Simulating mining operations can help you optimize your design, planning, and management of your project. However, simulations are only as good as the data and assumptions you use to create them. How can you make sure your results are accurate and reliable? Here are some tips to follow.
Before you start simulating, you need to have a clear idea of what you want to achieve and how you will measure it. Do you want to maximize production, minimize costs, reduce environmental impacts, or improve safety? What are the key performance indicators (KPIs) that you will use to evaluate your simulation outcomes? Having well-defined objectives will help you focus your simulation on the most relevant aspects of your mining operations.
Ensuring accuracy in simulating mining operations involves foundational elements such as precise data input, validation through historical data, and continuous calibration based on real-world feedback. Realistic assumptions guide the simulation, and scenario analyses, sensitivity assessments, and benchmarking against industry standards enhance its robustness. The iterative refinement process, incorporating external validation by mining experts, contributes to a reliable simulation model closely aligned with real-world dynamics. These strategies collectively empower informed decision-making and optimization of mining operations.
There are many software tools available for simulating mining operations, each with its own features, capabilities, and limitations. You need to choose the one that best suits your objectives, data, and budget. Some factors to consider are the level of detail, complexity, and realism of the simulation, the ease of use and customization of the software, the compatibility and integration with other software and data sources, and the support and documentation provided by the vendor.
The quality of your simulation depends largely on the quality of your data. You need to make sure that your data is accurate, complete, and consistent. You also need to check that your data reflects the current and expected conditions of your mining operations, such as geology, equipment, processes, costs, and regulations. You can use various methods to validate your data, such as comparing it with historical records, field observations, or expert opinions.
Use of benchmarking is critically important when modeling costs and operations. Rarely are projects developed with better outcomes than initially estimated. When people get into modeling or estimating often complex detail can make it more difficult to get estimates correctly. Stepping back and evaluating results based on benchmarking can be tremendously effective at catching and validating results.
Every simulation involves some assumptions and simplifications that may affect the accuracy of your results. You need to identify and test your assumptions to see how sensitive your simulation is to changes in the input parameters. You can use sensitivity analysis, scenario analysis, or Monte Carlo simulation to test your assumptions and explore the range of possible outcomes. You should also document your assumptions and justify them with evidence or logic.
Once you have built your simulation model, you need to verify that it works as intended and calibrate it to match the real-world data. You can use various techniques to verify and calibrate your model, such as debugging, tracing, animation, or comparison. You should also perform some validation tests to check that your model produces results that are consistent with reality or expectations. You can use historical data, benchmarks, or expert opinions to validate your model.
Your simulation is not a static product, but a dynamic tool that needs to be reviewed and updated regularly. You need to monitor your simulation performance and compare it with the actual performance of your mining operations. You also need to update your simulation with new data, changes in conditions, or feedback from stakeholders. You should also document your simulation process, results, and recommendations for future improvement.
Stratigraphic changes aren’t always readily apparent, even with core drilling and advanced modeling techniques. Faulting and unconformities can be hidden deep below the surface, resulting in significant changes in recoverable resources and associated economics.