Article 4: Caving Intelligence

In this series article, I looked at the challenges of dealing with big data during the exploration phase of a new underground or surface mine. Here, I look specifically at block caving and its need for big data and proper big data management.

As you know, with orebody grades continuing to decline around the world, more and more mines are looking for new sources of low-grade mineral deposits farther underground, below open-pit mining depths. But instead of turning to traditional underground mining systems, many mines are opting to try block caving, where a large section of rock is undercut and left to collapse under its own weight before the ore is extracted through draw points and excavation tunnels underneath the rubble.

Block caving is the only underground mining method that can offer production rates and operating costs comparable to open-pit mining. And it is even better than open-pit mining in other ways — for example, it costs less to drill and to blast, and it generates far less waste.

However, caving also comes with a number of challenges.

Caving challenges

While long-term operating costs are low, the initial financial investment in block-caving mining is high, largely because it may take many kilometres of development before production can begin in earnest.

Block caving also requires a very large deposit, with sufficient height and footprint area, to be cost-effective. In addition, that very large deposit should ideally include certain geotechnical characteristics, such as pre-existing rock fractures to speed fragmentation and enough rock mass strength to support extraction tunnels. But those characteristics can be hard to assess, and there is always the risk that the deposit will simply be too competent to cave or that it will collapse unpredictably, making it both difficult to extract the ore efficiently and potentially hazardous for workers and equipment.

It is also difficult for mines to monitor how the caving process is progressing during the cave propagation phase and to track the paths the ore is taking.

One solution

One solution to these challenges is for mines to pursue caving intelligence — that is, to use big data management and big data analysis to use their collected data to help eliminate caving issues and/or bottlenecks.

For example, GEOVIA’s Caving Intelligence application enables mines to collect, store, and integrate all their caving data — no matter the type or source — on the 3DEXPERIENCE platform. With all caving data in one location, mines then have the ability to:

• select, adopt, and customise a variety of advanced-analytics programs, including AI and machine learning, and
• create customised dashboards specifically for visualising specific aspects of the exploration and caving processes.

From there, mines can:

• compare the actual grade and tonnage versus forecasted grade and tonnage by:
  • selecting a specific time period
  • specifying sectors/draw points, and
  • visualizing the draw points as an interactive PLM model

• track both material movement from the draw point to the mill using a range of filters to refine and visualise the data, and equipment usage hours filtered by date ranges and equipment type, and

Use case: water influx

Here’s how Caving Intelligence might help a mine resolve a water influx issue:

• The mine’s senior geotechnical engineer (let’s call her Sara) uses Caving Intelligence to check the active draw points daily to see which draw points are wet or dry, and which are active, hung-up, in rehab, or closed.
• One day, Sara notices that there is significant water influx in three areas, so she creates an issue within the Caving Intelligence app in the 3DEXPERIENCE platform that highlights the affected rings and provides all relevant procedures or data related to the water influx issue
• Sara assigns the job of updating the draw sequence for the affected rings to Malachi, the planning manager, who then revises the extraction rate in the schedule of those draw points and updates the prescribed draw order.   Simulations of the draw sequence can also be performed to test various hypothesis and try and maintain the production profile
• Min-ji, the mine technical services manager, reviews the issue and Malachi’s proposed resolution. Once she is satisfied that issue has been resolved properly, Min-ji closes the issue.  3D Navigate– the right-hand side image shows the 3DNavigate applications (PLM model of the mine), and the graphs on the left-hand side are an example page from caving intelligence. The colour of the rings reflects the data shown in the moisture graph on the left.

Read the series