FracMan® discrete fracture network analysis supports extraction at an open-pit mine

Project Info




It’s a problem that has perplexed miners since the earliest humans dug pits into the ground in search of metal ore: how steep can the walls of the pit be, to balance efficiency against safety?

A steeply-sloped pit means less waste rock to move out of the way to get to the orebody, plus a smaller geographic footprint, and usually, lower financial costs. However, the steeper the pit walls are, the more vulnerable they are to collapse, endangering workers and delaying production.

This was the question faced by the owners of an open-pit iron mine in Europe. The mine had already experienced one major collapse of the pit wall. So, as part of their Feasibility Study to re-start the mine, the mining company asked Golder for help answering two questions:

  • Were there any other areas in the mine likely to have a significant collapse?
  • What was the optimal slope for the pit walls?

One concern was that while the rock into which the mine had been dug was strong enough to allow for steep pit slopes, there were many fractures weakening the rock mass as a whole. Some of the largest examples of these fractures, measuring up to hundreds of meters, formed failure surfaces on which the major collapse had occurred.

A key factor influencing the stability of fractures is the frictional strength of the rock fractures. By studying rock samples, information on this and other important rock properties could be incorporated into the slope stability analysis.

Understanding the rock mass with discrete fracture networks

To find an answer to the mining company’s questions, Golder used its proprietary FracMan® software, which uses a Discrete Fracture Network (DFN) methodology to understand a rock body’s fracture network in a 3-D environment.

The first step was to gather data so that the FracMan model would be based as closely as possible on the actual rock mass. This included lowering testing equipment down some of the boreholes, flying a drone around the pit to gather LiDAR (similar to radar, but using light to measure distance) data and undertaking photogrammetric mapping of the pit walls.

With this field data, Golder’s team was able to generate a FracMan DFN model of the rock and the fractures within it, simulating the orientation, intensity and size of fractures that were likely to be found within the target rock mass.

Computer model used to support key business decisions

A key component of Golder’s work came from wedge analysis – determining where fractures intersected in three dimensions, areas where pressures on the rock might cause a large portion to move out of the pit wall.

They tested many different slopes for each bench (the giant-sized “stairs” that step back the walls of the pit) and the overall pit slope. One of the strengths of the FracMan environment is that it allows automated parameter sweeping – where different values can be inserted – allowing for many different “what-if” scenarios to be tried automatically, helping to find the optimal solution.

This animation shows some possible ways the rock may react, given several different degrees of slope. The fractures are shown as lines; the wedges shown in green are considered to be stable, while those in red could be unstable.

This animation shows some possible ways the rock may react, given several different degrees of slope. The fractures are shown as lines; the wedges shown in green are considered to be stable, while those in red could be unstable.

One result of this analysis was that the mining company revised its design for the mine, so that the largest of those problematic “red-wedge” areas would be removed entirely as part of mining activities, mitigating a potential risk.

Further to mine planning, Golder carried out calculations on how much of the total bench area might be lost under a range of potential slopes between benches, reducing the area available to ‘catch’ material falling from above. This helped the mining company understand the efficiency cost of the slope, to be balanced against the need for slope stability.

Verification of Golder’s large-scale fracture model was carried out by virtually re-creating the pit wall collapse that had occurred, to verify whether the model would have indicated this area as posing a problem. FracMan’s stability analysis performed well in this test.

At the end of the project, Golder was able to help the mining company get answers to its two main questions and assist in completing the Feasibility Study. Areas with large-scale fractures that might be prone to slope failure were located, so that the mining company can exercise due caution and take mitigation steps. And, the small-scale fractures within the rock mass help indicate how steep the pit walls can be, to reach that elusive balance between efficiency and slope stability.



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