Date: October 14, 2021
Author: Ken Kuchling
In this blog I wish to discuss some personal approaches used for interpreting pit optimization data. I’m not going to detail the basics of pit optimization, instead assuming the reader is familiar with it .
Often in 43-101 technical reports, when it comes to pit optimization, one is presented with the basic “NPV vs Revenue Factor (RF)” curve. That’s it.
Revenue Factor represents the percent of the base case metal price(s) used to optimize for the pit. For example, if the base case gold price is $1600/oz (100% RF), then the 80% RF is $1280/oz.
The pit shell used for pit design is often selected based on the NPV vs RF curve, with a brief explanation of why the specific shell was selected. Typically it’s the 100% RF shell or something near the top of the curve.
However the pit optimization algorithm generates more data than shown in the NPV graph (see table below). For each Revenue Factor increment, the data for ore and waste tonnes is typically provided, along with strip ratio, NPV, Profit, Mining cost, Processing, and Total Cost at a minimum. It is quick and easy to examine more of the data than just the NPV.
In many 43-101 reports, limited optimization analysis is presented. Perhaps the engineers did drill down deeper into the data and merely included the NPV graph for simplicity purposes. I have sometimes done this to avoid creating five pages of text on pit optimization alone. However, in due diligence data rooms I have also seen many optimization summary files with very limited interpretation of the optimization data.
Pit optimization is a approximation process, as I outlined in a prior post titled “Pit Optimization–How I View It”. It is just a guide for pit design. One must not view it as a final and definitive answer to what is the best pit over the life of mine since optimization looks far into the future based on current information, .
The pit optimization analysis does yield a fair bit of information about the ore body configuration, the vertical grade distribution, and addresses how all of that impacts on the pit size. Therefore I normally examine a few other plots that help shed light on the economics of the orebody. Each orebody is different and can behave differently in optimization. While pit averages are useful, I also prefer to examine the incremental economic impacts between the Revenue Factors.
What Else Can We Look At?
The following charts illustrate the types of information that can be examined with the optimization data. Some of these relate to ore and waste tonnage. Some relate to mining costs. Incremental strip ratios, especially in high grade deposits, can be such that open pit mining costs (per tonne of ore) approach or exceed the costs of underground mining. Other charts relate to incremental NPV or Profit per tonne per Revenue Factor. (Apologies if the chart layout below appears odd…responsive web pages can behave oddly on different devices).
Conclusion
It’s always a good idea to drill down deeper into the optimization data, even if you don’t intend to present that analysis in a final report. It will help develop an understanding of the nature of the orebody.
It shows how changes in certain parameters can impact on a pit size and whether those impacts are significant or insignificant. It shows if economics are becoming very marginal at depth. You have the data, so use it.
This discussion presents my views about optimization and what things I tend to look at. I’m always learning so feel free to share ways that you use your optimization analysis to help in your pit design decision making process.
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Posted in: Junior Mining, Mine Economics, Mine Modelling, Mine Studies.Tagged: 43-101, Cashflow Model, Feasibility Study, Mine Engineering, PEA.Last Modified: July 13, 2022
This article was published by: Ken Kuchling
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