Abstract
Numerical groundwater modelling to support mining decisions is often challenging and time consuming. Simulation of open pit mining for model calibration or prediction requires models that include unsaturated flow, large magnitude hydraulic gradients and often require transient simulations with time varying material properties and boundary conditions. This combination of factors typically results in models with long simulation times and/or some level of numerical instability. In modelling practice, long run times and instability can result in reduced effort for predictive uncertainty analysis, and ultimately decrease the value of the decision-support modelling. This study presents an early application of the Iterative Ensemble Smoother (IES) method of calibration-constrained uncertainty analysis to a mining groundwater flow model. The challenges of mining models and uncertainty quantification were addressed using the IES method and facilitated by highly parallelized cloud computing. The project was an open pit mine in South Australia that required predictions of pit water levels and inflow rates to guide the design of a proposed pumped hydro energy storage system. The IES calibration successfully produced 150 model parameter realizations that acceptably reproduced groundwater observations. The flexibility of the IES method allowed for the inclusion of 1493 adjustable parameters and geostatistical realizations of hydraulic conductivity fields to be included in the analysis. Through the geostatistical realizations and IES analysis, alternative conceptual models of fractured rock aquifer orientation and connections could be conditioned to observation data and used for predictive uncertainty analysis. Importantly, the IES method out-performed finite difference methods when model simulations contained small magnitude numerical instabilities.
Highlights
Model predictive uncertainty analysis is a fundamental component of informative modeling for decision-making [1]
The results demonstrate that the Iterative Ensemble Smoother (IES) methodcan canbe be successfully successfullyapplied appliedtotomodels modelswith withinstabilities instabilitiesthat thathinder hinderfinite finitedifference-based difference-basedcalibration calibrationand and uncertainty uncertaintyanalysis
If more time was required to iteratively refine the calibration model so that finite difference methods could be applied, model run times would likely increase and prevent much effort being directed towards predictive uncertainty analysis. This would mean that decision makers would not be equipped with an understanding of the range in plausible values for important predictions for risk-based decisions. This project has demonstrated the successful application of the IES method for assessing alternative conceptual models of connected fractured rock strip aquifers in a real-world groundwater modeling study to support mining decisions
Summary
Model predictive uncertainty analysis is a fundamental component of informative modeling for decision-making [1]. This is recognized by the groundwater modelling community [2], in practice, uncertainty analysis is often lacking due to practical limitations of model complexity, client expectations and consulting time/budget constraints [3,4]. The application of rigorous Monte Carlo sampling methods [5,6,7] to do this analysis with computationally expensive numerical simulations of groundwater flow is not currently feasible. Water 2019, 11, 1649 a computational effort that scales with the dimensionality of the parameters, limiting the feasible parameterization of computationally expensive models, and potentially inducing predictive error through calibration with a simplified parameterization scheme [9,10]. Recent advances in predictive uncertainty techniques in the field of petroleum reservoir modeling [12,13]
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