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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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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O/P to U/G Cross-Over – Two Projects into One

Over the years I have been involved in numerous mining tradeoff studies. These could involve throughput rate selection, site selection, processing options, tailings disposal methods, and equipment sizing. These are all relatively straightforward analyses. However, in my view, one of the more technically interesting tradeoffs is the optimization of the open pit to underground crossover point.
The majority of mining projects tend to consist of either open pit only or underground only operations. However there are instances where the orebody is such that eventually the mine must transition from open pit to underground. Open pit stripping ratios can reach uneconomic levels hence the need for the change in direction.
The evaluation of the cross-over point is interesting because one is essentially trying to fit two different mining projects together.

Transitioning isn’t easy

There are several reasons why open pit and underground can be considered as two different projects within the same project.
There is a tug of war between conflicting factors that can pull the cross-over point in one direction or the other. The following discussion will describe some of these factors.
The operating cut-off grade in an open pit mine (e.g. ~0.5 g/t Au) will be lower than that for the underground mine (~2-3 g/t Au). Hence the mineable ore zone configuration and continuity can be different for each. The mined head grades will be different, as well as the dilution and ore loss assumptions. The ore that the process plant will see can differ significantly between the two.
When ore tonnes are reallocated from open pit to underground, one will normally see an increased head grade, increased mining cost, and possibly a reduction in total metal recovered. How much these factors change for the reallocated ore will impact on the economics of the overall project and the decision being made.
A process plant designed for an open pit project may be too large for the subsequent underground project. For example a “small” 5,000 tpd open pit mill may have difficulty being kept at capacity by an underground mine. Ideally one would like to have some satellite open pits to help keep the plant at capacity. If these satellite deposits don’t exist, then a restricted plant throughput can occur. Perhaps there is a large ore stockpile created during the open pit phase that can be used to supplement underground ore feed. When in a restricted ore situation, it is possible to reduce plant operating hours or campaign the underground ore but that normally doesn’t help the overall economics.
Some investors (and companies) will view underground mines as having riskier tonnes from the perspective of defining mineable zones, dilution control, operating cost, and potential ore abandonment due to ground control issues. These risks must be considered when deciding whether to shift ore tonnes from the open pit to underground.
An underground mine that uses a backfilling method will be able to dispose of some tailings underground. Conversely moving towards a larger open pit will require a larger tailings pond, larger waste dumps and overall larger footprint. This helps make the case for underground mining, particularly where surface area is restricted or local communities are anti-open pit.
Another issue is whether the open pit and underground mines should operate sequentially or concurrently. There will need to be some degree of production overlap during the underground ramp up period. However the duration of this overlap is a subject of discussion. There are some safety issues in trying to mine beneath an operating open pit. Underground mine access could either be part way down the open pit or require an entirely separate access away from the pit.
Concurrent open pit and underground operations may impact upon the ability to backfill the open pit with either waste rock or tailings. Underground mining operations beneath a backfilled open pit may be a concern with respect to safety of the workers and ore lost in crown pillars used to separate the workings.
Open pit and underground operations will require different skill sets from the perspective of supervision, technical, and operations. Underground mining can be a highly specialized skill while open pit mining is similar to earthworks construction where skilled labour is more readily available globally. Do local people want to learn underground mining skills? Do management teams have the capability and desire to manage both these mining approaches at the same time?
In some instances if the open pit is pushed deep, the amount of underground resource remaining beneath the pit is limited. This could make the economics of the capital investment for underground development look unfavorable, resulting in the possible loss of that ore. Perhaps had the open pit been kept shallower, the investment in underground infrastructure may have been justifiable, leading to more total life-of-mine ore recovery.
The timing of the cross-over will also create another significant capital investment period. By selecting a smaller this underground investment is seen earlier in the project life. This would recreate some of the financing and execution risks the project just went through. Conversely increasing the open pit size would delay the underground mine and defer this investment and its mining risk.

Conclusion

As you can see from the foregoing discussion, there are a multitude of factors playing off one another when examining the open pit to underground cross-over point. It can be like trying to mesh two different projects together.
The general consensus seems to be to push the underground mine as far off into the future as possible.  Maximize initial production based on the low risk open open pit before transitioning.
One way some groups will simplify the transition is to declare that the underground operation will be a block cave. That way they can maintain an open pit style low cutoff grade and high production rate. Unfortunately not many deposits are amenable to block caving.  Extensive geotechnical investigations are required to determine if block caving is even applicable.
Optimization studies in general are often not well documented in 43-101 Technical Reports. In most mining studies some tradeoffs will have been done (or should have been done).  There might be only brief mention of them in the 43-101 report. I don’t see a real problem with this since a Technical Report is to describe a project study, not provide all the technical data that went into it. The downside of not presenting these tradeoffs is that they cannot be scrutinized (without having data room access).
One of the features of any optimization study is that one never really knows if you got it wrong. Once the decision is made and the project moves forward, rarely will someone ever remember or question basic design decisions made years earlier. The project is now what it is.

 

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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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A Mining Career or a Travel Club

There have been some LinkedIn discussions about why the mining industry needs to attract more young people.  One of the selling points often mentioned is how mining gives a person the opportunity to experience the world.
Based on my own career, mining has definitely provided me with a chance to travel the world.  It will also help anyone overcome their fear of travel.   One will also learn that both international and domestic travel can be as equally rewarding.  There is nothing wrong with learning more about your own country.
The main purpose for my mining travel was due to either being a QP on a 43-101 study or visiting a site as a member of a due diligence team.   Other reasons have been to provide engineering support at a mine site or to meet with management teams for risk or strategy planning sessions.
Over the last year I haven’t traveled as much as in the past.  One reason is that not every QP working on a 43-101 report has to make a site visit. Fortunately, even when one doesn’t make a site visit, one still learns something about the local politics, legal system, infrastructure, and socio-economic situation in that country.
The map below shows places where I have been in my travels.  It also shows the locations of studies I was involved it. Mining really is a global business.   My map isn’t as cluttered as that of some geologists I know.  Exploration and resource geologists will visit many more destinations that an engineer will. After all, every project needs exploration drilling and a resource estimate, but not all projects advance to the engineering stage.

For those thinking of getting into mining, here is my list of pros and cons based on my own travel experience. Not everything is great about travel but some aspects of it can be fantastic.

What’s Good

  • I had the opportunity to visit many places for which there is a zero probability that I would have ever gone as a tourist.
  • Typically long duration long distance flights are in business class. You get lounge access and the perks associated with executive travel.   Less onerous short flights might be economy only, so be aware of your company policy.
  • All travel expenses, hotels, taxis, meals, etc. are paid for.  Just don’t get too exorbitant when wining and dining.  That’s the job of the senior person you are travelling with.
  • Upon arrival, often there will be a company representative to meet you at the airport.  They speak the local language and will take you where you need to go.  This saves you scrambling around an airport looking for a safe taxi to use.
  • You will get to meet local employees, go to dinner with them, travel around their country, and chat in the evenings. It’s a great way to learn about the people in the country you are visiting.
  • You will get to meet other technical people from around the world.  They might be expats working at a mine site or simply part of a multidisciplinary engineering team on the same visit.
  • You will be whisked away from tourist traps and thus have an opportunity to see the real countryside.
  • You will hit the ground running, get to visit mine sites, see some real live rocks, drill core, pit walls, equipment at work, and things happening.  You won’t get to see that while sitting in your downtown office.

What’s not so good

  • Unfortunately business trips are mostly of a very short duration since you’re not going there as a tourist.  You’re being paid for your time and expertise.

  • Mining trips are usually not to majors centers, so once arriving in the country you really haven’t arrived yet.  There might be more air flights or long pickup truck rides to get to the final destination. There can be a lot of waiting and the days can be long (and frustrating).   I remember on trip in northern Russia where four of us with luggage were jammed into a Volkswagen Rabbit in a snowstorm. That two hour trip took five hours, but we were just happy to make it back to the hotel.
  • Sometimes your accommodations will be less than stellar, i.e. one star hotels or tents.  So you’ll need to learn how to appreciate the charm of those places and not complain that it isn’t the Four Seasons Hotel.
  • Some travel locations can be potentially unsafe and require travelling security. That can lead to a bit of uneasiness.  I recall a trip to northern Mexico where we had two armed guards travelling with us.  I’m not sure if they were really needed and it was strangely more calming without them once they left.
  • Long east-west trips can leave you jet lagged and dog-tired. However the expectation is that at 7 am next morning you’re ready for breakfast and then head straight into the office.  You’re being paid to get to work, not to sleep.
  • The site visits will be focused on collecting or reviewing data and then immediately travelling back home to write your report.  Sightseeing opportunities can be limited other than what you will see during the course of your work. Sometimes you’ll get back home and think that you never really saw the place.

Conclusion

Business travel has always been one of the best parts of my mining career.   I can remember the details about a lot of the travel that I did.   Unfortunately the project details themselves will blur with those of other projects.
When I do travel now, it’s a nice change if just one flight gets you to your final destination.
During this Covid period, international travel is greatly restricted.  It will be interesting to see how soon things can return to normal, if they ever do.   To miss out on the travel aspect of a mining career would be a shame, unless the only travel you want to experience is sitting on public transit for a few hours each day.
By the way, my all time favorite place for a mining trip is…..Argentina.  It’s a long way from Toronto, but well worth it.

 

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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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Pre-Concentration – Maybe Good, Maybe Not

A while back I wrote a blog titled “Pre-Concentration – Savior or Not?”. That blog was touting the benefits of pre-concentration. More recently I attended a webinar where the presenter stated that the economics of pre-concentration may not necessarily be as good as we think they are.
My first thought was “this is blasphemy”. However upon further reflection I wondered if it’s true. To answer that question, I modified one of my old cashflow models from a Zn, Pb project using pre-concentration. I adjusted the model to enable running a trade-off, with and without pre-con by varying cost and recovery parameters.

Main input parameters

The trade-off model and some of the parameters are shown in the graphic below. The numbers used in the example are illustrative only, since I am mainly interested in seeing what factors have the greatest influence on the outcome.

The term “mass pull” is used to define the quantity of material that the pre-con plant pulls and sends to the grinding circuit. Unfortunately some metal may be lost with the pre-con rejects.  The main benefit of a pre-con plant is to allow the use of a smaller grinding/flotation circuit by scalping away waste. This will lower the grinding circuit capital cost, albeit slightly increase its unit operating cost.
Concentrate handling systems may not differ much between model options since roughly the same amount of final concentrate is (hopefully) generated.
Another one of the cost differences is tailings handling. The pre-con rejects likely must be trucked to a final disposal location while flotation tails can be pumped.  I assumed a low pumping cost, i.e to a nearby pit.
The pre-con plant doesn’t eliminate a tailings pond, but may make it smaller based on the mass pull factor. The most efficient pre-concentration plant from a tailings handling perspective is shown on the right.

The outcome

The findings of the trade-off surprised me a little bit.  There is an obvious link between pre-con mass pull and overall metal recovery. A high mass pull will increase metal recovery but also results in more tonnage sent to grinding. At some point a high mass pull will cause one to ask what’s the point of pre-con if you are still sending a high percentage of material to the grinding circuit.
The table below presents the NPV for different mass pull and recovery combinations. The column on the far right represents the NPV for the base case without any pre-con plant. The lower left corner of the table shows the recovery and mass pull combinations where the NPV exceeds the base case. The upper right are the combinations with a reduction in NPV value.
The width of this range surprised me showing that the value generated by pre-con isn’t automatic.  The NPV table shown is unique to the input assumptions I used and will be different for every project.

The economic analysis of pre-concentration does not include the possible benefits related to reduced water and energy consumption. These may be important factors for social license and permitting purposes, even if unsupported by the economics.  Here’s an article from ThermoFisher on this “How Bulk Ore Sorting Can Reduce Water and Energy Consumption in Mining Operations“.

Conclusion

The objective of this analysis isn’t to demonstrate the NPV of pre-concentration. The objective is to show that pre-concentration might or might not make sense depending on a project’s unique parameters. The following are some suggestions:
1. Every project should at least take a cursory look at pre-concentration to see if it is viable. This should be done on all projects, even if it’s only a cursory mineralogical assessment level.
2. Make certain to verify that all ore types in the deposit are amenable to the same pre-concentration circuit. This means one needs to have a good understanding of the ore types that will be encountered.
3. Anytime one is doing a study using pre-concentration, one should also examine the economics without it. This helps to understand the  economic drivers and the risks. You can then decide whether it is worth adding another operating circuit in the process flowsheet that has its own cost and performance risk. The more processing components added to a flow sheet, the more overall plant availability may be effected.
4. The head grade of the deposit also determines how economically risky pre-concentration might be. In higher grade ore bodies, the negative impact of any metal loss in pre-concentration may be offset by accepting higher cost for grinding (see chart on the right).
5. In my opinion, the best time to decide on pre-con would be at the PEA stage. Although the amount of testing data available may be limited, it may be sufficient to assess whether pre-con warrants further study.
6. Don’t fall in love with or over promote pre-concentration until you have run the economics. It can make it harder to retract the concept if the economics aren’t there.

 

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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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Simple Financial Models Can Really Help

A few years ago I posted an article about how I use a simple (one-dimensional) financial model to help me take a very quick look at mining projects. The link to that blog is here. I use this simple 1D model with clients that are looking at potential acquisitions or joint venture opportunities at early stages. In many instances the problem is that there is only a resource estimate but no engineering study or production schedule available.

By referring to my model as a 1D model, I imply that I don’t use a mine production schedule across the page like a conventional cashflow model would.
The 1D model simply uses life-of-mine reserves, life-of-mine revenues, operating costs, and capital costs. It’s essentially all done in a single column.  The 1D model also incorporates a very rudimentary tax calculation to ballpark an after-tax NPV.
The 1D model does not calculate payback period or IRR but focuses solely on NPV. NPV, for me, is the driver of the enterprise value of a project or a company. A project with a $100M NPV has that value regardless of whether the IRR is 15% or 30%.

How accurate is a 1D model?

One of the questions I have been asked is how valid is the 1D approach compared to the standard 2D cashflow model. In order to examine that, I have randomly selected several recent 43-101 studies and plugged their reserve and cost parameters into the 1D model.
It takes about 10 minutes to find the relevant data in the technical report and insert the numbers. Interestingly it is typically easy to find the data in reports authored by certain consultants. In other reports one must dig deeper to get the data and sometimes even can’t find it.
The results of the comparison are show in the scatter plots. The bottom x-axis is the 43-101 report NPV and the y-axis is the 1D model result. The 1:1 correlation line is shown on the plots.
There is surprisingly good agreement on both the discounted and undiscounted cases. Even the before and after tax cases look reasonably close.
Where the 1D model can run into difficulty is when a project has a production expansion after a few years. The 1D model logic assumes a uniform annual production rate for the life of mine reserve.
Another thing that hampers the 1D model is when a project uses low grade stockpiling to boost head grades early in the mine life. The 1D model assumes a uniform life-of-mine production reserve grade profile.
Nevertheless even with these limitations, the NPV results are reasonably representative. Staged plant expansions and high grading are usually modifications to an NPV and generally do not make or break a project.

Conclusion

My view is that the 1D cashflow model is an indicative tool only. It is quick and simple to use. It allows me to evaluate projects and test the NPV sensitivity to metal prices, head grades, process recovery, operating costs, etc. These are sensitivities that might not be described in the financial section of the 43-101 report.
This exercise involved comparing data from existing 43-101 reports. Obviously if your are taking a look at an early stage opportunity, you will need to define your own capital and operating cost inputs.
I prefer using a conventional cashflow model approach (i.e. 2D) when I can. However when working with limited technical data, it’s likely not worth the effort to create a complex cashflow model. For me, the 1D model can work just fine. Build one for yourself, if you need convincing.
In an upcoming blog I will examine the hill of value optimization approach with respect to the 1D model.
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