The proposed mining area is in a challenging hydrogeological environment, with groundwater inflows to the planned open-cut pits posing a potential risk to project feasibility.

The area has a complex network of faults and associated fractures. We identified a critical need to characterise these structural features, which have the potential to connect several pits to rivers and existing pit lakes. Additionally, borehole evidence indicated the presence of karstic features with potential to further complicate the dynamic groundwater response to mining.

The extent to which the fault and fracture networks may influence groundwater inflow into the proposed pits was not previously investigated during the pre-feasibility study for the project.
Field investigations were designed to test the hydraulic behavior and properties of key structures around the pits. Leapfrog® 3D geological models were used to identify the architecture of key structures and plan the locations and depths of additional field drilling.

Constant-rate pumping tests were undertaken at selected fault structures. Advanced aquifer testing analysis techniques (including diagnostic flow plots) indicated that a perennial river was acting as a constant head recharge boundary to a subset of fault structures. It also suggested the presence of regional no flow barriers causing linear flow conditions at two sites. Subsequent sensitivity analysis helped specify the linear boundaries of the dual-porosity aquifer system.

Unsupervised machine learning algorithms such as Principal Component Analysis allow large, multi-dimensional datasets to be reduced to a few essential characteristics that can explain underlying relationships and processes. For this project, machine learning offered further insight into the transient evolution of the aquifer major ion hydrochemistry before, during and after stress-testing, clarifying the role of river recharge sources and the influence of no-flow barriers.

The targeted field investigations and advanced aquifer testing analysis provided a more insightful and realistic representation of the site’s hydrogeological conceptual model.
Key hydrogeological features that would otherwise have remained hidden until operations commenced are now accounted for in pit inflow predictions.

The investigations and analysis by WSP reduced key uncertainties and presented a more realistic picture of the site’s water handling requirements, optimising water management planning and bringing clarity to project decision-making.

* This work was performed by Golder professionals who joined WSP in an acquisition completed in 2021.