Mining PEA’s – Is it Worth Agonizing Over Details

A previous blog post, titled “PEA’s – Not All PEA’s Are Created Equal”  , discussed that different PEA’s will consist of varying levels of detail.  This is dictated by the amount of technical data available (or not available).   But so too will different chapters in the same PEA be based on different data quality.

I have seen PEA’s where some of the chapters were fairly general, relying on limited data.  Meanwhile other sections in the same study had a lot of depth and detail.   Does it make sense to use varying detail in a study?  Detailed work cost more money, but is it wisely spent money?

The level of detail justifiable

If the Resource supporting the PEA mine plan is largely Inferred material, then the mine design and production plan will have a significant degree of uncertainty.  So there is not a lot of justification for other engineers to prepare a detailed tailings design associated with that mine plan.

Similarly there is little value in developing a very detailed operating cost model or cashflow model for a study that has many underlying uncertainties.  Certainly an IRR to two decimals is impressive, but unwarranted.  Overly detailed work may be a waste of time and money, adding to the PEA duration, increasing engineering costs, and giving the unintended (or maybe intended) impression that the study is more accurate than it really is.

Different levels of detail in the same study can crop up when diverse technical teams are each working independently on their own aspects of the study.   Some teams may feel they are working with highly accurate data (e.g. a production tonnage) when in reality the data they were provided with is still highly speculative.

Conclusion

The bottom line is that it is important for the Study Manager and project Owner to ensure the entire technical team is on the same page.

The team should understand the type of information they are working with, to ensure the technical detail in the final study is consistent throughout.

Experienced reviewers will recognize where the key data gaps are in a study.  Hence they may view the entire study with that in mind regardless of how detailed some of sections in the report appear to be.

You can read more on the subject of uncertainty in PEA’s in the previous blog post at this link “Mining PEA’s – Not All PEA’s Are Created Equal“.

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For some free mining calculator apps, including project timelines and a simplified cashflow modeller, check out this website https://sites.google.com/view/drillingdown