On the value of lithofacies data for improving groundwater flow model accuracy in a three-dimensional laboratory-scale synthetic aquifer

Improvement of the prediction accuracy of groundwater flow models has been receiving substantial attention from many researchers through the development of enhanced characterizations of the structure of subsurface lithofacies and of the distribution of hydraulic conductivity. In this study, we investigated how incorporating increasing amounts of lithofacies data into the construction of a conceptual model of aquifer heterogeneity helps to reduce prediction error and uncertainty in groundwater flow models. An approach based on both laboratory experiments and numerical simulations was tested using data from an intermediate-scale synthetic heterogeneous aquifer. The heterogeneous aquifer consisted of five lithofacies, corresponding to five test sands. Three pumping tests were conducted and provided experimental data to perform groundwater flow model calibration and validation. The pumping tests were also simulated numerically in order to provide a series of error-free synthetic hydraulic data sets. On the basis of Markov chains models of transition probabilities, a total of 901 random realizations of the heterogeneous distribution of lithofacies were created using varying amounts of conditioning lithofacies data sampled along randomly placed hypothetical boreholes. For each realization and for two other simplified lithofacies models, parameter estimation was performed to estimate the hydraulic conductivity of the lithofacies using the experimental and synthetic hydraulic data from the three pumping tests. The results generally showed that the use of more lithofacies data in the construction of the lithofacies realizations led to an improvement in groundwater flow model prediction accuracy. When using the error-free synthetic hydraulic data, the calibration-prediction error and uncertainty decreased drastically when the mean borehole spacing was on the order of twice the horizontal correlation length or less. When the experimental hydraulic data were used, this drastic improvement in the calibrationprediction error was somewhat obscured and, in some cases, exhibited a local minimum. This local minimum, although beyond practical limits, corresponded to an optimal number of boreholes. Finally, the effect of incorporating more lithofacies data for the construction of lithofacies realizations was found to have a similar impact on the quality of model calibration and on the quality of predictive simulations conducted using the calibrated model.