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| Type: | Resource | |
| Storage: | The size of this resource is 1.6 KB | |
| Created: | Feb 08, 2023 at 2:35 p.m. | |
| Last updated: | Feb 08, 2023 at 2:35 p.m. | |
| Citation: | See how to cite this resource |
| Sharing Status: | Public |
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| Views: | 548 |
| Downloads: | 212 |
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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.
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Additional Metadata
| Name | Value |
|---|---|
| DOI | 10.3390/w11081649 |
| Depth | |
| Scale | 11 - 101 km² |
| Layers | 13 |
| Purpose | Decision support |
| GroMoPo_ID | 274 |
| IsVerified | True |
| Model Code | Feflow |
| Model Link | https://doi.org/10.3390/w11081649 |
| Model Time | 2011-2018 |
| Model Year | 2019 |
| Model Authors | Hayley, K; Valenza, A; White, E; Hutchison, B; Schumacher, J |
| Model Country | Australia |
| Data Available | Report/paper only |
| Developer Email | khayley@groundwater-solutions.com.au |
| Dominant Geology | Model focuses on multiple geologic materials |
| Developer Country | Australia |
| Publication Title | Application of the Iterative Ensemble Smoother Method and Cloud Computing: A Groundwater Modeling Case Study |
| Original Developer | No |
| Additional Information | |
| Integration or Coupling | None of the above |
| Evaluation or Calibration | Dynamic water levels |
| Geologic Data Availability | No |
How to Cite
This resource is shared under the Creative Commons Attribution CC BY.
http://creativecommons.org/licenses/by/4.0/
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