Articles tagged with: Resources

Gold Exploration Intercepts – Interesting or Not?

As a mining engineer, I am not usually called in to review a project that is still at the exploration stage. This is normally the domain of the geologist. However from time to time I have an interest in better understanding the potential of an early stage mining project. This could be on behalf of a client, for investing purposes, or just for personal curiosity.
At the exploration stage one only has drill interval data from news releases to examine. A resource estimate may still be unavailable.
The drill data can consist of long intervals of low grade or short intervals of high grade and everything in between. What does it all mean and what can it tell you?
The following describes an approach I use for examining early stage gold deposits. The logic can be expanded to other metals but would take more effort.
My focus is on gold because it has been the predominant deposit of interest over the last few years, and it is simpler to analyze quickly.

We All Like Scatter Plots

My approach relies on a scatter plot to visual examine the distribution of interval thicknesses and gold grades. Where these data points cluster or how they are distributed can provide some prediction on the overall economic potential of a project. Its not a guarantee, but only an indicator.
I try to group the analysis into potential open pit intervals (0 to 200 metres from surface) and potential underground (deeper than 200m) intervals. This is because a 20m wide interval grading 2.0 g/t is of economic interest when near surface, however of less interest if occurring at a 300m depth.
Using information from a news release, I create a two column Excel table of highlighted intervals and assay grades. The nice thing about using intervals is that the company has provided their view of the mineable widths.
If one is provided with raw 1-metre assay data you would have to make that decision, which can be a significant task. The company has already helped make those decisions.
Normally I tend to use the highlighted sub-intervals and not the main intervals since issues with grade smoothing can occur.
A large interval containing multiple high grade sub-intervals may see some grade smoothing.This happens if the grade between sub-intervals is very low grade or even waste. It takes a fair bit of effort to assess this for each drill hole, hence it is easier to work with the sub-intervals.
I have an online calculator (Drill Intercept Calculator) that lets you assess if grade smoothing is occurring.
When inputting the interval thickness, I prefer to use the true thickness and not the interval length. If the assay information does not specify true thicknesses, then I simply multiple the interval length by 0.70 to try to accommodate some possible difference in width. Its all subjective.
The assays can consist of Au (g/t) or AuEq (g/t) if more metals are present. If very high grades are encountered (greater than 10 g/t) I simply input 9.9 g/t into the Excel table so they fit onto my scatter plot. Extremely high grades can be sporadic and localized anyhow.
Finally I need to decide whether the project is located in a region of high operating cost, low cost or about average costs. High costs could be with a fly in/ fly out, camp operation, with diesel power, and seasonal access.
A low cost operation could be in temperate climate, with good access to local infrastructure, water, labour, and grid power. An average operation would be somewhere in between the two. Its just a gut feel.

Results

The following charts describe how it works, using randomly generated dummy assay data in this example.
In the Average cost scenario (left chart) the points are equally scattered both above and below the Likely Economic line. As one moves to a high-cost situation (middle chart) the curve moves upwards and more drill intervals now fall below the economic line.
This would give me an unfavorable impression of the project. The third graph is the Low-Cost scenario and one can see that more assays are now above the line. Hence the same project located in a different region would yield a different economic impression.
The economic boundaries (dashed lines) presented in the plots are based on my personal experience and biases. Other people may have different criteria to define what they would view as economic and uneconomic intervals.

Conclusion

There is not much that a layperson person can do with the multitude of exploration data provided in corporate news releases. However, by aggregating the data one can get a sense of where a gold project positions itself economically. The more data points available, the more that one can gather from the plot.
One should prepare separate plots for shallow and deep mineralization or for different zones and deposits on a property rather than aggregate everything together.
It may be possible to undertake a similar analysis with different commodities if one can summarize the assays into a single equivalent value or NSR dollar value. Unfortunately, exploration news releases don’t often include the poly-metallic interval equivalent grade or NSR value. Calculating these manually would add an extra step in the process, however it can be done.
If you want to try out the concept, I have posted the online spreadsheet to my website at the link Drill Intercept Potential where you can input Au exploration data of interest. Unfortunately, you cannot save your input data so it’s a one time event.   Anyone can do this – its not rocket science.
Let me know your thoughts, suggestions, or other ways to play with news release data.
If your project contains metals other than gold, then the rock (or ore) value will be based on the revenue from a combination of metals.   How to approach this in discussed in another blog post titled “Ore Value Calculator – What’s My Ore Worth?

Great Bear Resources Example

Interesting the Great Bear Resources website allows one to download a data file with all their exploration intervals.  I have not seen another company provide this level of transparency.   I download their data file of over 1300 intervals and sub-divided them into major intervals and sub-intervals (more ore less).   The two plots below show the outcome.
The graph on the left is the sub-intervals showing that many points are above the “economic” line.  There are numerous data points along the top axis, indicating many sub-intervals at >10 g/t at widths ranging from 1 to 15 metres.  The graph on the right shows the major intervals.  While there are still many along the top axis, there are now more along the 40m width but at grades ranging from 1 g.t to 6 g/t.
One would surmise from these plots that overall there are many intervals above the line in the economic zone, showing the potential of the project.  It also shows that GBR have encountered many intervals likely sub-economic, but that’s the exploration game.

Great Bear Resources data

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AI versus the Geologists

We likely have all seen recent articles about how Artificial Intelligence (AI) is going to change the mining industry.   I have been wondering if AI is a real solution or just a great buzzword.   My original skepticism has diminished somewhat and let me explain why.
At a booth at the 2019 PDAC I had a chance to speak with a publicly traded company called Albert Mining (referencing Albert Einstein’s intelligence).  They are providing exploration consulting services by applying a form of AI and have been doing so for many years.  The company has been around since 2005 but were not using the term AI to describe their methods.
These days the term “AI” has become very trendy.  Currently IBM Canada and Goldcorp are using Watson and AI to further their exploration efforts on the Red Lake property. GoldSpot Discoveries is another recent player in the mining AI field.  It appears Goldspot offers something similar to Albert Mining but they extend their platform to include picking projects, picking teams, and picking investments. That’s a lot of analysis to undertake.  Albert Mining is focused solely on mineral exploration.

Here is what I learned

Albert Mining’s system, called CARDS (Computer Aided Resource Detection System) uses pattern recognition and multi-variate analysis to examine a mineral property to look for targets.     The system requires that the property has some known mineralization hits and assay samples.  These are used to “teach” the software.   Both positive hits and negative hits are valuable in this teaching step.
The exploration property is sub-divided into cells and data are assigned to each cell.  These data attributes could be derived from geophysics, geochemistry, topography, soil samples, indicator minerals, assayed samples, geological maps, etc.  I was told that a cell could contain over 700 different data attributes.
The algorithm then examines the cell data to teach itself which attributes correlate to known mineralization and which attributes correlate with barren areas. It essentially determines a geological “signature” for each mineralization type.    There could be millions of data points and combinations of attributes.  Correlation patterns may be invisible to the naked eye, but not to the computer algorithm.
Once the geological signatures are determined, the remainder of the property is examined to look for similar signature hits.  Geological biases are eliminated since it is all data driven.   The newly defined exploration targets are given a ranking score based on the extent of correlation.
Some things to note are that the system works best for shallow deposits, unless one has some deep penetrating geophysical surveys.  The system works best if there is fairly uniform data coverage across the entire property.  The property should also have generally similar geological conditions and as mentioned before, the property needs to have some mineralized assay information.
This exploration approach reminds me somewhat of the book Moneyball.  This book is about the Oakland A’s baseball team where unconventional statistics were used to rank players in order to find hidden gems.

Are geologists becoming obsolete?

I was told that many in the geological community tend to discount the AI approach.  Either they don’t think it will work or they are fearing for their jobs.  Personally I don’t understand these fears nor can I really see how geologists can ever be eliminated.  Someone still has to collect and prepare the data as well as ultimately make the final decision on the proposed targets.   I don’t see the downside in using AI as another tool in the geologist’s toolbox.
Albert Mining’s stock price has recently gained some traction (note: I am not promoting them)  because junior mining news releases are starting to mention their name more often (Spruce Ridge Resources and Falco Resources are some examples).
Probably years ago if a mining company said their drill targets were generated by an algorithm, they might have gotten strange looks.   Today if a mining company says their drill targets were generated by AI, it gives them a cutting edge persona.  Times have changed.

In conclusion

I suggest we all take a closer looks at the AI technology to better understand what it does.
P.S. I  might also suggest that Albert Mining consider revising their company name to incorporate the term “AI” to stay on trend. (Update: In October 2019, Albert Mining changed their name to Windfall Geotek; I’m not sure it better explains what they do).
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The Mining Bank or eBay for Mining Properties

mining properties
I recently attended the Money Show here in Toronto to learn a bit more about personal finanace, investing strategies, and to check out  the latest stock analysis software.
There was also a trade show, but only one mining company booth was present.  This definitely wasn’t the PDAC.  Interestingly there were about five marijuana company booths, so that is where the promotion is today.
The lone mining company was Globex Mining, here is their website.  They referred to themselves as a “mining bank”, so that was something that peaked my interest.

Mining bank

Speaking with their president, Jack Stoch, he gave me an overview on their business model.  As I understood it, GLOBEX’s model is to acquire a portfolio of mineral properties.  They would try to enhance their value by undertaking some limited geological work.  Finally they would option, JV, or sell the property while retaining an NSR royalty.
Mr. Stoch told me that Globex currently has over 140 land packages in their inventory.  Their properties will be at different stages.  Some have resource estimates, others only mineralized drill intersections, mineral showings, untested geophysical targets, or combinations of these.
They are focusing their acquisitions on lower risk jurisdictions like Quebec, Ontario, Nova Scotia, New Brunswick, Tennessee, Nevada, Washington, and Germany.  They try to acquire historical mines that have old shafts, following the adage the best place to find a new mine is next to an old mine.   They also have some industrial mineral properties.

 

Globex’s only NSR revenue property right now is a zinc project in Tennessee that can generate a seven-figure royalty each year, when that operation is up and running.  Unfortunately for Globex the zinc operation has not been in consistent operation the last few years.

Its a good concept

I like the concept that Globex are promoting.  I like the idea of having a one-stop shop that acquires and options out exploration properties to mining companies looking for new projects.
I also like the idea of trying to consolidate land packages in an area,  minimizing the patchwork of multiple ownership claims that can hinder advanced development.
Globex hope that by putting time and effort into a bunch of properties a few of them will pay off.  If they can generate sufficient NSR revenues, the company may get to the self-sustaining stage.

Its not a new idea

The idea of companies involving themselves in a portfolio of early stage prospects isn’t new.  This has been being done by EMX Royalty Corp (formerly Eurasian Minerals) for properties around the globe.    Abitibi Royalties is also doing something vaguely similar, whereby they would help fund prospectors in exchange for a long term royalty on a property. There are likely others.
There is a high risk to being successful but the cost of entry is relatively low.
It will be interesting to watch Globex over the longer term to see how many properties they can acquire and how many of these will pay off. Spending a bit of money on mapping and exploration on a property may benefit them by increasing value in the eyes of potential partners.
Statistically, mineral exploration is a high risk game but by limiting expenditures and diversifying the portfolio, some of that risk can be mitigated.
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Ore Stockpiling – Why are we doing this again?

ore stockpile
In many of the past mining studies that I have worked, stockpiling strategies were discussed and usually implemented. However sometimes team members were surprised at the size of the stockpiles that were generated by the production plan. In some cases it was apparent that not all team members were clear on the purpose of  stockpiling or had preconceived ideas on the rationale behind it. To many stockpiling may seem like a good idea until they saw it in action.
Mine Stockpile
In this blog I won’t go into all the costs and environmental issues associated with stockpile operation.  The discussion focuses on the reasons for stockpiling and why stockpiles can get large in size or numerous in quantity.
In my experience there are four main reasons why ore stockpiling might be done. They are:
1. Campaigning: For metallurgical reasons if there are some ore types that can cause process difficulties if mixed  with other ores. The problematic ore might be stockpiled until sufficient inventory allows one to process that ore (i.e. campaign) through the mill. Such stockpiles will only grow as large as the operator allows them to grow. At any time the operator can process the material and deplete the stockpile. Be aware that mining operations might still be mining other ore types, then those ores may need to be stockpiled during the campaigning.  That means even more ore stockpiles at site.
2. Grade Optimization: This stockpiling approach is used in situations where the mine delivers more ore than is required by the plant, thereby allowing the best grades to be processed directly while lower grades are stockpiled for a future date. Possibly one or more grade stockpiles may be used, for example a low grade and a medium-low grade stockpile. Such stockpiles may not get processed for years, possibly until the mine is depleted or until the mined grades are lower than those in the stockpile. Such stockpiles can grow to enormous size if accumulated over many years.  Oxidation and processability may be a concern with long term stockpiles.
3. Surge Control: Surge piles may be used in cases where the mine may have a fluctuating ore delivery rate and on some days excess ore is produced while other days there is underproduction. The stockpile is simply used to make up the difference to the plant to provide a steady feed rate. These stockpiles are also available as short term emergency supply if for some reason the mine is shut down (e.g. extreme weather). In general such stockpiles may be relatively small in size since they are simply used for surge control.
4. Blending: Blending stockpiles may be used where a processing plant needs a certain quality of feed material with respect to head grade or contaminant ratios (silica, iron, etc.). Blending stockpiles enables the operator to ensure the plant feed quality to be within a consistent range. Such stockpiles may not be large individually; however there could be several of them depending on the nature of the orebody.
There may be other stockpiling strategies beyond the four listed above but those are the most common.

Test Stockpiling Strategies

Using today’s production scheduling software, one can test multiple stockpiling strategies by applying different cutoff grades or using multiple grade stockpiles. The scheduling software algorithms determine whether one should be adding to stockpile or reclaiming from it. The software will track grades in the stockpile and sometimes be able to model stockpile balances assuming reclaim by average grade, or first in-first out (FIFO), or last in-first out (LIFO).
ore stockpile
Stockpiling in most cases provides potential benefits to an operation and the project economics. Even if metallurgical blending or ore campaigning is not required, one should always test the project economics with a few grade stockpiling scenarios.
Unfortunately these are not simple to undertake when using a manual scheduling approach and so are a reason to move towards automated scheduling software.
Make sure everyone on the team understands the rationale for the stockpiling strategy and what the stockpiles might ultimately look like. They might be surprised.
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Higher Metal Prices – Should Miners Lower the Cut-Off Grade?

When metals prices are high, we are generally told that we should lower the cutoff grade. Our cutoff grade versus metal price formula tells us this is the correct thing do. Our grade-tonnage curve reaffirms this since we will now have more metal in the mineral reserve.

But is lowering the cutoff grade the right thing?

Books have been written on the subject of cutoff grades where readers can get all kinds of detailed logic and calculations using Greek symbols (F = δV* − dV*/dT). Here is one well known book by Ken Lane, available on Amazon HERE.
Recently we have seen a trend of higher cash costs at operating mines when commodity prices are high. Why is this?
It may be due to higher cost operating inputs due to increasing labour rates or supplies. It may also be partly due to the lowering of cutoff grades.  This lowers the head grade, which then requires more tonnes to be milled to produce the same quantity of metal.
A mining construction manager once said to me that he never understood us mining guys who lower the cutoff grade when gold prices increase. His concern was that since the plant throughput rate is fixed, when gold prices are high we suddenly decide to lower the head grade and produce fewer and higher cost ounces of gold.

Do the opposite

His point was that we should do the opposite.  When prices are high, we should produce more ounces of gold, not fewer. In essence, periods when supply is low (or demand is high) may not be the right time to further cut  supply by lowering head grades.
Now this is the point where the grade-tonnage curve comes into play.
Certainly one can lower the cutoff grade, lower the head grade and produce fewer ounces of gold.  The upside being an extension in the mine life.  A company can report more ounces in reserves and perhaps the overall image of the company looks better (if it is being valued on reserves).

What if metal prices drop back?

The problem is that there is no guarantee that metal prices will remain where they are and the new lower cutoff grade will remain where it is. If the metal prices drop back down, the cutoff grade will be increased and the mineral reserve will revert back to where it was. All that was really done was accept a year of lower metal production for no real long term benefit.
This trade-off  contrasts a short term vision (i.e. maximizing annual production) against a long term vision (i.e. extending mineral reserves).

Conclusion

The bottom line is that there is no simple answer on what to do with the cutoff grades.  Hence there is a need to write books about it.
Different companies have different corporate objectives and each mining project will be unique with regards to the impacts of cutoff grade changes on the orebody.
I would like to caution that one should be mindful when plugging in new metal prices, and then running off to the mine operations department with the new cutoff grade. One should fully understand both the long term and short term impacts of that decision.
In another blog post on the cutoff grade issue, I discuss whether in poly-metallic deposits the cutoff should be based on metal equivalent or block NSR value.  Neither approach is perfect, but I prefer the NSR option.  You can read that post at “Metal Equivalent Grade versus NSR for Poly-Metallics“.

 

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Resource Estimates – Are Independent Audits A Good Idea?

mining reserves
Question: How important is the integrity of a tailings dam to the successful operation of a mine?
Answer: Very important.
Tailings dam stability is so important that in some jurisdictions regulators may be requiring that mining companies have third party independent review boards or third party audits done on their tailings dams.  The feeling is that, although a reputable consultant may be doing the dam design, there is still a need for some outside oversight.
Differences in interpretation, experience, or errors of omission are a possibility regardless of who does the design.  Hence a second set of eyes can be beneficial.

Is the resource estimate important?

Next question is how important is the integrity of the resource and reserve estimate to the successful operation of a mine?
Answer: Very important.  The mine life, project economics, and shareholder value all rely on it.     So why aren’t a second set of eyes or third party audits very common?

NI 43-101 was the first step

In the years prior to 43-101, junior mining companies could produce their own resource estimates and disclose the results publicly.  With the advent of NI 43-101, a second set of eyes was introduced whereby an independent QP  could review the company’s internal resource and/or prepare their own estimate.  Now the QP ultimately takes legal responsible for the estimate.
Nowadays most small companies do not develop their own in-house resource estimates.  The task is generally awarded to an independent QP.

Resource estimation is a special skill

Possibly companies don’t prepare their own resource estimates due to the specialization needed in modelling and geostatistics. Maybe its due to the skills needed to operate block modeling software.   Maybe the companies feel that doing their own internal resource estimate is a waste of time since an independent QP will be doing the work anyway.

The QP is the final answer..or is it?

Currently it seems the project resource estimate is prepared solely by the QP or a team of QP’s.   In most cases this resource gets published without any other oversight. In other words no second set of eyes has taken a look at it.  We assume the QP is a qualified expert, their judgement is without question, and their work is error free.

Leapfrog Model

As we have seen, some resources estimates have been mishandled and disciplinary actions have been taken against QP’s.   The conclusion is that not all QP’s are perfect.
Just because someone meets the requirements to be a Competent Person or a Qualified Person does not automatically mean they are competent or qualified. Geological modeling is not an exact science and will be based on their personal experience.

What is good practice?

The question being asked is whether it would be good practice for companies to have a second set of eyes take a look at their resource estimates developed by independent QP’s?
Where I have been involved in due diligence for acquisitions or mergers, it is not uncommon for one side to rebuild the resource model with their own technical team.  They don’t have 100% confidence in the original resource handed over to them.   The first thing asked is for the drill hole database.
One downside to a third party review is the added cost to the owner.
Another downside is that when one consultant reviews another consultant’s work there is a tendency to have a list of concerns. Some of these may not be material, which then muddles the conclusion of the review.
On the positive side, a third party review may identify serious interpretation issues or judgement decisions that could be fatal to the resource.
If tailings dams are so important that they require a second set of eyes, why not the resource estimate?  After all, it is the foundation of it all.
Note: If you would like to get notified when new blogs are posted, then sign up on the KJK mailing list on the website.  Otherwise I post notices on LinkedIn, so follow me at: https://www.linkedin.com/in/kenkuchling/.
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