Abstract:

Inverse modeling is an essential step for reliable modeling of subsurface flow and transport, which is important for groundwater resource management and aquifer remediation. Multiple-point statistics (MPS) based aquifer modeling algorithms, beyond traditional two-point statistics-based methods, offer an alternative to simulate complex geological features and patterns, conditioning to observed conductivity data. Parameter estimation, within the framework of MPS, for the characterization of conductivity fields using measured dynamic data such as piezometric head data, remains one of the most challenging tasks in geologic modeling. A novel inverse modeling approach, termed Ensemble PATten (EnPAT) matching approach, is proposed. Remarkable features in this approach include: (1) the pattern is defined and composed of both conductivity and piezometric head; (2) the pattern is sampled from an ensemble training images which consititue of prior geological models and simulated piezometric head maps; (3) the hydraulic conductivities are estimated only at pilot points locations, and then MPS is used to finish the estimation by extrapolating the previous simulated values; (4) as new data become available, the ensemble training images are refined by ranking the models in terms of the response. As a result, the estimated hydraulic conductivity preserves the geological structures of training image, and measured hydraulic conductivity and piezometric head data are conditioned. The EnPAT method is demonstrated for simulating a two-dimensional, bimodally-distributed heterogeneous conductivity field. The results indicate that the characterization of conductivity as well as flow and transport predictions is improved when the piezometric head data are integrated into the geological modeling.