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Open AccessArticle

Application of the Iterative Ensemble Smoother Method and Cloud Computing: A Groundwater Modeling Case Study

1
Groundwater Solutions Pty. Ltd., Melbourne 3031, Australia
2
Mining One Pty. Ltd., Melbourne 3000, Australia
3
Hillgrove Resources Pty. Ltd., Kanmantoo 5252, Australia
*
Author to whom correspondence should be addressed.
Water 2019, 11(8), 1649; https://doi.org/10.3390/w11081649
Received: 27 June 2019 / Revised: 6 August 2019 / Accepted: 7 August 2019 / Published: 9 August 2019
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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. View Full-Text
Keywords: groundwater modeling; predictive uncertainty analysis cloud computing; Iterative Ensemble Smoother; mining groundwater modeling; predictive uncertainty analysis cloud computing; Iterative Ensemble Smoother; mining
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Hayley, K.; Valenza, A.; White, E.; Hutchison, B.; Schumacher, J. Application of the Iterative Ensemble Smoother Method and Cloud Computing: A Groundwater Modeling Case Study. Water 2019, 11, 1649.

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