Articles tagged with: 43-101

Resources, Resources, and Mineral Reserves

Every so often I like to comment on issues related to the way the mining industry does things. This is one of those posts.
Currently the mining industry reports their exploration results as either Mineral Resources or Mineral Reserves. In my opinion, these two categories do not adequately reflect the reality of the current mining environment. I would suggest using a three category approach, as will be described below.
The implementation of this approach would not result in any more technical effort. However, it would provide clarity for stakeholders and investors and compare companies on a more equitable basis.

The issue

In today’s world, it is an onerous task to permit, finance, build, and operate a new mine. This is a significant achievement.
An operating company will be generating revenue and should be recognized for that big step. Hence does it make sense for an operating company to report Mineral Reserves while a junior company that has simply completed a pre-feasibility study to also report Mineral Reserves?
Both companies could report identical Reserves, but those reserves would not be the same thing. One company has built a mine while the other may have spent a few months doing a paper study. One company’s reserves will actually be mined in the foreseeable future while the other company’s project may never see the light of day. Yet both companies are allowed to present the same Mineral Reserves.
As a mine operates, the remaining ore reserves will deplete over time. However, a company can add to their reserves by finding satellite ore bodies or converting inferred material into a higher classification. The net of these adjustments will be reflected in the corporate Mineral Reserve Statement for all their operations.
A company can also increase the corporate Mineral Reserves simply by completing a pre-feasibility or feasibility study on a new project. However, is this a true reflection of the Reserves upon which the company should be evaluated?

Suggestion

I would suggest that the three reporting categories be used instead of two, described as follows:
1 – Mineral Resources (insitu): This category is the same as the current Mineral Resources being reported according to NI43-101. It is based on reasonable prospects for economic extraction. Hence open pit resources would be reported within an optimized shell and underground reserves within approximate stope shapes. No external dilution or mining criteria would be applied, as is the current approach.
2 – Economic Resources: This would be a new category that would simply be the outcome from a pre-feasibility or feasibility study, which is currently being labelled a “Mineral Reserve”. This Economic Resource would incorporate mining criteria, Measured & Indicated classes only, a mine plan, and an economic analysis. The differentiation from Reserves is because the mine is not built yet.
3 – Mineral Reserves: This highest-level category could be reported only once a mine has reached commercial production. The Economic Resources would automatically convert to Mineral Reserves once production is achieved. As the mine continues to operate, and as new ore sources are identified, the Mineral Reserves would increase / decrease. The Mineral Reserves would represent the remaining ore tonnage at operating mines and only that.
This three-category approach would help separate mine operators from junior development companies. The industry should recognize the difference between companies and projects at different life-cycle stages and that they are not all directly comparable. A junior explorer could be reporting huge reserves, but without a mine being there, should that company be compared to a mine operator that has similar reserves?
This approach would identify situations whereby a company suddenly reports a sizeable increase in Reserves. Is it because they found more ore at an existing operation (a great event) or because they did a paper study on a new project?
As a clarification, if a mine gets placed onto care & maintenance, likely due to poor economics, then the remaining tonnes at the mine would no longer be considered Mineral Reserves and may have to revert to Economic Resources, although even that would be questionable.

Examples

Out of curiosity I randomly selected three companies (Yamana Gold, Eldorado Gold, Alamos Gold) to compare their total Mineral Reserve tonnages based on their operations versus study stage development projects. The results are show in the images below. The percentage of Reserves provided by their producing (P) mines varied and ranged from 14% to 51%. A significant proportion of their Reserves (49% to 86%) are still at the development (D) stage. One or two large study-stage projects can boost the corporate reserves significantly. This is not immediately evident when looking at the total Mineral Reserves being reported.
For most junior miners 100% of their Reserves are still at the study-stage. They should not be able to declare Mineral Reserves and appear on an equal footing with mine operators. Their company should only be comparable to other companies with advanced study-stage projects.

Conclusion

The foregoing discussion is a suggestion as to how the mining industry can recognize the achievement and economic reality of building a mine, i.e. by being allowed to report Mineral Reserves. All others only get to report Resources. This would help clarify what long term tonnages are actually being mined versus simply being studied on paper.
The suggested approach does not create additional work for the mining companies. However, it provides a much fairer and transparent comparison between companies.
Interestingly, NI43-101 specifies that one cannot mathematically add together Indicated and Inferred resources because they are view as materially different. However, in a corporate Mineral Reserve Statement one is allowed to combine Reserves at an operating mine with Reserves from a study.  These two reserves, in my view, are even more materially different than Indicated and Inferred resources are.
Its great for a company to report Mineral Reserves from a pre-feasibility study.  However if for some reason that mine never gets built, then those Reserves are valueless. Maybe years ago it was foregone conclusion that a positive feasibility study would result in the construction of a mine, so the risk was less. That is no longer the case and this fact should be recognized when defining and reporting Mineral Reserves.
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Let A.I. Help Target Your Infill Drilling

From time to time I come across interesting new tech that I like to share with colleagues.  The topic of this blog relates to solving the problem of defining an optimal infill drill program.
In the past I have worked on some PEA’s whose economics were largely based on Inferred ore.  The company wanted to advance to the Pre-Feasibility (PFS) stage. However, before the PFS could start they would need additional drilling to convert much of the Inferred resource into Measured and Indicated resources.
I’ve seen similar experience with projects that are advance from PFS to FS where management has a requirement that the ore mined during the payback period consist of Measured classification.

The Problem

In both cases described above, it is necessary for someone to outline an infill drill program to upgrade the resource classification while also meeting other project priorities.  The goal is to design an infill drill program with minimal time and cost yet maximize resource conversion.  Possibly some resource expansion drilling, metallurgical sampling, and geotechnical investigations may be required at the same time.
I’m not certain how various resource geologists go about designing an infill drill plan.  However, I have seen instances where dummy holes were inserted into the block model and then the classification algorithm was re-run to determine the new block model tonnage classification.   If it didn’t meet the corporate objectives, then the dummy holes may be moved or new ones added, and the process repeated.
One would not consider such a trial & error solution as optimal. It may not necessarily meet the cost and time objectives although it may meet the resource conversion goals.

The Solution

The DRX Drill Hole and Reporting algorithm developed by Objectivity.ca uses artificial intelligence to optimize the infill drilling layout.  It intends to match the QP/CP constraints with corporate/project objectives.
For example, does company management require 70% of the resource in M&I classifications or do they require 90% in M&I?  Each goal can be achieved with a different drill plan.
The following description of DRX is based on discussions with the Objectivity staff as well as a review of some case studies.  The company is willing to share these studies if you contact them.
The DRX algorithm relies on the resource classification criteria specified by the company QP.  For example, the criteria could be something like “For a block to qualify as Measured, the average distance to the nearest three drill holes must be 30 m or less of the block centroid. For a block to qualify as Indicated, the average distance from the block centroid to the nearest three holes must be 50 m or less. For a block to qualify as Inferred it will generally be within 100 m laterally and 50 m vertically of a single drill hole.
The DRX algorithm will use these criteria to optimize drill hole placement three dimensionally to deliver the biggest bang for the buck.   Whatever the corporate objective, DRX will attempt to find an optimal layout to achieve it.  The idea being that fewer well targeted holes may deliver a better value than a large manually developed drill program.
The DRX outcome will prioritize the hole drilling sequence in case the drill program gets cut short due to poor weather, lack of funding, or the arrival of the PDAC news cycle.
The DRX approach can also be used to optimally site metallurgical holes and/or geotechnical holes in combination with resource drilling if there are defined criteria that must be met (by location, ore type, rock type, etc.).   The algorithm will rely on rules and search criteria developed by experts in those disciplines.  It does not develop the rules, it only applies them.
DRX can also help optimize step-out drilling, such that the step-out distance will not be beyond the range that negates the use of the hole in a resource estimate.  It can also consider geological structure in defining drill targets.

By optimizing the number of drill holes and their orientation, the company may see savings in drill pad prep, drilling costs, field support costs, and sample assaying.
One can even request drilling multiple holes from the same drill pad to minimize drill relocation costs and safety issues in difficult terrain.
A large benefit of DRX is to be able to examine what-ifs.  For example, one may desire 85% of the resource to be M&I.   However, if one is willing to accept 80%, then one may be able to save multiple holes and associated costs.   Perhaps with the addition of just a few extra holes one could get to 90% M&I.   These are optimizations that can be evaluated with DRX.

An Example

In the one case study provided to me, a $758,000 manually developed drill program would convert 96.6% of the Inferred resource to Indicated.  DMX suggested that they could achieve 96.7% for $465,000. Alternatively they could achieve 94% conversion for $210,000.  These are large reductions in drilling cost for small reductions in conversion rate.  This may allow the drill-metres saved to be used for other purposes.
For that same project, a subsequent study was done to convert Indicated to Measured in a starter pit area. DRX concluded that a 5000-metre program could convert 62% of Indicated into Measured.  A 12,000-metre program would convert 86%,  A 16,000-metre program would achieve 92%.
So now company management can make an informed decision on either how much money they wish to spend or how much Measure Resource they want to have.

Conclusion

Although I have not yet worked with DRX, I can see the value in it.   I look forward to one day applying it on a project I’m involved with to develop a better understanding of what goes in and what comes out.   DRX hopes to become to resource drilling what Whittle has become to pit design – an industry standard.
The use of the DRX algorithm may help mitigate situations where, moving from a PEA to PFS, one finds that the infill program did not deliver as hoped on the resource conversion.  Unfortunately, this leaves the PFS with less mineable ore than anticipated and sub-optimal economics.
New tech is continually being developed in the mining industry.  Hopefully this is one we continue to see forward advancement. It makes sense to me and DRX could be another tool in the geologist toolbox.  Check out their website at objectivity.ca
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Mining Financial Modeling – Make it Better!

In my view one thing lacking in the mining industry today is a consistent approach to quantifying and presenting the risks associated with mining projects. In a blog written in 2015, I discussed the limitations of the standard “spider graph” sensitivity analysis (blog link here) often seen in Section 22 of 43-101 reports. This new blog expands on that discussion by describing a preferred approach. A six-year time gap between the two articles – no need to rush I guess.
This blog summarizes excerpts from an article written by a colleague that specializes in probabilistic financial analysis. That article is a result of conversations we had about the current methods of addressing risk in mining. The full article can be found at this link, however selected excerpts and graphs have been reprinted here with permission from the author.
The author is Lachlan Hughson, the Founder of 4-D Resources Advisory LLC. He has a 30-year career in the mining/metals and oil gas industry as an investment banker and a corporate executive. His website is here 4-D Resources Advisory LLC.

Excerpts from the article

Mining can be risky

“The natural resources industry, especially the finance function, tends to use a static, or single data estimate, approach to its planning, valuation and M&A models. This often fails to capture the dynamic interrelationships between the strategic, operational and financial variables of the business, especially commodity price volatility, over time.”
“A comprehensive financial model should correctly reflect the dynamic interplay of these fundamental variables over the company life and commodity price cycles. This requires enhancing the quality of key input variables and quantitatively defining how they interrelate and change depending on the strategy, operational focus and capital structure utilized by the company.”
“Given these critical limitations, a static modeling approach fundamentally reduces the decision making power of the results generated leading to unbalanced views as to the actual probabilities associated with expected outcomes. Equally, it creates an over-confident belief as to outcomes and eliminates the potential optionality of different courses of action as real options cannot be fully evaluated.”

Monte Carlo can be risky

“Fortunately, there is another financial modeling method – using Monte Carlo simulation – which generates more meaningful output data to enhance the company’s decision making process.”
Monte Carlo simulation is not new.  For example  @RISK has been available as an easy to use Excel add-in for decades. Crystal Ball does much the same thing.
“Dynamic, or probabilistic, modeling allows for far greater flexibility of input variables and their correlation, so they better reflect the operating reality, while generating an output which provides more insight than single data estimates of the output variable.”
“The dynamic approach gives the user an understanding of the likely output range (presented as a normal distribution here) and the probabilities associated with a particular output value. The static approach is relatively “random” as it is based on input assumptions that are often subject to biases and a poor understanding of their potential range vs. reality (i.e. +/- 10%, 20% vs. historical or projected data range).”
“In the case of a dynamic model, there is less scope for the biases (compensation, optionality, historic perspective, desire for optimal transaction outcome) that often impact the static, single data estimates modeling process. Additionally, it imposes a fiscal discipline on management as there is less scope to manipulate input data for desired outcomes (i.e. strategic misrepresentation), especially where strong correlations to historical data exist.”
“It encourages management to consider the likely range of outcomes, and probabilities and options, rather than being bound to/driven by achieving a specific outcome with no known probability. Equally, it introduces an “option” mindset to recognize and value real options as a key way to maintain/enhance company momentum over time.”

Image from the 4-D Resources article

“In the simple example (to the right), the financial model was more real-world through using input variables and correlation assumptions that reflect historical and projected reality rather than single data estimates that tend towards the most expected value.”
“Additionally, the output data provide greater insight into the variability of outcomes than the static model Downside, Base and Upside cases’ single data estimates did.”
The tornado diagram, shown below the histogram, essentially is another representation of the spider diagram information. ie.e which factors have the biggest impact.
“The dynamic data also facilitated the real option value of the asset in a manner a static model cannot. And the model took less time to build, with less internal relationships to create to make the output trustworthy, given input variables and correlation were set using the @RISK software options. This dynamic modeling approach can be used for all types of financial models.”
To read the full article, follow this link.

Conclusion

image from 4-D Resources article

Improvements are needed in the way risks are evaluated and explained to mining stakeholders. Improvements are required given increasing complexity in the risks impacting on decision making.
The probabilistic risk evaluation approach described above isn’t new and isn’t that complicated. In fact, it can be very intuitive when undertaken properly.
Probabilistic risk analysis isn’t something that should only be done within the inner sanctums of large mining companies. The approach should filter down to all mining studies and 43-101 reports. It should ultimately become a best practice or standard part of all mining project economic analyses. The more often the approach is applied, the sooner people will become familiar (and comfortable) with it.
Mining projects can be risky, as demonstrated by the numerous ventures that have derailed. Yet recognition of this risk never seems to be brought to light beforehand. Essentially all mining projects look the same to outsiders from a risk perspective, when in reality they are not. The mining industry should try to get better in explaining this.
UPDATE:  For those interesting in this subject, there is a follow up article by the same author published in January 2022 titled “Using Dynamic Financial Modeling to Enhance Insights from Financial Reports!“.
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Pit Optimization – More Than Just a “NPV vs RF” Graph

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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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.

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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Are Engineers Too Pessimistic

Geological colleagues have often joked that engineers are a pessimistic lot; they are never technically satisfied. The engineers will fire back that geologists are an overly optimistic lot; every speck of mineralization makes them ecstatic. Together they make a great team since each cancels the other out.
In my opinion engineers are often pessimistic. This is mainly because they have been trained to be that way. Throughout my own engineering career I have been called upon many times to focus on the downsides, i.e. what can happen that we don’t want to happen.

It starts early and continues on

This pessimism training started early in my career while working as a geotechnical engineer. Geotechnical engineers were always looking at failure modes and the potential causes of failure when assessing factors of safety.
Slope failure could be due to the water table, excess pore pressures, seismic or blast vibrations, liquefaction, unknown weak layers, overly steepen slopes, or operating error. As part of our job we had to come up with our list of negatives and consider them all. The more pessimistic view you had, the better job you did.
This training continued through the other stages of a career. The focus on negatives continues in mine planning and costing.
For example, there are 8,760 hours in a year, but how many productive hours will each piece of equipment provide? There will delays due to weather conditions, planned maintenance, unplanned breakdowns, inter-equipment delays, operator efficiency, and other unforeseen events. The more pessimistic a view of equipment productivity, the larger the required fleet. Geotechnical engineers would call this the factor of safety.
In the more recent past, I have been involved in numerous due diligences. Some of these were done for major mining companies looking at acquisitions. Others were on behalf of JV partners, project financiers, and juniors looking at acquisitions.
When undertaking a due diligence, particularly for a major company or financier, we are not hired to tell them how great the project is. We are hired to look for fatal flaws, identify poorly based design assumptions or errors and omissions in the technical work. We are mainly looking for negatives or red flags.
Often we get asked to participate in a Risk Analysis or SWOT analysis (Strengths-Weaknesses-Opportunities-Threats) where we are tasked with identifying strengths and weaknesses in a project.
Typically at the end of these SWOT exercises, one will see many pages of project risks with few pages of opportunities.
The opportunities will usually consist of the following cliches (feel free to use them in your own risk session); metal prices may be higher than predicted; operating costs will be lower than estimated; dilution will be better than estimated; and grind size optimization will improve process recoveries.
The project’s risk list will be long and have a broad range. The longer the list of risks, the smarter the review team appears to be.

Investing isn’t easy

After decades of the training described above, it becomes a challenge for me to invest in junior miners. My skewed view of projects carries over into my investing approach, whereby I tend to see the negatives in a project fairly quickly. These may consist of overly optimistic design assumptions or key technical aspects not understood in sufficient depth.
Most 43-101 technical reports provide a lot of technical detail; however some of them will still leave me wanting more. Most times some red flags will appear when first reviewing these reports. Some of the red flags may be relatively inconsequential or can be mitigated. However the fact that they exist can create concern. I don’t know if management knows they exists or knows how they can mitigate them.
It has been my experience that digging in a data room or speaking with the engineering consultants can reveal issues not identifiable in a 43-101 report. Possibly some of these issues were mentioned or glossed over in the report, but you won’t understand the full extent of the issues until digging deeper.
43-101 reports generally tell you what was done, but not why it was done. The fact I cannot dig into the data room or speak with the technical experts is what has me on the fence. What facts might I be missing?
Statistics show that few deposits or advanced projects become real mines. However every advanced study will say that this will be an operating mine. Many projects have positive feasibility studies but these studies are still sitting on the shelf. Is the project owner a tough bargainer or do potential acquirers / financiers see something from their due diligence review that we are not aware of?   You don’t get to see these third party reviews unless you have access to the data room.
My hesitance in investing in some companies unfortunately can be penalizing. I may end up sitting on the sidelines while watching the rising stock price. Junior mining investors tend to be a positive bunch, when combined with good promotion can result in investors piling into a stock.
Possibly I would benefit by putting my negatives aside and instead ask whether anyone else sees these negatives. If they don’t, then it might be worth taking a chance, albeit making sure to bail out at the right time.
Often newsletter writers will recommend that you “Do your own due diligence”. Undertaking a deep dive in a company takes time. In addition I’m not sure one can even do a proper due diligence without accessing a data room or the consulting team. In my opinion speaking with the engineering consultants that did the study is the best way to figure things out. That’s one reason why “hostile” due diligences can be difficult, while “friendly” DD’s allow access to a lot more information.

Conclusion

Sometimes studies that I have been involved with have undergone third party due diligence. Most times one can predict ahead of time which issues will be raised in the review. One knows how their engineers are going to think and what they are going to highlight as concerns.
Most times the issue is something we couldn’t fully address given the level of study. We might have been forced to make best guess assumptions to move forward. The review engineers will have their opinions about what assumptions they would have used. Typically the common comment is that our assumption is too optimistic and their assumption would have been more conservative or realistic (in their view).
Ultimately if the roles were reversed and I were reviewing the project I may have had the same comments. After all, the third party reviewers aren’t being hired to say everything is perfect with a project.
The odd time one hears that our assumption was too pessimistic. You usually hear this comment when the reviewing consultant wants to do the next study for the client. They would be a much more optimistic and accommodating team.
To close off this rambling blog, the next time you feel that your engineers are too negative just remember that they are trained to be that way.  If you want more positivity, hang out with a geologist (or hire a new grad).

 

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Heap Leach or CIL or Maybe Both

Typically gold mines consist of either a heap leach (HL) operation or a CIL type plant. There are a few projects that operate (or are considering) concurrent heap leach and CIL operations. Ultimately the mineral resource distribution determines if it makes economic sense to have both.  This blog discusses this concept based on past experience.
A CIL operation has higher capital and operating costs than a heap leach. However that higher cost is offset by achieving improved gold recovery, perhaps 20-30% higher. At higher gold prices or head grades, the economic benefit from improved CIL recovery can exceed the additional cost incurred to achieve that recovery.

Some background

Several years ago I was VP Engineering for a Vancouver based junior miner (Oromin Expl) who had a gold project in Senegal. We were in the doldrums of Stage 3 of the Lassonde Curve (read this blog to learn what I mean) having completed our advanced studies. Our timeline was as follows.
Initially in August 2009 we completed a Pre-Feasibility Study for a standalone CIL operation. Subsequently in June 2010 we completed a Feasibility Study. The technical aspects of Stage 2 were done and we were entering Stage 3. Now what do we do? Build or wait for a sale?
The property’s next door neighbor was the Teranga Sabodala operation. It made sense for Teranga to acquire our project to increase their long term reserves. It also made sense for a third party to acquire both of us. The Feasibility Study also made the economic case to go it alone and build a mine.
While waiting for various third-party due diligences to be completed, the company continue to do exploration drilling. There were still a lot of untested showings on the property and geologists need to stay busy.
Two years later in 2013 we completed an update to the CIL Feasibility Study based on an updated resource model. Concurrently our geologists had identified seven lower grade deposits that were not considered in the Feasibility Study.
These deposits had gold grades in the range of 0.5 to 0.7 g/t compared to 2.0 g/t for the deposits in the CIL Feasibility Study. We therefore decided to also complete a Heap Leach PEA in 2013, looking solely on the lower grade deposits.
These HL deposits were 2-8 km from the proposed CIL plant so their ore could be shipped to the CIL plant if it made economic sense. Test work had indicated that heap leach recoveries could be in the range of 70% versus >90% with a CIL circuit. The gold price at that time was about $ 1,100/oz.
Ultimately our project was acquired by Teranga in the middle of 2013.

Where should the ore go?

With regards to the Heap Leach PEA, we did not wish to complicate the Feasibility Study by adding a new feed supply to that plant from mixed CIL/HL pits. The heap leach project was therefore considered as a separate satellite operation.
The assumption was that all of the low grade pit ore would go only to the heap leach facility. However, in the back of our minds we knew that perhaps higher grade portions of those deposits might warrant trucking to the CIL plant.
For internal purposes, we started to look at some destination trade-off analyses. We considered both hard (fresh rock) and soft ore (saprolite) separately. CIL operating costs associated with soft ore would be lower than for hard ore. Blasting wasn’t required and less grinding energy is needed. The CIL plant throughput rate could be 30-50% higher with soft ore than with hard ore, depending on the blend.
I have updated and simplified the trade-off analysis for this blog. Table 1 provides the costs and recoveries used herein, including increasing the gold price to $1500/oz.
The graph shows the profit per tonne for CIL versus HL processing methods for different head grades.
The cross-over point is the head grade where profit is better for CIL than Heap Leach. For soft ore, this cross-over point is 0.53 g/t. For hard ore, this cross over point is at 0.74 g/t.
The cross-over point will be contingent on the gold price used, so a series of sensitivity analyses were run.
The typical result, for hard ore, is shown in Table 2. As the gold price increases, the HL to CIL cross-over grade decreases.
These cross-over points described in Table 2 are relevant only for the costs shown in Table 1 and will be different for each project.

Conclusion

It may make sense for some deposits to have both CIL and heap leach facilities. However one should first examine the trade-off for the CIL versus HL to determine the cross-over points.
Then confirm the size of the heap leach tonnage below that cross-over point. Don’t automatically assume that all lower grade ore is optimal for the heap leach.
If some of the lower grade deposits are further away from the CIL plant, the extra haul distance costs will tend to raise their cross-over point. Hence each satellite pit would have its own unique cross-over criteria and should be examined individually.
Since Teranga complete the takeover in mid 2013, we were never able to pursue these trade-offs any further.
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The Lassonde Curve – A Wild Ride

Normally I don’t write about mining stock markets, preferring instead to focus on technical matters.  However I have seen some recent discussions on Twitter about stock price trends.  For every stock there are a wide range of price expectations.  Ultimately some of the expectations and realizations can be linked back to the Lassonde Curve.
The Lassonde Curve has been touted by many as a realistic representation of the life stages of a junior mining company.  The curve can sometimes be a roller coaster ride for company management.
Pierre Lassonde, was one of the founders of Franco-Nevada, the first gold royalty company. Thirty years ago he created his curve, that has now become a foundation in the junior mining business.  The Lassonde Curve outlines the company life stages, beginning at exploration and ending at production.  It shows the perceived value (i.e. stock value) that investors may assign at each life stage.
The stock price trend illustrated by the curve can, knowingly or unknowingly, impact on a company’s decision making process.  So in effect, there are some technical ramifications from it.
People may have differing opinions on what factors are driving the curve.   Take a look at it and decide for yourself. Typically people define the curve into four life stages, but I tend to view it in five stages.

Stages 1 to 5

Stage 1 Climb

Stage 1 is the earliest stage, consisting of exploration.  This period generates rising anticipation from promotion and exciting press releases. The stock value climbs as the perceived value of the insitu geology increases.  Great Bear is an example of company currently in Stage 1 (as of June 2020), and appears to be in no hurry to exit from Stage 1.
Stage 2 is when the prospect moves into technical evaluation.  In other words, the engineers now climb aboard the ride.  This stage encompasses the PEA, PFS, and FS studies. Each of these can take months to complete, meantime new information releases may be lacking.
If the stock value declines, perhaps its because the engineers bring reality into the picture.  Investors may see that the project isn’t as easy or great as they anticipated during Stage 1.
Companies can also lose some presence in the market with no new news. Investors may begin looking at other companies that are still in Stage 1 and hence sell their shares.
Some companies may try to shorten Stage 2 and even skip over Stage 3 by going from a PEA directly into Stage 4 construction.
Stage 3 is the period when the studies have largely been completed and a production decision is pending.  At this time the company will be seeking strategic partners and project funding.  Permitting is also underway.  Unfortunately a lack of financing or poor permitting efforts will extend the time in Stage 3, which can extend for decades or even perpetuity. It’s easy to rattle off the names of companies sitting in Stage 3; for example Donlin Creek, Casino, KSM, and Galore Creek. It seems that once locked in a prolonged Stage 3, it can be difficult to get out of it.  Company promotion and marketing can be difficult.
Stage 4 begins when the financing is done and construction begins.  This is a sign that the project has been figured out, permits approved, and third-party due diligence found no fatal flaws.  The stock value may increase on this positive news, especially if construction is on time and on budget. Its even better news if it’s a period of rising commodity prices.
Stage 5 is the start-up and commercial production period, possibly nerve-racking for some investors. This is where the rubber hits the road. The stock price can fall if milled grades, operating costs, or production rates are not as expected.
Investors may need to decipher press releases to figure out if things are going well or not.  Some investors may now bail out at this time to companies in Stage 1 for greater upside (the 10 bagger).

Staying front and center

Companies know that investors can move elsewhere at any time, so they will try to address the Stage 2 and Stage 3 doldrums in different ways.   They can:
  • Find new exploration prospects elsewhere while the engineering work is underway.
  • Undertake a series of optimization studies on the same project to keep up the news flow.
  • Continue step out drilling on the same property to expand resources and generate new excitement.
  • Have management appear regularly on podcasts, webinars, conferences, and keep promoting on LinkedIn, Twitter, and with newsletter writers.
Ideally one would like to stagger multiple prospects at different stages of the Curve. While this makes sense, it also takes a fair bit of funding to do it.   It also may bring criticism that the company is losing focus on their flagship project.  Generally if the stock price is improving, you don’t see this complaint.

Conclusion

In closing, I just wanted to present the Lassonde Curve for those who may not have seen it before. For those playing the junior stocks, it may help explain why their prices fluctuate for essentially the same project.  For some companies, the curve can be a wild ride.
Some corporate presentations will highlight the Lassonde Curve, particularly when they are rising in Stage 1.  You are less likely to see the curve presented when they are rolling along in Stages 2 or 3.
Some say the Curve relates to the de-risking of a project as it advances, with risks shifting from exploration related to development related.   That may be true, but I suggest the curve is simply based on investor perception and impatience.
The ability to promote oneself and stay relevant in the market plays a key role in defining the shape of a company’s curve.

 

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Climbing the Hill of Value With 1D Modelling

Recently I read some articles about the Hill of Value.  I’m not going into detail about it but the Hill of Value is a mine optimization approach that’s been around for a while.  Here is a link to an AusIMM article that describes it “The role of mine planning in high performance”.  For those interested, here is a another post about this subject “About the Hill of Value. Learning from Mistakes (II)“.
hill of value

(From AusIMM)

The basic premise is that an optimal mining project is based on a relationship between cut-off grade and production rate.  The standard breakeven or incremental cutoff grade we normally use may not be optimal for a project.
The image to the right (from the aforementioned AusIMM article) illustrates the peak in the NPV (i.e. the hill of value) on a vertical axis.
A project requires a considerable technical effort to properly evaluate the hill of value. Each iteration of a cutoff grade results in a new mine plan, new production schedule, and a new mining capex and opex estimate.
Each iteration of the plant throughput requires a different mine plan and plant size and the associated project capex and opex.   All of these iterations will generate a new cashflow model.
The effort to do that level of study thoroughly is quite significant.  Perhaps one day artificial intelligence will be able to generate these iterations quickly, but we are not at that stage yet.

Can we simplify it?

In previous blogs (here and here) I described a 1D cashflow model that I use to quickly evaluate projects.  The 1D approach does not rely on a production schedule, instead uses life-of-mine quantities and costs.  Given its simplicity, I was curious if the 1D model could be used to evaluate the hill of value.
I compiled some data to run several iterations for a hypothetical project, loosely based on a mining study I had on hand.  The critical inputs for such an analysis are the operating and capital cost ranges for different plant throughputs.
hill of valueI had a grade tonnage curve, including the tonnes of ore and waste, for a designed pit.  This data is shown graphically on the right.   Essentially the mineable reserve is 62 Mt @ 0.94 g/t Pd with a strip ratio of 0.6 at a breakeven cutoff grade of 0.35 g/t.   It’s a large tonnage, low strip ratio, and low grade deposit.  The total pit tonnage is 100 Mt of combined ore and waste.
I estimated capital costs and operating costs for different production rates using escalation factors such as the rule of 0.6 and the 20% fixed – 80% variable basis.   It would be best to complete proper cost estimations but that is beyond the scope of this analysis. Factoring is the main option when there are no other options.
The charts below show the cost inputs used in the model.   Obviously each project would have its own set of unique cost curves.
The 1D cashflow model was used to evaluate economics for a range of cutoff grades (from 0.20 g/t to 1.70 g/t) and production rates (12,000 tpd to 19,000 tpd).  The NPV sensitivity analysis was done using the Excel data table function.  This is one of my favorite and most useful Excel features.
A total of 225 cases were run (15 COG versus x 15 throughputs) for this example.

What are the results?

The results are shown below.  Interestingly the optimal plant size and cutoff grade varies depending on the economic objective selected.
The discounted NPV 5% analysis indicates an optimal plant with a high throughput (19,000 tpd ) using a low cutoff grade (0.40 g/t).  This would be expected due to the low grade nature of the orebody.  Economies of scale, low operating costs, high revenues, are desired.   Discounted models like revenue as quickly as possible; hence the high throughput rate.
The undiscounted NPV 0% analysis gave a different result.  Since the timing of revenue is less important, a smaller plant was optimal (12,000 tpd) albeit using a similar low cutoff grade near the breakeven cutoff.
If one targets a low cash cost as an economic objective, one gets a different optimal project.  This time a large plant with an elevated cutoff of 0.80 g/t was deemed optimal.
The Excel data table matrices for the three economic objectives are shown below.  The “hot spots” in each case are evident.

hill of value

hill of value

Conclusion

The Hill of Value is an interesting optimization concept to apply to a project.  In the example I have provided, the optimal project varies depending on what the financial objective is.  I don’t know if this would be the case with all projects, however I suspect so.
In this example, if one wants to be a low cash cost producer, one may have to sacrifice some NPV to do this.
If one wants to maximize discounted NPV, then a large plant with low opex would be the best alternative.
If one prefers a long mine life, say to take advantage of forecasted upticks in metal prices, then an undiscounted scenario might win out.
I would recommend that every project undergoes some sort of hill of value test, preferably with more engineering rigor. It helps you to  understand a projects strengths and weaknesses.  The simple 1D analysis can be used as a guide to help select what cases to look at more closely. Nobody wants to assess 225 alternatives in engineering detail.
In reality I don’t ever recall seeing a 43-101 report describing a project with the hill of value test. Let me know if you are aware of any, I’d be interested in sharing them.  Alternatively, if you have a project and would like me to test it on my simple hill of value let me know.
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Connecting With Investors – Any New Ideas?

I recently read some LinkedIn posts from junior mining executives and IR staff asking for ideas about new ways to engage with investors.  The commonly used ways rely on PowerPoints, webinars, and trade show booths.   However during this Covid-19 crisis, trade shows are no longer an option.  Therefore these face to face discussions with investors will now be missing.  This will impact on the ability of a company to connect with and establish trust with those people.

What else can be done?

Perhaps with technology, like Zoom, one can replicate the personal feel of a trade show booth. One can still have back and forth conversations with investors rather than just doing lecture style webinars.
Free discussion is good in most cases. Letting investors feel they are sitting around a table will give them a better understanding of how management thinks and how decisions are being made.  It will also help them get to know the personality of the management team.
I’m not an IR person but I admire the job they have to do, especially in today’s business environment.  I have recently sat in on several junior mining online webinars.  When listening to the Q&A’s afterwards, it is apparent that many attendees enjoyed understanding the technical aspects of a project.  However they will only get that understanding by asking questions.  Trade show booths gave them that opportunity.

Technology gives some options.  Like what?

Set up regularly scheduled Zoom meetings, enabling investors to have interactive back and forth conversations with management.  Try to avoid long presentations with questions only at the end. Have a moderator review and ask questions as they come in.
Management teams should introduce more than just the CEO or COO.  Include VP’s of geology, engineering, corporate development, from time to time.    Don’t hesitate to let the public meet more of your team.  Trade show booths are often manned by different team members.
Pick different topics for discussion on each conference call to avoid repeating the same PowerPoint over and over again.
Avoid being too scripted.
For example one call could be a fly-around of the property using Google Earth.  Another call could focus on the ore body and resource model.  Another call might discuss metallurgy and the thought process behind the flow sheet. Perhaps discuss the development options you have considered.
None of this information is likely confidential if it has been presented in your 43-101 report.
Companies file highly technical 43-101 reports on SEDAR, but then let the investors fend for themselves.   One could take some online time for high level walk through of the report.  Clearly explain technical issues and how they have been addressed or will be addressed in the future.  This is an opportunity to explain things in plain English, and field questions.
One downside to such calls is if there are significant flaws with a project.  Open discussions may help expose them.   One needs to know your own project well, be aware of all the issues, and have them under control in one way or another.

Conclusion

Better communication with investors can increase confidence in a management team.   Although some investors may not enjoy technical discussions, I think there is a subset that will find them very helpful and interesting.  There will likely be an audience out there.
Mining projects are complex with many moving parts and many uncertainties. Trust and confidence will come if a company is transparent in what they are doing and explain why they are doing it.
The mining industry is looking for new ways to reach out, so it shouldn’t be afraid to try new things. Some management teams will be great at it, others not so much.  Figure out where you fit in.
Unfortunately one of the aspects of trade shows that cannot be replicated is the ability for investors to wander around aimlessly, take a quick glance at a lot of companies, and then decide which ones they want to learn more about.

Warning: zoom bombing

As an aside, if you are using Zoom make sure the host has configured the right settings.  There are instances where anonymous participants can suddenly share their own computer screen, i.e. with questionable videos, to the group.  It’s been referred to as “zoom bombing”.
Read more about how to prevent zoom bombing at the following two links.
https://www.forbes.com/sites/leemathews/2020/03/21/troll-terrifies-zoom-meeting-zoombombing/#2765abfc3e70
https://www.businessinsider.com/zoom-settings-change-avoids-trolls-porn-2020-3
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Consultants Have to Earn a Living Too

The number of independent mining consultants is increasing daily as more people reach retirement age or are made redundant.
Nowadays it seems everyone is gradually becoming a self-employed consultant. Possibly that is because retirees need the money.  Maybe they need something post-career to keep them occupied.
Here are a couple of lesser known ways to generate income for those of you choosing this new career path.
One of these has been around for awhile while the other is relatively new.  I only have personal experience with one of them.

GLG – Give me an hour

GLG (https://glg.it/) is one of several information services that provide short term consulting assignments.  By short term, I mean 1 to 2 hours long.
GLG has been around for many years providing a platform for connecting those seeking information with those who have it.
Typically someone, like an industry analyst, poses a question that gets sent out to relevant experts.
The question could be something like “XX Mining Company owns the Bonanza mine and our client would like to learn more about that operation including reserves and operating costs”.
Anyone who has the requisite knowledge can accept the consultation and submit their credentials for review.  If you’re selected, such consultations take place very soon.  They can be for 1 to 2 hours and pay $200 to $500 dollars.  GLG are very strict that rumors or confidential information are not disclosed during any of the consultations.  Only public information is to be used.
Since I have a background in potash, I am often issued potash industry related requests.  Questions posed might be “Can you describe the Saskatchewan potash industry, including operations, expansions, marketing plans, and operating costs”.  That’s a heck of a lot of information to provide in a 1 to 2 hour time frame for $400.   Likely very few people would possess all of that knowledge.   I assume their approach is to consult with several different experts and eventually piece together the puzzle.
Check out the GLG website. It’s free to sign up as an expert and maybe you’ll get yourself an assignment. I think there is even a reward for referrals (which I assume I will get shortly).

Digbee – What’s wrong now?

Digbee (https://thedigbee.com/) is a relatively new online venture that I’ve not yet used.  It is essentially a due diligence platform where one can hire experts to undertake targeted due diligence studies.
Furthermore any expert can prepare an independent review on a topic of their choice and then offer it up for sale.
The typical report costs $1,640 to $4,680 dollars.   As of March 2020, they have 13 reports for sale and 5 more in the pipeline.  Here’s a brief explainer video from the founder
The report list can be seen at this link.  The reports appear to be focusing on potential technical flaws in a project. Some titles are listed below.
  • Sample recoveries at shallow depths is a concern, this is not helped by the total lack of QAQC data” an analysis of Bomboré ($4,680)
  • Has the extensive testwork at Bomboré finally found an optimum process to proceed to development?” ($4,680)
  • Alpala’s technical merits and compares its cost estimate to other block caving development projects.” ($3,120).
  • Cerro Blanco’s very complicated geology in Guatemala may mean more expensive mining techniques will be required.” ($4,680)
  • Epithermal geologist raises questions on the reliability of the low grade resource at DeLamar.” ($3,120)
  • What impact does serpentinisation have on the confidence of recovered grade at RNC’s Dumont project?” ($4,680)
I’m not sure how many report copies each consultant will be able to sell .  However a click-bait title may help sell at least one copy.  That would be to the company the report is about.   Perhaps major investors or financial analysts will also buy a copy.
So if you have some free time, pick a project that’s on your radar and write a review.   It appears that you’ll get a 50% share of the revenue.    To learn more, read an article at this link.
I’m curious if the Digbee platform will continue to grow.  It’s unique to see independent research identifying potential issues with mining projects. Someone jokingly mentioned that these are the anti-newsletter writers.  I’m also curious to see how long before the lawyers and lawsuits begin to show up.
Given the relatively low price for these reports, I think one might make a lot more money (from TMZ) if one wrote a report titled “Famous Hollywood starlet has scandalous affair with mining company CEO”.

Conclusion

If you’re becoming an independent consultant, check out these two revenue channels.   They are tailor made for our growing numbers.
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Consulting and Stock Compensation

The other day a press release came across my desk with the following title “First Mining Issues First Tranche Of Shares To Ausenco; Pre-Feasibility Study For Springpole Gold Project Underway”.
Reading it further, it was apparent that their study consultant, Ausenco, was being paid in company stock in lieu of cash.  The arrangement included an initial financing of $750k with a further $375k to follow once the pre-feasibility study was 75% complete.  Upon completion of the study another share payment was due.
That press release was interesting. I personally had never seen one like this before.
Some may see independence as an issue with their fiscal arrangement. Maybe… but this blog isn’t about the need for independent QP’s.  In fact I don’t recall feasibility studies having that requirement.  Some 43-101 resources estimates do require independence.
An industry discussion about where independence is required would be an interesting exercise.  However I will leave that conversation for a future post.

Would you work for company shares only?

I have never been in a situation where I was consulting with  company shares as my compensation.  Neither have I ever managed a study where outside consultants were being paid in shares.   However I can see the possibility of interesting dynamics at play.
In the past I have worked as an owner’s study manager and been awarded stock options along with salary.  In that role, my job was to look after the owner’s interests, pushing for cost efficiencies and optimizations.
Regarding share compensation, there are significant risks on the consultant’s side when they agree to be paid in shares.   I can see both positive and negative aspects with that type of a relationship.
I am not passing judgement here on what is right or wrong.  My objective is to comment on some basic issues that may arise.

Pro’s and Con’s

The positive aspects one might experience include;
  • It’s easier for the company to pay for the study since there are no cash outlays from the treasury.
  • The consultants might have the company’s best interests at heart since they will now be part owners of the company.
  • Possibly there will be greater technical effort to produce optimal designs and cost estimating efficiencies in the drive for great economics.
The potential pitfalls of this approach might include;
  • A public perception that the study is not impartial.
  • There is an overhang of shares that may be dumped onto the market in the near future.
  • Possibly the consultant will charge a premium for their services due to the financial risks they are taking.
  • The company may be more tolerable of study cost overruns since there is no hard cash outlay.
Regarding the first item “impartiality”, in the past there have been questions raised about the impartiality of engineering firms. I first recall reading this claim many years ago in a public response to a mining EIA application. Unfortunately I cannot find the exact source now.
The concern was whether the consultant’s work would be overly optimistic, seeing that they would eventually gain as a project moved from PEA through to the PFS and FS stages. They didn’t want to kill the golden goose. The project’s opponents were making the argument to the regulators “don’t believe what the engineering company is telling you”.
I’m curious how many times this argument has been used, seeing that it’s been around for some time.

Conclusion

It would be interesting to know how many consulting firms would be willing to accept compensation solely in shares.  Stock prices move up and down and the outcome of the study itself can have an impact on  share performance.
Unlike being paid in bitcoin, which also fluctuates in value, shares will generally have a hold period before they can be sold off.  This further increases the consultant’s risk.
I am curious to see whether the First Mining + Ausenco financial arrangement will create a precedent. Possibly it happens more than I am aware of.  Realistically I don’t see anything wrong with the approach, although one needs to understand the perceptions that it can create.   See where you sit if you were on the owner’s or consultant’s side and this idea was being discussed.  What would you do?
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