The project involves modeling the execution of the planned mine, which would take place over a period of 50 years, and predicting the resulting impacts to the regional groundwater system, out to 150 years post closure. The groundwater model and accompanying results are subject to a rigorous level of review by stakeholders, as well as Federal and State agencies for permitting purposes. Therefore, an accurate representation of the block cave mine within the groundwater model is required for producing the most defensible model and associated predictions.
Block cave mining is an underground mining method in which the ore body is mined from the bottom up, as opposed to more traditional methods of open pit surface mining or underground tunneling. In the case of this project, the base of the ore body sits approximately 7,000 feet below the ground surface and the resulting cave is expected to reach a mile in width.
The mining industry presents unique challenges for groundwater modeling projects, given the frequent and continuous changes to surface and subsurface ground conditions. Mining practices—underground tunneling, blasting, backfilling and block cave mining, for example—alter rock and change the hydraulic properties of the affected rock mass over time.
The block cave mining process, in particular, induces a large stress on the overlying rock mass through the removal of rock at the base of the ore body. This stress in turn causes a network of fractures to propagate upwards resulting in increases to hydraulic conductivity and storage parameter values by orders of magnitude. A standard groundwater modeling approach may implement broad, general assumptions in regards to the timing and magnitude of hydraulic property changes. By contrast, the approach WSP has taken utilizes geotechnical modeling outputs, which quantifies the deformation of the ore body and overlying rock through time, and translates these data into transient hydraulic property changes. The use of geotechnical model output for groundwater model parameter estimation provides a more accurate and defensible model for integrated groundwater modeling, providing higher confidence predictions.