Posterior Assessment of Parameters in a Time Domain Random Walk Model of Partitioning Tracer Tests in Two‐Phase Flow Scenarios

Abstract We provide a reliable and efficient methodological framework for the interpretation of laboratory‐scale partitioning tracer test data under uncertainty. The proposed approach rests on Time domain random walk (TDRW) particle tracking methodology. range applicability latter is extended to include transport tracers upon considering retardation trapping mechanisms. A classical maximum likelihood (ML) applied extensive set experimental observations Dwarakanath et al. (1999, https://doi.org/10.1021/es990082v ). This yields best estimates model parameters, including residual immobile phase saturation, partition coefficient parameters memory function employed simulate impact solute trapping. Experimental are included in objective relying regularization term. show that these types information, which typically obtained through batch experiments, important attain joint saturation. Sample probability distributions conditional available then assessed stochastic inverse modeling approach. step poses significant challenge terms computational effort performed reduced order surrogate model. Our results TDRW‐based can effectively capture key features observed breakthrough curves various analyzed satisfactory