Data-Driven Groundwater Model Development: A Case Study in Baton Rouge, Louisiana

This study targets two challenges in data-driven groundwater model development: grid generation and model calibration for siliciclastic aquifer systems that are fluvial in origin. Realistic hydrostratigraphy can be developed using a large quantity of well log data to capture the complexity of an aquifer system. However, generating valid groundwater model grids to be consistent with the complex hydrostratigraphy is non-trivial. Model calibration can also become intractable for groundwater models that intend to match the complex hydrostratigraphy. This study uses the Baton Rouge aquifer system, Louisiana to illustrate a technical need to cope with grid generation and model calibration issues. A grid generation technique is introduced based on indicator kriging to interpolate 583 well logs in the Baton Rouge area to derive a hydrostratigraphic architecture with fine vertical discretization. Then, an upscaling procedure is developed to determine a groundwater model structure with 162 layers that captures facies geometry in the hydrostratigraphic architecture. To handle model calibration for such a large model, this study utilizes a derivative-free optimization method in parallel computing to complete parameter estimation in a few months. The constructed hydrostratigraphy indicates the Baton Rouge aquifer system is fluvial in origin. The calibration result indicates hydraulic conductivity for Miocene sands is higher than that for Pliocene to Holocene sands and indicates the Baton Rouge fault and the Denham Springs-Scotlandville fault to be low-permeability leaky aquifers.