Development Of Sequential Self-Calibration Method To Integrate Tracer Test Data For Refinement Of Geostatistical Hydraulic Conductivity Fields

Based on local measurements of hydraulic conductivity, geostatistical methods have been found to be useful in heterogeneity characterization of hydraulic conductivity distribution in a regional scale. However, the methods are not suited to directly integrate dynamic production data, such as hydraulic head and solute concentration, into the study of a conductivity distribution. These data record the flow and transport processes in the medium, which are closely related to spatial distribution of hydraulic conductivity. In this study, a three-dimensional gradient-based inverse method– the Sequential Self-Calibration method (SSC)—is developed to calibrate a hydraulic conductivity field, initially generated by a geostatistical simulation method, conditioning to tracer test results. The SSC method can honor both local hydraulic conductivity measurements and tracer test data. The mismatch between the simulated hydraulic conductivity field and the reference true one, measured by its Mean Square Error (MSE), reduced considerably through the SSC conditional study; the uncertainty of solute transport prediction in observed locations, measured by either variance or mismatch of mean value, also decreased significantly in comparison with the prediction without conditioning to tracer test data. Finally, the spatial distribution of uncertainty of solute transport prediction over the whole domain with and without conditioning to tracer test data are provided, which evidently shows the robustness of SSC method to reduce uncertainty of solute transport prediction.