Optimal allocation of computational resources in hydrogeological models under uncertainty

Flow and transport models in heterogeneous geological formations are usually large-scale with excessive computational complexity and uncertain characteristics. Uncertainty quantification for predicting subsurface flow and transport often entails utilizing a numerical Monte Carlo framework, which repeatedly simulates the model according to a random field parameter representing hydrogeological characteristics of the aquifer. The physical resolution (e.g. spatial grid resolution) for the simulation is customarily chosen based on recommendations in the literature, independent of the number of Monte Carlo realizations. This practice may lead to either excessive computational burden or inaccurate solutions. We develop an optimization-based methodology that considers the trade-off between the following conflicting objectives: time associated with computational costs, statistical convergence of the model prediction and physical errors corresponding to numerical grid resolution. Computational resources are allocated by considering the overall error based on a joint statistical–numerical analysis and optimizing the error model subject to a given computational constraint. The derived expression for the overall error explicitly takes into account the joint dependence between the discretization error of the physical space and the statistical error associated with Monte Carlo realizations. The performance of the framework is tested against computationally extensive simulations of flow and transport in spatially heterogeneous aquifers. Results show that modelers can achieve optimum physical and statistical resolutions while keeping a minimum error for a given computational time. The physical and statistical resolutions obtained through our analysis yield lower computational costs when compared to the results obtained with prevalent recommendations in the literature. Lastly, we highlight the significance of the geometrical characteristics of the contaminant source zone on the optimum physical and statistical resolutions.