Mining Turns To Tech To Predict Profitable Sites

Ruediger Schroedter

Gone are the days in mining when assessments of potential dig sites meant lots of waiting for results. Gone, too, is the uncertainty on a mine job about where to go next.

For mining executives, recent advances in digital technology allow companies to make decisions at a rapid pace. Decisions that used to take days and weeks now can be done in minutes and hours.

With more information available faster, mining leaders reduce both short- and long-term financial risk. Data from across the enterprise inform decisions about buying and selling assets. Profitability should increase, driven by key technology advances.

Digging in to the data

There are two key drivers to this digital revolution. The first is the rise of the Internet of Things (IoT). The IoT consists of devices that are equipped with sensors, software, and wireless capabilities. These devices are connected to each other and can detect, store, and send data.

The second is the rise of Big Data, mobile, and cloud computing. Today’s mobile devices can track, send, and receive data from remote sites worldwide. Cloud computing stores billions of bytes of data at low cost. Big Data analytics programs take data coming from many different locations and systems and synthesize it. Those programs then better inform decisions by offering dashboards, metrics, and predictive modeling.

Robots are able to venture into hazardous areas and move material with remote human oversight. On-site mining data is sent via mobile phone to a cloud-based platform. For mining, the convergence of these technologies provides extraordinary possibilities.

Technology at play

The potential impact is significant. A recent report by McKinsey & Co. showed the use of advanced analytics in mining and related industries had a major impact. Firms using these programs to assess production areas increased their profit margins by 2-3 percentage points.

One mining company used so-called Monte Carlo simulations to reduce certain capital expenses. Frequently used in finance, biology, and insurance, Monte Carlo simulations use complex algorithms and repeated random sampling to model possible outcomes. The Mining Journal reported on how the company challenged assumptions about a project’s capital needs. It took historical data on certain disruptions such as rainfall patterns, and made models of its mines showing the impact of flooding and rainwater. The data led to a new strategy that maximized storage capacity and handling across all its mines. Capital costs dropped by 20 percent.

Buy or sell?

With so many variables at play, mining valuation is not for the faint of heart. Integrated data streams available at the discovery stage make for better-informed purchase decisions.

Software programs today can take data to build and validate exploration models. These programs use 3D visualization and validated geophysical, analytical, and drill hole data. In turn, detailed 3D topographical models are possible.

Other programs assess historical, assay, and drilling data. This information creates viable scenarios for determining whether to buy or sell a site.

These tools use data consistently from one potential site to the next, allowing for forecasting of economic risk that is consistent across the organization. The firm today can use “real options valuation” to develop models of outcomes given changing economic conditions. With clearer information about potential risks, firms can decide whether to stage, sell, abandon, expand, or buy.

Anticipating, not reacting

Mining companies realize today that these analytic platforms and dashboards offer many advantages. Users have a clearer interpretation of the aggregated and analyzed data points from multiple areas. Using predictive analytics, mining decisions are made based on smart assumptions rather than past historical information.

Robust software programs can generate reports almost instantaneously. Supervisors have on-site access to the analysis through a web browser or app. This data has many uses. Drilling managers save time and can make quicker decisions on next moves. Supplies can be ordered faster. Needed data for accreditation and compliance is immediately accessible.

Selecting the right sites

One example is assay analysis. Today geologists do not wait weeks or months for assay results. Instead of off-site analysis, web-based applications deliver information much faster to inform decisions.

Robots are sending information about field operations, safety, needed maintenance, and drilling performance. Some devices send the information themselves. In other cases, staff members use mobile phones, tablets, or laptops. This information and analytics in turn help with site selection. Integrating data from mine planning, ventilation, safety, rock engineering, and mineral resources improves overall forecasting.

Discovery, particularly of Tier 1 sites, is an increasingly costly venture for mining companies. Demand for many products is increasing while discovery rates are dropping. Mined product is of a lesser quality, particularly in mature mining locations. Many possible sites are in areas that are under-explored areas with difficult and deep cover.

The advanced technologies available today are contributing to rapid improvement in these discovery issues.

Prospective drilling

Consider the drill hole. To reduce exploration costs, there must be enough rich information from the opening drill hole, and it must be delivered in as close to real time as possible. Doing so lessens the risk of the second drill hole. Better information from the start helps improve vectoring and provides better information about what mineral systems are being drilled.

This approach, called prospective drilling, is becoming increasingly used in mining. It employs drilling activity to map covered mineral systems. In turn, geochemical and geophysical vectoring can lead firms toward deposits.

Australia has invested heavily in this area. The Deep Exploration Technologies Cooperative Research Centre (DET CRC) has a singular vision: uncovering the future. Its core purpose is “to develop transformational technologies for successful mineral exploration through deep, barren cover rocks.”

To get to that point, the DET CRC is borrowing a drilling technique from the oil business. Coiled tubing is paired with downhole and top-of-the-hole sensors. The information provides petrophysical, structural, rock fabric, geochemical, and mineralogical data all at once.

Conclusion

To meet increasing demands for new viable sites and to improve efficiency on sites, mining is changing. Using smart, connected products and robust data modeling, mining is being done faster, more safely, and more efficiently than ever.

To learn more about digital transformation in mining, visit Drive Efficiency in Mining with Digital Transformation.


Ruediger Schroedter

About Ruediger Schroedter

Ruediger Schroedter is responsible for solution management of SAP solutions for the mining industry worldwide. He has spent more than 15 years in the mill products and mining industries and has extensive experience implementing SAP solutions for customers in these industries before coming to SAP.