A Graveyard of Mining Studies: What Kills Mining Projects After the Numbers Check Out

Date: June 15, 2026 Author: Ken Kuchling Comments: Leave a reply

Every few weeks we see another feasibility study completed. Normally the numbers will look fantastic. The feasibility study shows that a project could work, but will it really work?

A positive feasibility study is the moment a mining project is supposed to come alive. It’s the point where geology, engineering, and economics merge into a real (i.e. bankable) business case. However, it is also the starting point of an entirely different and more difficult path.

The roadblocks between a feasibility study and a producing mine can be numerous, varied, and often have nothing to do with the geology itself. A government can change the tax regime, or a community can withdraw support. The commodity prices can turn, or environmental permits can be challenged in court. A company’s own board can lose conviction. Understanding this development gauntlet is the difference between allocating capital to projects with returns and those that will consume capital for a decade.

Stalled projects will experience several of the roadblocks simultaneously. A single roadblock might be surmountable, but multiple roadblocks may not be.

Many of these roadblocks will be identified during confidential third-party due diligence by potential partners, acquirers, or financiers. Hence investors may never learn the actual truth as to why the project is stalled and are left guessing why.

I have been part of due diligences on behalf of lenders and have seen the negative reasons never make it to the public eye. Sometimes the company may itself not know why a project was declined, although they can make an educated guess.

The following is a checklist of the various pitfalls leading to the study graveyard. Check off all those that you think apply to your favorite stalled project. Be honest. Typically, more than one will apply and they will tend to compound.

Geotechnical & Geological Risk

Review of the block modelling reveals concerns with grade, continuity, or metallurgical recovery.
Review of the expected geotechnical conditions reveals mining concerns associated with faulting, weak ground, karst, high water inflows.
Metallurgical complexity understated in feasibility (refractory ore, penalty elements).
Resource classification is suspect and a lot more infill drilling is required, at a high cost.
Hydrology issues, e.g. acid rock drainage severe and difficult to manage long term and becomes a corporate liability.

Technical & Engineering

Feasibility study found to be technically flawed upon review.
Project is very complex and will be difficult to build on budget and on time, as well as difficult to staff with qualified operating personnel.
Processing technology unproven at scale, ore sorting risk ,etc.
Infrastructure assumptions (power, water, road) prove more costly or difficult.
Mine plan optimistic on strip ratios, mining rates, or equipment productivity.
Tailings storage facility design issues, high risk, or siting problems.
Water rights access insufficient or contested.
Offsite infrastructure (road, rail, or port) inadequate and too costly to build.
Remoteness – labor costs and retention problems underestimated.

Permitting, Regulatory, and Social License

Environmental impact assessments likely to be rejected or endlessly delayed.
Perceived difficulties to get operating licenses, water licenses, or discharge permits.
Regulatory framework uncertain and changes mid-process (new environmental laws, mining codes).
Federal/state/provincial jurisdictions overlap creating jurisdictional gridlock.
Permits granted but successfully challenged in court by third parties.
Indigenous or First Nations consultation failures, failure to negotiate community benefit agreements acceptable to all parties.
Local community opposition leading to blockades or political pressure.
NGO campaigns attracting negative media attention that spooks investors or lenders.
Religious, cultural, or heritage site conflicts with site plan.
Mineral title disputes persist with overlapping claims or historical issues.
Land access agreements with surface rights owners difficult to acquire.

Political & Country Risks

Government instability, coup, or change of administration hostile to mining.
Retroactive tax increases, windfall profit taxes, or royalty rate changes.
Nationalization or forced renegotiation of mining agreements.
Corruption demands that the company is unwilling to meet.
Sanctions, war, or civil unrest making the region inaccessible.

Financing & Capital

Inability to secure project financing (debt or equity) due to financier risk appetite, commodity price outlook, or lender requirements.
Cost overruns discovered during reviews that make the economics unviable.
Declining commodity prices between feasibility and financing.
Owner balance sheet too weak to fund large construction; inability to attract a joint venture partner.
Royalty or streaming deals entered into that are too dilutive, making equity unattractive.

Corporate & Strategic

Management change leading to strategy pivot away from the project. The internal champion is gone.
Company acquired by a buyer with a different portfolio strategy — project shelved.
Board loses conviction or knows they cannot manage this; project deprioritized in favor of capital returns or other assets.
Key technical personnel depart, taking institutional knowledge with them.
High market cap of owner makes the acquisition cost high, when considering the capital cost to build must also be incurred by the acquiror. This will lower the return.

Market & Macro

Commodity price collapse, or forecasting an oversupply, makes the project sub-economic even with a positive study.
Input cost inflation (energy, steel, labor, reagents) erodes profit margins.
ESG-driven investor exclusions make it impossible to raise equity capital.
Offtake agreements cannot be secured on acceptable terms. This can be important in industrial and battery mineral projects. This may require focusing on downstream processing into upgraded specialty products, increasing risks and costs.

Conclusion

The list of potential production roadblocks is extensive. Moving from the study stage to production is very difficult and very few can do it successfully. A positive feasibility study is a necessary but far from sufficient condition for production.

When projects stall, it is likely due to multiple factors listed above. A ranking analysis may conclude the compounding effects of the perceived risks makes the project a no-go for financiers.

Some people will say be thankful that more projects don’t advance to production, because we would likely see more failures as the risks come to bear Ideally only the best projects are moving forward, but even there we can see mixed results.

Due to the upcoming shortages of < insert critical mineral > we need more exploration and more discoveries. Given the number of idle feasibility stage projects now, who is to say that these new discoveries won’t see the same roadblocks that the current projects are seeing. Real mining is a tough business – doing studies isn’t.

In case you missed it, the last blog post was “The Double Life of Inferred Resources: Now You See It – Now You Don’t“. The entire blog post library can be found at https://kuchling.com/library/

You can sign up for the KJK mailing list to get notified when new blogs are posted. Follow me on LinkedIn or Twitter (@KJKLtd) for updates and other mining commentary.

The Double Life of Inferred Resources: Now You See It – Now You Don’t

Date: April 18, 2026 Author: Ken Kuchling Comments: Leave a reply

There is a unique paradox sitting at the heart of how the mining industry evaluates its projects. It’s called the Inferred Resource—now you see it, now you don’t.

A company can publish a Preliminary Economic Assessment (PEA) showing a mine life, a robust internal rate of return, and a strong net present value. That study can be based on mineral resources that regulators consider too geologically uncertain to support a production decision. These are the infamous Inferred resources: tonnes, grades, and dollars in the economic model, yet with a classification that effectively flags them as educated guesses. The PEA rules permit their inclusion because early-stage projects need a way to test whether chasing more resource certainty is worth the additional cost.

The paradox occurs when that same project advances to the pre-feasibility (PFS) or feasibility (FS) stage. Suddenly the Inferred ore tonnes are gone; they are now waste rock. The mine plan must be based on more certain Measured and Indicated resources. Theoretically, the life-of-mine production profile that looked compelling in the PEA may now shrink—so the NPV and IRR might shrink too. Nothing has gone wrong in any technical sense. The project simply requires a higher standard of certainty now.

Potentially, some investors who focused on the PEA economics may feel the subsequent feasibility study is a disappointment (especially if costs have also escalated). They’ll say the PEA is garbage. In reality, the project has moved from an aspirational study (i.e., the PEA) toward a bank-financeable study. To compensate for the loss of Inferred material, companies will rely of step out drilling to grow the resource to maintain size.

What Are Inferred Resources?

Inferred resources represent the lowest confidence category of mineral resources; typically estimated in zones with limited sampling and unconfirmed geological continuity. They carry the highest geological uncertainty of the three resource categories.

In Preliminary Economic Assessments (PEAs), Inferred resources are allowed—but only under certain conditions:

They can be included in mine plans and economic models, which is the reason PEAs exist: to allow early-stage projects to test economic viability using all available resource data.
However, any PEA that includes Inferred resources cannot be used to support a production decision and must carry prominent cautionary language. Under NI 43-101, the technical report must explicitly state that the PEA is preliminary in nature, that Inferred resources are too speculative geologically to have economic considerations applied, and that there is no certainty the PEA will be realized. (It seems a PEA without Inferred resources can be used to support a production decision.)
Inferred tonnes are routinely used to extend mine life or improve project economics in PEA studies. Investors must understand this risk and should examine the proportion of mined tonnage that is classified as Inferred. This breakdown is normally presented in the Technical Report.

Conversely, in Pre-Feasibility Studies (PFS) and Feasibility Studies (FS), the rules for Inferred material are different:

Inferred resources cannot be included in mineral reserve estimates or in the economic analysis underpinning a PFS or FS. Inferred “ore” is treated as waste rock.
Only Indicated and Measured resources can be converted to Probable and Proven mineral reserves.
Including Inferred material in a PFS mine plan would typically disqualify the study from being used for project financing or a production decision.
Some companies might include Inferred material in a PFS as “upside” or as a sensitivity case, but that analysis must be clearly distinguished from the base case.

The resource upgrade requirement (from Inferred to Indicated) creates some interesting dynamics:

Companies may need additional funds to drill sufficiently to upgrade the resource before advancing to the PFS/FS stage. The cost and time for this infill drilling can be a major driver of exploration spending and can delay project timelines. Mining projects can take a long time to develop, and this is one reason why.
Companies will look at ways to compensate for the Inferred material deduction. Cutoff grade changes and step out drilling are ways to mitigate this impact.
A large Inferred resource that cannot be upgraded without great cost (due to depth, remoteness, or lack of ore-zone continuity) can permanently stall a project at the PEA stage. Hence, one may see multiple PEAs completed on the same project (i.e., the PEA loop).
In closing, the peculiarity of the Inferred resource is that it is essentially a tiered permission structure. Inferred resources are useful for early economic screening, but they must be converted to higher-confidence categories before they can support a bankable study or project debt financing.

Permitting via a PEA

Companies sometimes will commence the permitting process based on their PEA study. There are some risks to doing this, and the Inferred resource creates one of these risks.

With limited capital, a junior miner may not be able to afford the $5–20 million cost of a full feasibility study before determining whether a project is even permittable. A PEA, costing a fraction of a FS, can provide enough technical substance to engage regulators and begin the environmental baseline work that must precede any formal permit application.

Baseline studies for hydrology, ecology, and air quality typically require two to three years of data collection. So starting early makes sense, even with only a PEA-level project layout in hand.

Permitting and ongoing technical study work will run in parallel on most projects. Waiting for a completed FS before starting permitting would add years to the project timeline. Companies routinely will concurrently advance environmental impact assessments, indigenous consultation, and baseline data collection with subsequent PFS and FS work.

Jurisdiction may play a role too. Some areas are more receptive to early-stage permitting engagement. Other permitting processes may be more rigorous such that operators want at least a PFS in hand before committing to a full EIA process.

Now, with respect to the Inferred resource, the risk is that a project permitted around a PEA-scale footprint may shrink in size at the feasibility stage. Some reasons for this size reduction will be discussed in a future blog post, as well as the ways companies avoid this with ever increasing ore tonnage. Project shrinkage can result in a company acquiring permits and bonding for a proposed mine plan that no longer exists.

How Can Inferred Resources Affect Permitting

Let us examine some specific aspects of permitting that can be influenced by Inferred resources.

1. Project Footprint and Disturbance Area: Permits are issued for a defined physical footprint, consisting of pit limits, waste dumps, tailings facilities, and infrastructure corridors. If a PEA mine plan is inflated by Inferred tonnes and defines a large footprint versus the feasibility study, the company faces a choice:

(a) Permit the smaller FS footprint and risk under-permitting if Inferred is later upgraded. Requesting future permit modification for a suddenly larger project is sometimes viewed by regulators as “permitting by stealth”.

(b) Permit the larger PEA footprint to provide flexibility, which may trigger more extensive environmental review and higher bonding requirements.

2. Environmental Impact Assessment (EIA) Scope & Cost: A mine plan that includes Inferred material:

– May define a larger disturbance envelope, larger waste dumps, larger tailings facility, all of which require assessment of broader habitat, hydrology, and community impacts. Perhaps the project must advance into a new watershed. This can add permitting cost and time. If the Inferred material is later excluded, the assessment work may have been unnecessarily extensive.

3. Tailings and Waste Facility Sizing: Tailings storage facilities (TSFs) and waste rock dumps are sized to the life-of-mine tonnage. Inferred tonnes included in a PEA can drive facility sizing significantly. Permitting a TSF for a larger tonnage is:

– More difficult and time-consuming to obtain if the height and footprint increase

– Will be subject to more rigorous dam safety and closure review- However this could be potentially beneficial if the Inferred resource is later confirmed during mining

Conversely, I have seen situations where permitting was done on the pre-feasibility level design, thereby excluding Inferred ore from the plan. The operability of a planned co-disposal waste facility relied on relative ratios of clean waste rock, acid generating rock, and tailings. During production, the conversion of Inferred material from “waste” to “ore” would mean more tailings, less waste rock, and could shift the required material balance for co-disposal in a negative way. Permitting based on a PEA might make more sense here.

4. Financial Assurance and Reclamation Bonding: Regulators require bonds or financial assurances sized to the cost of full site reclamation. A larger mine footprint driven by Inferred tonnes means:

– Higher bonding requirements

– Larger financial burden on the company during the project development phase

– Potential difficulty for junior companies in securing bonds for Inferred-inflated footprints that have not been proven out yet.

5. Water Licences and Discharge Permits: Water use and discharge volumes scale with throughput and mine life. A longer mine life driven by Inferred tonnes may require expanded water licences. If those tonnes are later excluded, the licence may be oversized. Keep in mind that obtaining an amendment to increase a water licence later can be harder than acquiring a larger one initially, so there is a trade-off here.

6. Indigenous and Community Consultation: In jurisdictions with consultation requirements (Canada’s duty to consult, FPIC principles, etc.), the scope of consultation is tied to the project’s impact footprint and duration. A mine life extended by Inferred tonnes:

– Triggers consultation over a longer operational period

– May affect benefit agreement negotiations (royalties, employment commitments) tied to mine life or total tonnage

– If mine life subsequently shrinks at FS stage, it can create credibility and trust issues with communities who were counting on a longer mine life.

In closing, personally I feel that project proponents should try to permit to a footprint somewhat larger than the Pre-Feasibility base case to preserve operation flexibility. For more on the benefits of flexibility, see the blog post “Mining’s Obsession with Optimization – Good or Bad”.

Conclusion

Inferred resources present a unique paradox; they can and can’t be used in mining economic analysis. They can be used to examine project viability but can’t be used to make a production decision.

The manner in which Inferred resources are viewed can also affect permitting. Although they don’t directly enter the regulatory process, they can shape the physical parameters of the mine plan that regulators will evaluate.

Even if a company grows the resource size between the PEA and PFS, there will still be a component of Inferred material within the PFS mine design that might be considered real tonnage or not.

Getting the permitted footprint right relative to the eventual resource confidence level is an underappreciated skill in project development. Each mining project is unique, and there is no easy one-size fits all solution when considering the impact of Inferred resources on project development.

In case you missed it, the last blog post was ” Mining’s Obsession with Optimization – Good or Bad“. https://kuchling.com/minings-obsession-with-optimization/ The entire blog post library can be found at https://kuchling.com/library/

You can sign up for the KJK mailing list to get notified when new blogs are posted. Follow me on LinkedIn or Twitter (@KJKLtd) for updates and other mining commentary.

The Surprising Parallels Between Junior Miners and Tech Startups

Date: March 22, 2026 Author: Ken Kuchling Comments: 2 Replies

Recently I’ve seen a few LinkedIn conversations about whether the mining industry is spending enough money on Research and Development (R&D). Usually when one thinks of R&D, one might envision the development of new technology, new drug, equipment or software.

I would suggest that mineral exploration should be viewed through the R&D lens. Exploration (or acquisition of explorers) is the most significant way that the industry can self-manage to grow revenues. If exploration is R&D, that then leads to the observation that Junior Miners are the precursors for today’s Silicon Valley startups.

Before venture capital and “startup culture” became a phenomenon, junior mining companies were already operating on the startup core principles. Small, capital driven explorcos were based on unproven assets, led by specialized technical teams. They were funded by investors accepting risk in the hopes for 10 bagger or 100 bagger upsides. This is effectively the same business model that Silicon Valley would build upon.

This blog post examines whether the junior mining industry was a leader in Tech Startup culture. Even things like the Lassonde Curve and the PEA have their comparables in the current tech world.

Are Junior Miners precursors to Tech Startups

Junior mining companies and Tech Startups share numerous similarities, although they operate in very different worlds. The following comments should recognize that junior mining ecosystem has been around for generations, long before the birth of tech ecosystems.

Junior mining companies and Tech Startups are both high-risk, high-reward ventures. Junior Miners and Tech Startups are early-stage companies where the vast majority fail. Investors accept enormous risk in exchange for the possibility of enormous returns if a discovery or product is a resounding success. Lets look at some of the similarities.

A startup spends years (and millions of dollars) building an MVP (Minimum Viable Product). If the market doesn’t want it or the tech fails to scale, the company goes to zero. Similarly if holes don’t hit or the metallurgy is too complex, the mining asset can face significant headwinds.
Both mining and tech are essentially “concept” or “pre-revenue” gambles. A Junior Miner typically has no producing mine, just exploration assets and the dream of an economic mineral deposit. Similarly, many Tech Startups have no revenue, just a product idea or some early traction. Investors in both cases are betting on the team and its future value, not on the non-existing cash flow.
Both burn through cash (lots of it) before generating revenue. Mining juniors need constant financing rounds (placements) to fund exploration; startups need financing rounds for R&D and growth. Neither can easily self-fund, although bootstrapping is more common in tech than in mining. Both may rely on exit strategies consisting of acquisition from larger industry players.
Both generate proprietary data. A Junior Miner’s most valuable asset is often its geological data (drill results, resource estimates, land tenure). A Tech Startup’s asset is its IP, code, or trade secrets. In both cases, the assets are largely intangible until proven economic.
Both rely on quality founders and management. A small, skilled team can make or break the company. A geologist or executive with a great track record (“the Midas touch”) is analogous to a serial tech founder. Often the investors are backing the person more than the project.
Both sectors are heavily sentiment-driven (mining likes 2025, tech not so much). A hot commodity cycle floods junior mining with capital. A hot tech cycle (AI, crypto, SaaS) floods startups. When investor sentiment reverses, funding dries up fast and many companies are left to die, possibly to rise again in the next cycle.
Both sectors can follow the “Lassonde Curve” (mining) or the “Hype Cycle” (tech). There is an initial surge of excitement during discovery/launch, followed by a “boring” period of technical de-risking (development/user acquisition), and finally a re-rating once they reach production or profitability. (More on the Lassonde Curve later in this blog post).
Both sectors require pitching their story to investors. The Tech Startups rely on pitching to angel investor via shows like Shark Tank, pitch summits (t) using 5 minute elevator pitches (“get to the point” pitches). The Junior Miners rely on the numerous mining conferences like PDAC, Mines & Money, Beaver Creek, Zurich, again relying on the PowerPoint pitch to gather eyeballs.

Obviously we should also point out there some differences between juniors and Tech Startups.

Juniors work with physical geological reality. You either find the ore body or you don’t. Startups can pivot; geology can’t.
Mining has far longer timelines; discovery to production can take 10–20 years vs. a startup’s typical 5–7 year VC cycle. Any longer than that, and a Tech Startups technology can become obsolete.
Regulatory, environmental permitting, and social license is a constraint for miners with no real startup equivalent. Viable exploration projects can get blocked through no fault of the miner itself.
Junior Miners are more commodity-price dependent. Even a great deposit can be uneconomic at the wrong metal price. Conversely a miner’s asset could become more valuable over time based on metal prices. Tech startup do not rely on a commodity price outside their control.
Junior Miners tend to rely on public capital markets for financing right from the start, although the trend toward private equity mining investment may be increasing. Conversely, at early stages, Tech Startups tend to be bootstrapped and financed via private equity, venture capital, and angel investors. Junior Miner investments can provide more liquidity and exit opportunities due to their public listing. Tech investors may be locked in until a liquidity event occurs.
The startup world will label their financing rounds (seed, Series A, Series B,..) with the hope that future investors provide financing at higher valuations than earlier investors. The mining industry does not label their placement rounds – perhaps they should.

In conclusion, an analogy between Junior Miners and Tech Startups can help outsiders understand the risk, capital structure, and investor behavior of both industries. One might conclude that Junior Miners were the Tech Startups of decades prior, and are still functioning that way today.

Should exploration expenses be considered R&D

Exploration spending shares some of the same characteristics of more commonly R&D.

R&D is uncertain in outcome, generates intellectual property (geological data, resource models), and is expensed before any revenue is realized. A pharma company doesn’t know if a new drug will be a win, similarly a Junior Miner doesn’t know if a drill program will yield an economic deposit. Both activities are investments in discovering something of future value, hopefully.

R&D is about de-risking a concept, and de-risking is a term commonly used by Junior Miners. Every drill hole, soil sample, and geophysical survey is a data point that builds knowledge, and even uneconomic drill holes provide value by focusing the search area.

Much like our drug development example, exploration has a high-failure, high-reward path. Early-stage “lab work” (geochemistry/geophysics) leads to “clinical trials” (core drilling) and eventually “commercialization” (feasibility and production).

There are also some differences between exploration and conventional R&D. R&D will typically create proprietary intellectual property, like a new drug or software platform that can be replicated and marketed globally. A mineral discovery is a unique, non-replicable physical asset in a given location. R&D in tech is about creating something from nothing (innovation), while exploration is about finding something that already exists (discovery).

Exploration also tends to be more binary since it is geologically constrained. You either find an ore body or you don’t. You can’t modify or pivot with an orebody. On the other hand, drug and software R&D can yield partial successes or new technologies, that may have other applications.

Although there are both similarities and differences, the analogy is interesting. Exploration may be considered as a hybrid since it has the uncertainty and knowledge-creation aspect of R&D, but the result is a unique physical asset and not IP. In my view exploration is equivalent to R&D.

Lassonde Curve vs Hype Cycle Curve

As mentioned previously, junior mining and Tech Startups will follow a cyclic path of hype and despair. In mining it is known as the “Lassonde Curve” and in tech its called the “Gartner Hype Cycle”. Let’s look at the similarities. Which came first?

The Lassonde Curve is the “elder” of the two models, predating the Gartner Hype Cycle by about a decade. I had written a previous blog post on this at Mining’s Lassonde Curve – A Wild Ride.

The image below shows them side by side, and they do look similar. While both charts effectively track the “rollercoaster” of investor psychology and technical de-risking, they emerged from different eras and industries. Both charts have a x-axis that represents time and both have a y-axis that represents expectation ( stock price can be viewed as a measure of expectation).

1. The Lassonde Curve (developed in late 1980s)
Created by Pierre Lassonde, the legendary mining financier and co-founder of Franco-Nevada. The model explains the life cycle of junior mining stocks to investors, explaining why sometimes investors get burned after a discovery even if the project is technically sound.

2. The Gartner Hype Cycle (developed in 1995)
Created by analyst Jackie Fenn at the technology research firm Gartner to help clients distinguish between the “hype” of a new technology and its actual commercial maturity. The model guides corporate IT departments on when to invest in new technologies (e.g., AI, Cloud, VR) without getting burned by the “Peak of Inflated Expectations.”

Does the Gartner HC model use the earlier Lassonde Model as a template? Both models show that humans tend to over-speculate on “newness” (whether it’s a drill hole hit or a new technology) and then lose interest when the hard work begins. It seems that Pierre Lassonde mapped that human behavior a few years before the tech world did. In this aspect, the understanding of investor behavior in junior mining was leading the way for Tech Startup behavior.

Is a PEA Study Like a Tech Product Market Fit (PMF) Study

Another similarity between junior mining and tech world is in the way early-stage viability is assessed. This is required to decide whether millions of dollars of further investment is warranted. Miners will complete a PEA. Startups will complete Product-Market Fit research.

A Preliminary Economic Assessment (PEA or scoping study) is an early-stage technical and economic evaluation of a mineral deposit. Its core purpose is to determine whether a project is potentially viable before committing significant capital to more advanced studies.

Product-Market Fit (PMF) research for a Tech Startup is a structured effort to determine whether a product satisfies a strong market demand. The goal isn’t just to confirm PMF exists; its to understand the who, why, and how it will work before committing to aggressive growth.

Comparing a Preliminary Economic Assessment (PEA) to a Tech Startup’s Product-Market Fit (PMF) stage is a great way to see how both industries similarly “de-risk” an idea before committing big money.
In both worlds, this is the moment where one stops saying “We have a cool idea or a nice deposit” and start saying “We have a viable business.”

1. The “Does This Thing Actually Work?” Test

– Tech (PMF): Once the team has built a beta, they can see if people are using it and are willing to pay for it. They need to prove there is a market for the tech.

– Mining (PEA): The team has found a deposit. The PEA is the first time they can put a dollar sign on it. It’s a conceptual study that predicts “If we build a mine here with these current economic inputs, it should make money.”

2. The Shift from “Geology” to “Economics”

Just as a Tech Startup shifts from coding to customer acquisition cost (CAC), a Junior Miner shifts from geology to metallurgy and CAPEX.

– Tech: It doesn’t matter how good the code is, if it costs >$50 to acquire a customer who only spends $5 it will not be a viable venture.

– Mining: It doesn’t matter if you have 2 million ounces of gold if the rock is difficult to process or if the project costs billions to build. The PEA is the first reality check on these costs.

3. Attracting the Investors

The PEA / PMF stage may be the ultimate gatekeeper for many institutional capitalists.

– Tech: Once you deliver a PMF, Venture Capital (VC) firms may be more willing to provide growth capital to scale the business.

– Mining: Once a positive PEA is released, the company may see a “re-rating.” Larger funds and mid-tier miners may start looking at the project as a real asset rather than just a speculation.

In closing, It is important to remember that both PEA / PMF stages are still early. A tech company with PMF can still be crushed by a competitor or a change in regulatory or platform privacy requirements. A mining project with a PEA can still fail if the Pre-Feasibility Study (PFS) reveals that the environmental permitting or economic factors are not as expected. Neither early stage study is a guarantee for future success, which is another similarity between the two sectors.

Conclusions

There are many parallels between Junior Miners and Tech Startups. The similarities are in how they are built, how they function, and how they acquire funding.

In that way, exploration expenditures can also be viewed through the lens of R&D spending. So it may be improper for some to suggest that the mining industry is not spending enough on R&D, when it actually is spending huge amounts on R&D.

The Junior Mining industry has been around much longer than the Tech Startup world, and hence have led the way in building an ecosystem for speculative investment.

Is it cool to work in the startup world? The answer is yes if its tech, and (unfortunately) no if its mining.

**end**

In case you missed it, the last blog post was “Beyond the Headline Grade: For or Against Assay Transparency?“.

You can sign up for the KJK mailing list to get notified when new blogs are posted. Follow me on Twitter at @KJKLtd for updates and other mining posts. The entire blog post library can be found at https://kuchling.com/library/

Beyond the Headline Grade: For or Against Assay Transparency?

Date: March 11, 2026 Author: Ken Kuchling Comments: Leave a reply

Recently I have been reviewing a few mining projects from an investor’s perspective. This led me to wonder whether junior mining companies should share more than just their drill hole highlights. What about the raw assays? A mining company announces highlighted drill intervals, but what exactly do those numbers represent?

The highlighted drill interval is based on a series of individual assay samples. The selection of the interval “From” and “To” and “Including” is made by the company using their own criteria. They may include low-grade sections (i.e. smoothing), apply variable cutoffs, sometimes use metal equivalent grades or NSR value to defines the ore/waste cutoff.

The interval definition process is largely invisible to investors, yet for early-stage projects, drill hole intervals are the primary driver of investor communications and their own project valuation.

The underlying basis for drill intervals are raw assay data. These assays can be complex, messy, technical, and may require expertise to interpret properly. However examining the individual assay data can tell more of the story than looking at intervals alone. For example, assays will demonstrate the grade continuity, any nugget effects, or whether thin high-grade spikes are boosting average grades.

This raises the question of whether companies should routinely publish, in CSV format, for download BOTH interval data and raw assay data.

This blog post discusses the pros and cons of releasing assay data electronically.

Why Might an Investor Want the CSV Data

There is a sense that many mining investors are becoming more sophisticated, and they want to fully understand the exploration process.

Some investors have geological software with which to examine the exploration data in 3D. Other investors may simply rely on Excel to run their own statistics. Investors may wish to verify what a company is doing, as well as examine their own concept for a project.

Investors might be questioning whether:

Companies change the nature of a deposit by smoothing narrow high grade intervals over wider intervals to give the impression of a large tonnage bulk deposit;
Companies are using by-product metals in their metal-equivalent, which investors have less confidence in. Perhaps they would prefer to examine the deposit based solely on their primary metals of interest. Perhaps investors prefer to understand the NSR Rock Value ($/t) instead of relying metal-equivalent grades.
Companies have not provided sufficient geological cross-sections or 3D images, and investors wish to create their own.

To examine any of these issues, one must have the exploration data in electronic format. For large players, signing an NDA (Non-Disclosure Agreement) and entering the corporate data room provides preferential access to this information. Small players may not be able to do this and some may consider this unfair and selective disclosure.

Highlighted Interval Data in CSV Format

Companies will disclose all highlighted drill INTERVALS in news releases, usually in table format (see example image). If someone wishes to analyze all the interval data on a project, it can be a tedious process to gather all the new releases PDF’s. The tabular data can then be scraped using Ai or one can use Excel “Get Data” functionality on a table by table basis.

This work can be cumbersome and sometimes data does not transform cleanly, often with missing rows, columns, or misaligned data.

Why make investors jump though those hoops to summarize data that’s already been disclosed? Hence, I would suggest companies maintain downloadable historical drill interval data in CSV format on their websites, in a format as presented in the news releases.

Raw assay data, however, is a different matter than interval data since assays have not been made public in the news releases. This is discussed in the next section.

Raw Assay Data in CSV Format

Deciding whether to release raw drill hole assay data is a balance between transparency and strategic risk. The only companies that are doing this (that I am aware of) is Power Metallic and (previously) Great Bear Resources. There may be more that i am not privy to.

There are both pros and cons to making the raw assay data available. Note that raw assay data files typically will also include drill hole collar coordinates and downhole survey information, the whole package.

The Pro’s and Benefits of Raw Data Disclosure

1. Market Credibility: A company being “radically transparent” can set them apart by signaling to the market confidence in their data and they have nothing to hide. This can build trust with those who want to verify the discovery. This can differentiate one from peers in a crowded junior market where transparency may be lacking.

2. Free Technical Analysis: Raw data appeals to technically sophisticated investors, geologists, and analysts who want to do their own review. The independent “super-investors” who run their own models may reach the same conclusions as the company, proving external validation for the project. They may essentially help IR with free marketing and 3rd party promotion. Of course, the opposite can also happen if the project is not as robust as being promoted by the company.

3. Speed: Raw data access may speed up due diligence for potential acquirers or JV partners who can initiate confidential internal reviews prior to deciding whether to enter a formal NDA and due diligence process.

The Con’s and Risks of Raw Data Disclosure

1. Misinterpretation & “Amateur” Experts: One risk is that someone with a very basic understanding of mining software and limited understanding of the local geology, runs flawed interpretations and publicizes their incorrect conclusions. A company may find that correcting false narratives publicly can be harder than preventing them.

Conversely stock pumpers can cherry-pick individual high-grade intervals out of context and also create misleading narratives (albeit positive) outside of the company control. They may have their own motives to pump the stock.

2. Competitive Intelligence: If in a competitive exploration district, neighbors can use raw assay values and lithology codes to predict where the mineralization is trending. This could help them acquire adjacent mineral claims before one can fully consolidate the regional land package. Regardless, competitors may still be able to do some of this using the drill hole interval data from news releases, so this may not be a big risk.

3. Liability and Compliance: NI 43-101 reporting requires technical data to be verified by a Qualified Person (QP). If one provides a raw assay file that isn’t properly vetted or contains errors, will there be regulatory or legal issues? Normally a corporate QP signs off on news releases, not necessarily an independent QP.

There have also been cases where assay data has been purposely manipulated from lab certificates to the drill hole database, and investors would then be working with this falsified data. Corporate liability could be a big risk in the eyes of some.

The Assay Disclosure Middle Ground

Once the assay data is public, it may be more difficult for a company to manage the story. A press release lets them frame results in the context of their business plan; a raw data file does not.

Instead of providing a full assay database download, some companies might follow the approach below. It may not satisfy everyone, but could be better than just ignoring requests for more detailed information.

Interactive Map: Use a web-based GIS tool that allows users to click holes and see downhole results graphically without downloading the full database.
Cross-Sections: Provide multiple high-quality cross-sections that show the geological interpretation alongside the raw assay grades or use a grade bar chart along the drill hole trace. This provides information on the grade continuity and uniformity without releasing the actual grades.
Post-resource estimate disclosure: Once the database has been formally audited by an independent QP and incorporated into a 43-101 technical report for a resource estimate, releasing the assay data may be lower risk.

These actions may give a sense of greater transparency while keeping the raw data within a controlled environment. It will help prevent amateurs from erroneous modeling the geology yet still “somewhat” satisfying the sophisticated mining investor.

Conclusion

For investors trying to assess a junior explorer, or geologists conducting a technical review, or a regulator trying to ensure fair and accurate disclosure, access to raw assay data can play a part in promoting good judgment and accurate disclosure from companies.

However some suggest that raw data without context is subject to misinterpretation.

Personally, I would prefer to see the release of both drill intervals and raw assay tables in CSV format.

In this modern area of Ai and investor sophistication, greater data transparency may be a positive and help build more trust in the industry. However, I understand the reasons not to do this and understand if companies choose to not go this route.

Currently the mining industry norm is that raw assays stay in private data rooms and not on corporate websites. Credibility tries to be achieved via clear, technically detailed news releases and Technical Reports.

In case you missed it, the last blog post was “What Does Ai Think of Toll Milling“.

You can sign up for the KJK mailing list to get notified when new blogs are posted. Follow me on Twitter at @KJKLtd for updates and other mining posts. The entire blog post library can be found at https://kuchling.com/library/

APPENDIX: EXCEL TASKS with and ASSAY DATABASE

I asked an Ai colleague Claude to list all the things one could do in Excel with publicly released drill hole information. Here is what it said (unedited). Its never lacking in advice and Claude will even help you create the Excel worksheet logic if needed.

Grade Analysis

Descriptive statistics – mean, median, mode, min/max, standard deviation of assay values (Au, Ag, Cu, etc.)

Grade frequency distributions — histogram charts to understand grade population shape.
High-grade outlier identification — flagging values above a threshold (e.g., >3x median) that may need capping consideration.
Grade capping/cutting analysis — testing different cap values and their effect on average grade.
Coefficient of Variation (CV) — assessing grade variability and nugget effect risk.

Composite & Interval Analysis

Downhole compositing — averaging assay intervals into fixed-length composites (e.g., 2m or 5m) using weighted averages.
True width estimation — applying trigonometric corrections for hole dip/azimuth vs. vein orientation.

Grade x thickness (GT) calculations — multiplying average grade by intercept width for comparative ranking of intersections.
Cut-off grade sensitivity — running intercept calculations at multiple cut-off grades to see how reported widths and grades change.

Hole Correlation & Comparison

Hole-by-hole grade comparison — ranking holes by peak grade, average grade, or GT value.
Section-by-section comparison— grouping holes by fence/section for along-strike trend analysis.
Depth vs. grade plots — scatter plots to look for grade zonation with depth.
Multi-element ratio analysis— e.g., Au:Ag ratios, Cu:Mo ratios to identify metallogenic zonation or alteration trends.

Spatial & Trend Analysis

Collar location plotting — basic X/Y scatter plots of collar coordinates to visualize drill pattern coverage.
Downhole survey data — using dip/azimuth measurements to approximate downhole trace (basic desurveying).
Strike and dip trend identification — plotting grade against easting or northing to identify directional trends.
Variogram-lite analysis — plotting grade vs. distance between holes to get a rough sense of spatial continuity (proper variography needs dedicated software).

Multi-Element & Geochemical Analysis

Correlation matrices — identifying which elements are positively or negatively correlated (e.g., Au vs. As in a Carlin-type system).
Scatter plots — visualizing element-pair relationships to identify pathfinder associations.
Log-transformation — applying LOG10 to skewed assay data before statistical analysis, which is standard practice.
Anomaly thresholding— mean + 2 standard deviations as a simple anomaly threshold for pathfinder elements.

Resource Estimation (Basic)

Polygonal area calculations — basic area-of-influence resource estimation using drill spacing geometry.
Grade tonnage curves — plotting tonnes vs. grade at varying cut-offs using simplified volume assumptions.
Bulk density sensitivity** — testing how different SG assumptions affect tonnage estimates.

Reporting & QA/QC

Duplicate analysis — comparing field duplicate, coarse reject duplicate, and pulp duplicate pairs using scatter plots and % relative difference calculations.
Standard performance tracking — plotting certified reference material (CRM) results against expected values and control limits (±2SD, ±3SD).
Blank monitoring — flagging blank samples that exceed contamination thresholds.
Laboratory turnaround tracking — monitoring submission dates vs. return dates.

What Does Ai Think of Toll Milling

Date: February 24, 2026 Author: Ken Kuchling Comments: Leave a reply

I recently noticed a few new mining studies that relied on toll milling as their base case operation. This made me wonder if this is a trend and if there are more examples of where this is happening.

If only there was a way to find out.

I have been tinkering with Ai (mainly ChatGPT and Gemini) to see what it can do, but I never really had a “project” for it to work on.

So I thought, lets see what Ai can tell us about toll milling. This would be an opportunity to test out Claude (AI assistant built by Anthropic) to see what it will deliver. I could ask it a few questions about the concept of toll milling to find out what it would say.

The following blog is the unedited responses that were given to my user prompts. The results, albeit lengthy, are interesting and informative. Let’s dive in.

By the way, this is the first blog post ever where I have applied Ai.

CLAUDE Ai CONVERSATION

USER PROMPT: Can you prepare a list of recent mining studies that used toll milling as the base case.

RESPONSE: Here is a summary of recent mining studies (primarily PEAs and PFS-level studies) that used toll milling as the base case or a key component, based on publicly available information from 2024-2026:

1. RPX GOLD – WAWA GOLD PROJECT (ONTARIO, CANADA) PEA – February 2026
The base case is built entirely around toll milling, with crushed ROM ore trucked to an off-site facility within 150 km for processing into gold dore. No on-site mill or tailings facility is planned, significantly reducing capital requirements. The project sits in the Michipicoten Greenstone Belt near Wesdome, Alamos, and Hemlo operations, all of which offer potential excess capacity.

2. AMEX EXPLORATION – PERRON GOLD PROJECT (QUEBEC, CANADA) Updated PEA – September 2025
Features a two-stage development strategy: a 4-year, 1,000 tpd contract mining and toll-milling operation in Phase 1, followed by construction of an on-site processing plant in Phase 2. The Abitibi region’s numerous operating mills make this approach practical. Initial capex is C$146M, partially offset by C$68.6M in pre-production revenues during the toll-milling phase.

3. CHALLENGER GOLD – HUALILAN PROJECT (ARGENTINA) PFS – 2024/2025
The PFS is structured around toll milling as the path to near-term production, with the study outlining forecast EBITDA of approximately A$221M over a 3-year tolling period at current metal prices. Challenger also executed a binding term sheet with Dolphin Real Assets/Elsztain Group to facilitate processing through a tolling agreement, with the arrangement expected to fund mining, transportation, and processing costs until initial gold revenue is received.

4. SCOTTIE RESOURCES – SCOTTIE GOLD MINE PROJECT (BRITISH COLUMBIA, CANADA) PEA – October 2025
The primary base case is a Direct-Ship Ore (DSO) scenario, but the study prominently presents toll milling at the nearby Premier mill as a significant upside scenario. Under the toll-milling arrangement, the after-tax NPV(5%) rises from C$215.8M to C$380.1M at US$2,600/oz gold – a meaningful enhancement. No formal toll-milling agreement was in place at the time of the study.

5. MAGNA MINING – CREAN HILL PROJECT (ONTARIO, CANADA) PEA – 2024
The PEA incorporates a definitive off-take agreement with Vale Base Metals to process Contact mineralization through Vale’s Clarabelle Mill in Sudbury. This is a firm, contracted toll-milling arrangement rather than a conceptual one, giving the study’s economics a higher degree of confidence in processing costs.

6. AURIC MINING – JEFFREYS FIND GOLD MINE (WESTERN AUSTRALIA) Operational toll-milling program – 2024
While not a formal feasibility study, Auric and joint venture partner BML Ventures executed a contract to toll mill a minimum of 300,000 tonnes of ore through the Greenfields Mill in Coolgardie during 2024. The first stage produced ~9,741 oz of gold at 1.86 g/t, demonstrating the model’s viability for small, high-grade open pit operations lacking on-site processing infrastructure.

GENERAL OBSERVATIONS:

Toll milling as a base case is most common in:

– Early-stage PEAs for junior developers in established mining districts (Abitibi, Michipicoten, Sudbury) where nearby mill capacity exists.
– High-grade, lower-tonnage projects where the capital cost of a dedicated mill is hard to justify at the outset.
– Projects pursuing a phased development path to generate near-term cash flow before committing to full plant construction.

USER PROMPT: What are the risks of toll milling plan.

RESPONSE: Toll milling carries several meaningful risks that study authors and investors should consider carefully.

OPERATIONAL & LOGISTICS RISKS

The most fundamental risk is mill availability. A toll miller may prioritize its own ore or other clients during peak periods, leaving your project without processing capacity exactly when you need it. Scheduling conflicts can delay production and erode project economics significantly. Transportation of ore or concentrate over long distances also introduces costs that are sensitive to fuel prices, road conditions, and seasonal access – particularly in remote northern jurisdictions.

COMMERCIAL & CONTRACTUAL RISKS

Toll milling agreements are typically not long-term ironclad contracts, and terms can be renegotiated or terminated. The toll miller holds significant leverage, especially if there are few alternative processors nearby. Processing rates, payable metal terms, and penalty clauses for deleterious elements can all shift unfavorably over time. If no binding agreement exists at the study stage – as was the case with Scottie Resources – the entire economic base case rests on an assumption rather than a firm commitment.

METALLURGICAL RISKS

The toll miller’s circuit is optimized for their own ore, not yours. Differences in mineralogy, grind size requirements, or the presence of penalty elements like arsenic, bismuth, or mercury can result in lower recoveries than modeled, higher processing charges, or outright refusal to accept the ore. Blending your material with the host mill’s feed can also complicate settlement and metal accounting.

FINANCIAL & ROYALTY RISKS

Without ownership of the processing facility, you have limited control over costs. Any increase in the toll miller’s operating costs – energy, reagents, labor – can be passed through to you. Additionally, some royalty structures are calculated on net smelter returns, meaning toll milling charges directly reduce the royalty base and affect third-party obligations.

REGULATORY & PERMITTING RISKS

Transportation of ore across jurisdictions may require additional permits, environmental assessments, or spill containment plans. If the toll mill’s own permits are challenged or its tailings facility reaches capacity, your project could be indirectly affected by regulatory actions aimed at a third party.

STRATEGIC RISKS

Relying on toll milling can delay the development of on-site infrastructure and institutional processing knowledge, which matters if the long-term plan is to build a dedicated mill. It can also make the project harder to finance or sell, since acquirers typically prefer integrated operations with controlled processing costs. Finally, if the toll miller is acquired, restructured, or shuts down, continuity of the arrangement is immediately in question.

In short, toll milling is an elegant solution for reducing upfront capital, but it trades capex risk for a different set of operational, commercial, and strategic risks that need to be carefully managed and clearly disclosed in any technical study.

USER PROMPT: What responsibility does the Qualified Person have in signing off on a study with toll milling.

RESPONSE: The Qualified Person (QP) signing off on a technical study that uses toll milling as the base case carries significant professional and regulatory responsibility, particularly under NI 43-101 in Canada or JORC in Australia.

CORE RESPONSIBILITY UNDER REGULATION

Under NI 43-101, the QP must take responsibility for the technical report in its entirety, or for clearly delineated sections where multiple QPs are involved. The QP must have sufficient relevant experience not just in geology or engineering broadly, but specifically in the type of deposit and the technical aspects being certified – which in a toll milling scenario includes process engineering, metallurgy, and mine economics. A geologist signing off on processing assumptions they are not qualified to assess would be in breach of their obligations.

REASONABLENESS OF ASSUMPTIONS

The QP is required to certify that the study’s assumptions are reasonable and reflect industry best practice. For toll milling specifically, this means the QP must be satisfied that the processing costs used in the economic model are realistic and supportable. If the toll milling rate is based on a verbal understanding or a letter of intent rather than a binding agreement, the QP must disclose this clearly and assess whether the assumed rate is achievable in the current market. Using a stale or unsupported toll rate without disclosure would represent a failure of duty.

DISCLOSURE OF RISKS

The QP has an affirmative obligation to disclose material risks that could affect the project’s viability. A rigorous technical report should explicitly identify the risks associated with toll milling – mill availability, contract uncertainty, metallurgical compatibility, transportation costs – rather than burying them in boilerplate language. Superficial or generic risk disclosure that does not reflect the specific circumstances of the toll milling arrangement would fall short of professional standards.

METALLURGICAL COMPETENCE

If the study relies on recoveries and processing costs derived from testwork conducted on the toll miller’s circuit, the QP must evaluate whether that testwork is adequate, recent, and representative of the ore to be processed. If no testwork has been done on the specific mill to be used, the QP must justify why analogous data is sufficient, and must disclose the uncertainty this introduces. Blindly accepting a recovery assumption without interrogating its basis is a serious professional risk.

INDEPENDENCE AND CONFLICTS OF INTEREST

NI 43-101 requires the QP to be independent in certain circumstances, and to disclose any relationships with the issuer. If the toll miller is a related party or has a financial interest in the project, the QP must consider whether this creates a conflict that needs to be disclosed or whether it affects the reliability of commercial terms assumed in the study.

SITE VISIT REQUIREMENTS

The QP is generally required to have conducted a site visit. In a toll milling scenario, a thorough QP would arguably need to be familiar not just with the mine site but with the processing facility to be used – its condition, spare capacity, metallurgical compatibility, and operational track record. Signing off on processing assumptions for a facility that has never been visited or independently assessed is a professional vulnerability.

PRACTICAL AND PROFESSIONAL CONSEQUENCES

If a study proves materially misleading due to unsupported toll milling assumptions, the QP faces regulatory action from securities regulators, professional discipline from their engineering or geoscience association, civil liability to investors, and reputational damage. Canadian securities regulators have historically taken a dim view of QPs who rubber-stamp economic assumptions without genuine independent verification.

In summary, the QP’s responsibility in a toll milling study is not merely to sign a form – it is to genuinely interrogate the commercial, metallurgical, and logistical assumptions underpinning the arrangement, ensure they are adequately disclosed, and take personal professional accountability for their reasonableness. A QP who treats toll milling as a convenient shortcut to a positive economic outcome without doing this work is exposing themselves to considerable risk.

I asked Claude to create a QP checklist for factors that should be reviewed for a toll milling study. You can download that file at this link DOWNLOAD FILE.

CONCLUSION

The results given by Claude are quite thorough and insightful. It’s hard to argue with its observations and conclusions. This research took all of 30 seconds, so I can see it is no longer difficult to become a blog writer. Writing isn’t the challenge; finding interesting topics is.

One toll milling project that Claude did not list was the Galway Metals Estrades Project PEA published February 18, 2026. Perhaps that study was too recent to be familiar with it.

While Claude is not a mining specific Ai platform, there are some that are under development with a mining research focus. Some of these include MineGPT , SureOre.ai, ProspectorAi , and there are probably even more out there.

This area will continue to evolve, and may help mitigate the technical personnel shortage being experienced.

END

In case you missed it, the last blog post was “Mine Waste Risk Management – A Step Towards Consistency“.

You can sign up for the KJK mailing list to get notified when new blogs are posted. Follow me on Twitter at @KJKLtd for updates and other mining posts. The entire blog post library can be found at https://kuchling.com/library/

Junior Mining Shams and Scams – Part 2

Date: June 3, 2024 Author: Ken Kuchling Comments: Leave a reply

This is Part 2 of the blog post discussing junior mining scams and the sanctioning of those responsible. Part 1 can be found at this link “Junior Mining Shams and Scams – Part 1” and can be read first to get some background.

Part 2 provides examples involving professional Qualified Persons (QPs). We may have the feeling that QP’s are never held accountable for their work, but that is not always the case. There are numerous instances of QP’s being hauled off in front of their professional organizations for unprofessionalism. This blog will highlight some of those cases.

The lesson is that QP’s signing off on technical information for clients should be proficient in the nature of their work and need to know the reporting rules very well. Some of these incidents involve error and poor judgment, not outright fraud.

These examples involve in-house QP’s that work for a company while others involve independent QP’s that have been contracted by the company.

In most of these cases, any sanctions, if applied, were mainly doled out by the professional engineering or geological associations. The legal courts are often not involved.

Examples (Part 2)

Inaccurate QP Resource Estimate: This example relates to the Barkerville Gold Mines resource estimate complete in 2012. The mineral resource estimate had numerous errors and issues related to assays, capping, cutoff grades, etc. The allegation was the QP had demonstrated incompetence, unprofessional conduct, and negligence. The sanctions on the QP included a $15,000 fine, $20,000 legal costs, re-training, and license suspension.

Link 1:

Link 2:

False QP Statements: Here is an example from 2011. The QP / company Director was charged with multiple allegations, including stating the mine was in commercial production (when it wasn’t), leaving the impression there were mineral resource (which there weren’t any), and generally approving information that was not accurate. Ultimately the allegation was demonstrating incompetence, unprofessional conduct, and negligence. The resulting sanctions included 2 year license suspension, re-training, payment of $80,000 to the professional association.

Link 1:

False QP Statements: This is case were a resource QP failed to comply with the NI43-101, Form 43-101F1, CIM Standards, and CIM Guidelines. The Panel concluded that the Member was guilty of non-compliance with the standards and hence guilty of professional misconduct. Its not clear what specifically was not complied with, but this illustrates that QP’s will be held accountable for their work. The two reports in question were either amended or withdrawn to address the concerns of the regulator in British Columbia, although there had been no intentional non-compliance and no economic impact. The penalty was 2 months suspension (deferred), and the QP had to work under a mentor for 9 months and pay a maximum $10,000 for the mentor’s fees.

Link 1:

False QP Statements: Here is another example of sanctioning a QP in 2018. The person was charged with unprofessional conduct and incompetence. He stated that he was responsible for the preparation of the technical report, had no prior involvement in the project, and was independent of the issuer. This was false. Sanctions included three month license suspension, taking some re-training, require peer review of the report. In 2022, further sanctions consisted of a $75,000 penalty, further sanctioned along with an executive in the company for insider trading issues (see Link 3 below).

Link 1:

Link 2:

Link 3:

False QP Statements: This is an example for 2017 when an independent QP took responsibility for the resource estimation work in section 14 of the report, yet they did not have the experience to take responsibility. They also misrepresented that the Report complied with 2014 CIM standards, yet it didn’t, resulting in an overestimation of the mineral resource in both confidence and magnitude. Other claims include relying on non-QP experts but not stating that, failing to adequately discuss the nature of sample quality control procedures as required. The sanctions included license suspension of 3 months, must work with a technical supervisor, re-training, and $7,500 in legal fees.

Link 1:

False QP Statements: This is another example from 2019, when the QP / Director permitted the disclosure of information to the public in multiple news releases where they should have known that that information was misleading. An example is the disclosure of inferred reserves when there were no reserves and “inferred” is not even a classification of reserves. The sanctions were a license suspension of 4 months, $5,000 legal costs, re-training, and requirement not to act as a QP again.

Link 1:

QP Resource Estimate Error: This occurred in 2012 when a QP and consulting firm delivered a incorrect resource estimate and reportedly withheld negative opinions on the project from the client. The company spent $3 million on a feasibility study during which the resource estimate error was discovered. The “corrected” resource made the project uneconomic, meaning the feasibility study should not have been done. The mining company sought compensation from the consulting firm, ultimately receiving $1.25 million. The Link 3 references is interesting in that it states “As this case demonstrates, it is critically important for professional service firms to be forthcoming about any reservations they may have regarding the work they are being employed to undertake.” Whatever that really means?

Link 1:

Link 2:

Link 3:

Disclaimers

Many QP’s realize the legal responsibility and liability they have. Hence in 43-101 reports QP’s sometimes attempt to mitigate this by putting in broad disclaimers to limited that liability. If the securities commission notices such disclaimers, they will require that they be removed.

For example, sometimes the QP of a chapter may have other geologists or engineers (i.e. experts) assisting them. They may then put in disclaimers saying “They disclaim responsibility for such expert content” for the work done by the others. A QP essentially has to sign off on the work of others in the sections they are responsible for. A recent example is a Generation Mining technical report prepared by G-Mining, that had to be amended from (March 31_2023 to May 31_2024) removing several QP liability limiting disclaimers.

43-101 regulations state that “An issuer must not file a technical report that contains a disclaimer by any qualified person responsible for preparing or supervising the preparation of all or part of the report that

(a) disclaims responsibility for, or limits reliance by another party on, any information in the part of the report the qualified person prepared or supervised the preparation of;

or (b) limits the use or publication of the report in a manner that interferes with the issuer’s obligation to reproduce the report by filing it on SEDAR.“

” These disclaimers are also potentially misleading disclosure because, in certain circumstances, securities legislation provides investors with a statutory right of action against a qualified person for a misrepresentation in disclosure that is based upon the qualified person’s technical report.

That right of action exists despite any disclaimer to the contrary that appears in the technical report. The securities regulatory authorities will generally require the issuer to have its qualified person remove any blanket disclaimers in a technical report that the issuer uses to support its public offering document.“

Conclusion

This ends Part 2 of this blog post. It hopefully highlights the importance of QP’s being knowledgably on the disclosure rules and the technical aspects of what they are hired to do.

Companies should always ensure that only qualified QP’s are used for their project work, even if some of the others might be less expensive. The potential headaches and costs afterwards may override the higher upfront cost.

It should be noted that this Sham and Scam blog does not discuss the other less than desirable junior mining traits seen from time to time. These relate to; insider trading; abuse of micro-penny founder shares; and the ever present “pump & dump” schemes.

In closing, if anyone is aware of other good examples of QP issues, please provide any links to any publicly disclosed information. I can add them to the blog post.

Note: You can sign up for the KJK mailing list to get notified when new blogs are posted. Follow me on Twitter at @KJKLtd for updates and other mining posts. The entire blog post library can be found at https://kuchling.com/library/

Junior Mining Shams and Scams – Part 1

Date: May 30, 2024 Author: Ken Kuchling Comments: Leave a reply

Recently (in April 2024) Red Pine Exploration issued several press releases highlighting that some assays in their geological database were found to have been manipulated. Numerous assays input into their database did not match the original lab certificates. Is this another mining scam?

The Red Pine event led me to ask some colleagues about similar situations that have occurred and whether the personnel responsible were ever sanctioned. Their feedback provided me with several past examples of such incidents, which I have attempted to summarize in this two-part blog post. Big thanks to my colleagues that took the time to provide these examples.

Raise the Red Flag

The focus of this blog is on the types of activities that raised the red flags in the past. I am less interested in naming the people responsible, although the associated web links do provide more detail on the events.

Not all of the examples listed in these two blogs are scams or deliberate falsification of results. Some may be incompetence, faulty reporting, or lack of diligence and care. Some of these involve company executives, in-house Qualified Persons (QPs), and independent QP’s working for the companies.

Part 1 has examples mainly involving company management or in-house QPs. Part 2 will provide other examples where QP’s have been held to account for their poor quality of their work.

Examples (Part 1)

The following are presented in no particular order. Some of these may still be at the allegation or investigation stage. This blog post can be updated when the issue is eventually resolved.

Tampering with Samples: Bre-X salting of samples is the number one example of a well orchestrated scam. I’m not sure if anyone was ever officially convicted of anything at Bre-X, but it warranted several books, recent podcasts, and even a loosely-based Hollywood movie (Gold). As an aside, I had spoken with the Bre-X team in 1995 about consulting work while I was living in Calgary. However, they were still far from needing mine engineering services at that time. That would have been a wild ride, although with my luck, I would have ended up being the only one in jail. For further information here is an interesting story from Warren Irwin on the Bre-x story. https://redcloudfs.com/25-years-after-bre-x-by-the-man-who-made-a-fortune-going-long-short-of-the-biggest-ever-mining-fraud/

Falsifying QP Signature: The B.C. Securities Commission (BCSC) is alleging that a B.C.-based mining company and its CEO made false or misleading statements about an Idaho mineral deposit in a report that it filed. In 2019, Multi-Metal filed a technical report which contained an electronic signature of a qualified person – a professional engineer – and listed him as an author. The BCSC alleges the qualified person did not review, sign, or consent to filing Multi-Metal’s report. At this time, the BCSC’s allegations have not yet been proven.
Link 1

Falsifying Assay Data: The Ontario Securities Commission approved a settlement agreement between a geologist with 30 years of experience and the Qualified Person for Bear Lake Gold Ltd. Between 2007 and 2009 the QP altered certain assay results and transferred these results into the company’s assay database; prepared draft press releases that contained incorrect and inflated data, then provided Independent QP’s with the altered data, and also replaced core and modified a drill core log. In the settlement, the QP agreed to a permanent ban from acting as a Director and Officer of any issuer, an administrative penalty of $750,000, $50,000 in costs, and a prohibition from trading.
Link 1
Link 2
Link 3

Falsifying Assay Data: In 2024 Red Pine Exploration Inc. reported that there were 382 assay inconsistencies out of a total of 60,000 assay results for the 2019-2024 Period, representing 95 intersections contained within 69 drill holes as follows. An independent investigation is underway, however at the time of this blog, the investigation is still on-going. A link will be provided here once their final report is disclosed publicly.
Link 1

Tampering with Samples: This example goes back to 1981, involving New Cinch Uranium. They published test results that showed significant gold and silver at their New Mexico property. After the stock jumped, third party tests showed that samples did not contain any significant amount of precious metals. The New Cinch samples were “salted”. The Vancouver Stock Exchange was sued for not verifying the company’s test results. In response, the VSE made it compulsory for companies to issue a disclaimer on each press release stating that the VSE “neither approves nor disapproves of their contents”. This case goes back 40 years, so limited information is available on it. A bit more discussion on this case and discussing the VSE is found at the link below. While the VSE no longer exists, the TSX has taken over.
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Falsifying Assay Data: This example involves Southwestern Resources, a company with the Boka Project in China. The former CEO and President, John Paterson, was the company’s QP. In 2007, a month after Paterson’s resignation, Southwestern announced there were errors in previously reported assay results. As a result, Southwestern withdrew all of its previously disclosed results for that project. Sounds familiar? An independent investigation by Snowden led to a revised resource estimate that was substantially less than previously reported and identified 433 discrepancies in gold grades reported in dozens of 2003 and 2007. The original assay certificates were sent to Paterson and he was the sole recipient. Instead of transferring the true assay certificate data, Paterson transferred data containing discrepancies into the database. There was 6 years of jail time involved with this case.
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Falsified Resource Estimate: This one goes back to 1997, although sanctioning of parties was only done in 2007. The company geologist was accused of several things, including not having adequate data to support the findings in his 1997 resource report; the methods used to calculate resources were not appropriate; the report portrayed the project as more advanced than it was; the $129 projected share value was based on “unsubstantiated tonnage and grade information and data.”. Exotic assaying methods and duping accredited investors was also part of this operation.
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Using Exploration Targets in Economic Analysis: This event goes back to 2012 and involves a company breaking the rules by disclosing the results of an economic analysis that included a target for further exploration (pie-in-the-sky) of the company’s gold mining operation. The economic analysis was not based on a current resource estimate. The punishment was the proponents had to pay to the commission $20,000 and complete a course of study on the requirements of Canadian mining rules.
Link 1

Conclusion

This ends Part 1 of this blog post. Part 2 will continue with a few more examples, specifically involving Qualified Persons, and can be found at this link Junior Mining Shams and Scams – Part 2

Discipline is typically rendered in two ways; the Security Commission may prosecute; or the professional associations will provide sanctions. Typically, the professional association penalties are more lenient, consisting of a temporary license suspension and the payment of legal fees.

If readers have any other examples of such junior mining stories, email them to me at kjkltd@rogers.com and I can add them to this blog.

Note: You can sign up for the KJK mailing list to get notified when new blogs are posted. Follow me on Twitter at @KJKLtd for updates and other mining posts. The entire blog post library can be found at https://kuchling.com/library/

NPV a Disappointment? A Few Ways to Fix It

Date: April 5, 2024 Author: Ken Kuchling Comments: Leave a reply

So, you just completed your initial PEA cashflow model and the resulting NPV and IRR are a little disappointing. They are not what everyone was expecting. They don’t meet the ideal targets of an IRR greater than 30% and an NPV that is more than 2x the initial capital cost. The project could now be on life support in the eyes of some.

Now what to do? Its time to jump into NPV repair mode.

Hopefully this blog post isn’t too controversial but will lead to some discussion about how studies are done. Its based on observations I have made over the years as to what different studies will do to try to improve their economics.

The first thought typically is to lower (i.e. low ball) the capital and operating costs. We know that will certainly improve the economics. A risk with that is it might discredit the entire study if the costs are not in line with similar projects. Perhaps someone does a deep dive into the costing details or does some benchmarking against other projects. Also, advanced studies will develop more accurate costs, ultimately highlighting that the initial study was inaccurate and misleading. So overly optimistic, under-estimated costing is not a good approach.

What other things can help bump up the NPV? Let’s look at some of the ones I have seen, some of which I have applied in my work. I would expect (and hope) that some of these ideas will already have been adopted in the initial engineering and cashflow model.

Using the Time Value of Money

The discounting of cashflows in a cashflow model means that up-front revenues and costs have a bigger impact on the final economics than those far off in the future. This effect is amplified at higher discount rates.

Hence looking at ways to bring revenue forward or push costs backwards are typically the first options considered. Here are a few of the ways this will be done.

High Grading and Stockpiling: One can bring revenue forward by using a Low Grade Ore stockpiling strategy. Select an elevated cutoff grade to define High Grade Ore and send only that ore to the plant. The Low Grade Ore can be placed into a stockpile and processed gradually or all at once at the end of the mine life. One must mine more tonnes to undertake stockpiling and will eventually incur an ore rehandling cost. However, in my experience, the early revenue benefit from high grade normally outweighs the associated cost impacts.

Stockpiling Tramming: If using the stockpiling approach described above, many assume that all stockpile rehandling to the crusher will simply be done by tramming with a wheel loader. Having to re-load the ore into trucks will cost more than double the cost of tramming. So place the ore stockpiles close to the crusher to lower rehandling costs.

Milling Soft Ore: If the deposit has both an upper soft ore (oxide, saprolite) and a deeper hard ore, one can take advantage of the soft material and push more ore tonnage through the plant at start-up. This will increase the up-front revenue. It may also allow the cost deferral of some plant components that are only needed for processing the harder ore.

Defer Stripping Tonnages: Delaying some waste stripping costs from pre-production (Y-1) to Year 1 or Year 2 will help improve the NPV. However, care must be taken that increasing mining tonnages in Year 1 or 2 doesn’t trigger the purchase of additional loaders and trucks. The deferred tonnes need to be small enough not to trigger a fleet size increase or could negate the impact of the cost deferral.

Capitalize Waste Stripping: It may be possible to capitalize waste stripping for satellite pits and pit wall pushbacks to better align stripping costs with the timing of ore mining. Capitalizing waste stripping may result in lower short term taxable income since the entire expense is not immediately deducted. This can reduce tax liabilities and improve cash flow. Each situation may be unique.

Accelerate Depreciation: In some jurisdictions, tax laws permit accelerated depreciation rates. This will help to lower or eliminate taxable income in the early years. This boosts the after-tax cashflow in those years, bumping up the NPV. If accelerated depreciation is the case, enhancing revenue (by high grading) at the same time, gives an even bigger nudge to the NPV. Maximize the revenue during tax free periods.

Apply Tax Losses: On some projects there are historical corporate tax loss carry-overs. These losses allow one to offset future taxes payable in the early years. This help bump up the initial after-tax cashflows.

Leasing of Equipment: One can look at equipment leasing to defer some of the initial capital costs. Leasing will distribute the purchase cost over several years (typically 60 months). Although the lease interest will increase the total cost of the machine, the capital cost deferral likely results in an NPV benefit.

Use Contract Mining: To avoid the entire cost of purchasing major mining equipment, many will look to contract mining. In studies, sometimes contract mining costs are estimated or they can be derived from budgetary contractor quotes. At an early stage these contractor quotes might be quite “favorable” as the contractor tries to stay in the good books of the mining company. Contract mining will greatly reduce the mining equipment capital cost and can help the NPV, even if the unit mining costs may be slightly higher with a contractor.

Using Other Cashflow Tweaks

There are other tweaks that one can make to the cashflow model. Sometimes several of the small ones, when compounded together, will result in a significant impact. Here are some of the other cashflow model adjustments that I have seen.

Increase Metal Prices: Normally when selecting metal prices for the cashflow model one looks at; trailing averages; analyst consensus forecasts; marketing study forecasts; and prices being used in other current studies. It is usually simple to defend whatever price you wish to use. In a rising price environment, one can see what other recent studies have used and escalate those prices by 5%-10%. That likely won’t be viewed as unreasonable. After all, someone has to be the trailblazer in raising modelling metal prices.

Improve metal recoveries: At an early study stage, one may have limited number of metallurgical tests upon which to base the process recoveries. I have seen some bump up the recoveries slightly and add the statement “Further metallurgical testing, grind size optimization, and reagent optimization should improve the recovery above those shown by the current test work”. This can gain a bit of revenue at no extra cost.

Optimistic Dilution: It can be very difficult to predict ore mining dilution at an early stage. Two different engineers looking at he same mining method, may come up with different dilution assumptions. Hence one may have the opportunity to select an optimistic dilution. Lower dilution will increase the head grade to the mill and hence increase the revenue at no extra cost. Even a modest reduction in dilution will play its role in nudging up the NPV.

Reduction in Working Capital: Some cashflow models do not include the cost for working capital, while others will include it. Working capital is the money needed on hand to pay the monthly operating cost in Year 1 before payable revenue is generated. If difficulties arise in achieving commercial production, one wishes to have more working capital on hand. Working capital typical is 2-4 months of operating cost. To bump up NPV, some will use the lower range of 2 months working capital. Some will just omit working capital entirely. Take a look at the working capital needs and decide what is reasonable.

Buy the Royalty: Some projects may have the option for a company to buy out the royalty from the royalty holder. Although doing this may result in an upfront cost, the payable royalty saving may offset that up-front buy-out cost. At high metal prices, the royalty saving could be significant.

Reclamation Cost Equals Salvage Value: At the end of the mine life, the final closure and reclamation cost will be in the tens of millions of dollars. Although this cost is heavily discounted back to the start of the cashflow model, I have seen cases where it is assumed that salvage value of the mine and plant equipment is sufficient to pay the entire closure cost. I don’t know how realistic this is, but I have seen that assumption used.

Lower The Discount Rate: A few years ago, it seems the benchmark discount rate was 5% used in most studies. In 2024, the cost of capital has gone up. Hence many studies seem to be using 8%-10% as their base case. A project at the PEA stage today isn’t going to be built for a few years. Some can argue that interest rates will likely be lower in a few years, and so using 5% discount rate today is still reasonable. Conversely some will maintain that is is best to use what others are using so that current projects are all comparable.

Conclusion

Don’t let a disappointing NPV get you down. There may be a few ways to boost the NPV by applying some common practices. However, if after applying all of these adjustments, the NPV still isn’t great, something bigger may be required. That could be an entire project scope re-think.

Or go drill for more ore higher grade ore.

Or low-ball the cost estimates (just kidding).

I have heard that if the project requires fancy tax manipulation to make it work, then it isn’t a good project to begin with. If taxes are critical, the economics may be too marginal.

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The Anatomy of 43-101 Chapter 16 – Mining (Part 2)

Date: March 22, 2024 Author: Ken Kuchling Comments: Leave a reply

Part 2 of this blog post will focus on the remaining engineering work to finish Chapter 16 of the Technical Report. We only wrote about half of it in Part 1. The mining engineer can generally handle the rest of these tasks without requiring a lot of external input. You can read Part 1 at this link “The Anatomy of 43-101 Chapter 16 – Mining (Part 1)”.

The pit design and phases were completed at the end of Part 1, and we can move on to scheduling.

4. Production Scheduling

Once the pit design is complete, everyone will be calling for the production schedule as soon as possible. Others on the team are waiting for it. The tailings engineers need the production schedule for the tailings stage design. The process engineers need the scheduled head grades to finalize sizing the plant components. The client wants the schedule to plug into their internal cashflow model for a quick peek at the economics.

However, before the mine engineer can start scheduling, the dilution approach needs to be selected. Dilution is waste that is mixed in with ore during mining. A high amount of dilution can dramatically lower the processed head grades. There may be a desire to “low ball” the dilution to make the grades look better, but the engineer should base the dilution on what they would expect to see.

Two dilution approaches are common. One can either construct a diluted block model; or one can apply dilution afterwards in the production schedule. I have used both approaches at different times.

The production schedule must be on a diluted basis, since that represents what the processing plant will actually see.

Generally, two different production schedules must be created: (i) a Mining schedule, and (ii) a Processing schedule. In some instances, they may be one and the same schedule. However, if any ore stockpiling is done, then the Mining schedule will be separate from the Processing schedule.

The Mining schedule shows ore going directly to the plant and ore going into the stockpiles. The Processing schedule will show ore delivered directly from the mine and ore reclaimed from stockpiles. Building stockpiles and pulling ore from stockpiles are two independent activities.

Sometimes lower grade stockpiles are built up by the mine each year but only processed at the end of the mine life. Periodically the ore mining rate may exceed the processing rate and other times it may be less. This is where the stockpile provides its service, smoothing the ore delivery to the plant.

Scheduling can be done with variable time periods. Perhaps the schedule is generated using monthly time periods, or quarters, or years.

The 43-101 report will normally show the annual production schedule, but that does not mean it was generated that way. I prefer to use short time periods (monthly or quarterly) for the entire mine life, to ensure ore is always available to feed the plant. A 10 year mine life would result in 120 monthly time periods, so output spreadsheets can get large.

Scheduling can be done manually (in Excel) or by using commercial software, like Datamine’s NPVS. The commercial software is better in that it allows one to run different scenarios more quickly, and it does a lot of the thinking for the engineer. It also does a good job of stockpile tracking. It also decides when it is necessary to transition to mining in satellite pits.

Once the schedules are finalized, they are normally reviewed by the client for approval. The strip ratio and ore grade profile by date are of interest. One may then be asked to look to at different stockpiling approaches to see if an NPV (i.e. head grade) improvement is possible.

One can stockpile lower grade ore and feed the plant with better grade by mining at a higher rate with more equipment. One might need to examine iterative schedules of that type.

Sometimes one must take two steps backwards and re-design some of the initial pit phases to reduce waste stripping or improve grades. Then one would run the schedules again until getting one that satisfies everyone.

Now that the schedule is complete, we can write up the Chapter 16 text up to page 15. We’re getting closer to the end.

5. Site Layout Design

With the pit tonnages and mining sequence from the schedule, the mine engineers can start to look at the site layout (waste dumps and haul roads). Normally the tailings engineers will be responsible for the tailings layout. However, if there is no tailings engineer on the PEA team, the mining engineer may look after this too.

First there is a need for a waste balance. This defines how much mined overburden or waste rock will be needed to build haulroads, laydown pads, and tailings dams. Then the remaining waste volume must be placed into waste dumps.

Hopefully the tailings engineers have finished their tailings dam construction sequence by this time to provide their rockfill needs (although unlikely if you only gave them the production schedule two days ago).

The geotechnical engineers will provide the waste dump design criteria; for example, 3:1 overall side slope using 15m high dump lifts. Ideally it is nice to have soil and foundation information beneath the waste dump sites, but at PEA stage most often this isn’t available. The dump locations are only being defined now.

The mining engineers will size the various waste dumps to their required capacity. Then they can lay out the mine haulroads from the pit ramps exits to the ore crusher, the ore stockpiles, and to each waste dump.

That’s it for the site layout input. Add another 2 pages to Chapter 16. Now the mining engineers can look at the mining equipment fleet.

6. Fleet Sizing and Mining Manpower

The last task for the mine engineer in Chapter 16 is estimating the open pit equipment fleet and manpower needs. The capital and operating costs for the mining operation will also be calculated as part of this work, but the costs are only presented in Chapter 21.

The primary pieces of equipment are the haul trucks. They can range in size from 30 tonnes to 350 tonnes and anywhere in between.

Typically, the larger the equipment is, the lower the unit cost ($/t), especially in jurisdictions where labor costs are high. One doesn’t want a mine fleet with only 5 trucks nor one with 50 trucks. So where is the happy medium?

Once the schedule and site layout are complete, the mine engineers can run the truck haul cycles, in minutes. They need to estimate the time to drive from the pit face, up the ramp, to the waste dump, to the ore crusher, and return back into the mine. Cycle times determine the truck productivity, in tonnes per hour per truck and include the time to load the truck. Some destinations may have long cycle times (to a far off crusher) while others may be quick (to an adjacent waste dump).

The cycle time must be calculated for each material type going to each destination. As the pit deepens, the cycle times increase.

Very simplistically, if a 100 tonne truck has a 20 minute cycle time, it can do three cycles in an hour (300 tph). If one has to mine 10 million tonnes of ore per year, then that would require 33,300 truck hours. If a single truck provides 6500 operating hours per year, that activity would require a fleet of 5 trucks. The same calculation goes for waste.

The total trucking hours will vary year to year as waste stripping tonnages change or haul cycle times increase in deeper pits. The required truck fleet may vary year to year. Keeping haul distance short and haul cycles quick is the key to a lower cost mine.

The mine engineers undertake the productivity calculations for loading equipment to estimate annual operating hours, and the required shovel / loader fleet size.

The support equipment needs (dozers, graders, pickups, mechanics trucks, etc.) are typically fixed. For example, 2 graders per year regardless if the annual tonnages mined fluctuate.

The support equipment needs are normally based on the mining engineer’s experience. Hence the benefit of actually working at a mine at some point in your career.

Blasting includes both the blasthole drilling activity and hole charging. The mining engineer estimates drill productivity and specifications based on the bench height, the expected rock mass quality, and the power factor (kg/t) need to properly demolish the rock.

Finally, the mine operation manpower is estimated based on all the equipment operating hours as well as the fixed number of personnel to support and supervise the mine.

This essentially concludes the mining information presented in Chapter 16 of a typical 43-101 open pit report.

Conclusion

These two blog posts hopefully give an overview of some of the things that mining engineers do as part of their jobs. Hopefully the posts also shed light on the amount of work that goes into Chapter 16 of a 43-101 report. While that chapter may not seem that long compared to some of the others, a lot of the effort is behind the scenes.

Some will say PEA’s are not very accurate documents that should be taken with a grain of salt. One should understand that engineers are working with a limited amount of information at this early stage while forming the concept for the proposed operation.

The subsequent study stages are where more accurate costs are expected and can be demanded.

I don’t know if this overview makes one want to sign up to be a mining engineer or learn to code instead. None of this is rocket science; it just requires practical thinking.

If young people want to get into mining, but not sure into which aspect, I suggest go read through a 43-101 report. There are sections describing exploration, resource modelling, mine engineering, metallurgy, geotechnical engineering, environmental, and financial modelling. Its all in one document. See if any of these areas are of interest to you. Universities should use 43-101 reports as part of their mining engineering curriculum.

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The Anatomy of 43-101 Chapter 16 – Mining (Part 1)

Date: March 20, 2024 Author: Ken Kuchling Comments: 4 Replies

When people learn that I’m a mining engineer, I’ll normally get perplexed looks and asked what that job is about. Most never even knew the job existed.

So I thought what better way to explain the mining engineer role than by describing the anatomy of a typical Chapter 16 (MINING) in a 43-101 Technical Report. That chapter is a good example of the range of tasks typically undertaken by mining engineers.

Secondarily it also provides an opportunity to describe in detail all the steps that go into writing a Chapter 16, focusing on the PEA.

PEA’s tend to have a poor reputation for lack of accuracy, and this blog post may shed some light on why that is. To avoid running on too long, I have subdivided this into Part 1 and Part 2.

Generally, one will see a single QP sign off on Chapter 16. However, the chapter requires input from several people. Section 16 is generally prepared in the same way for a PEA or a feasibility study (FS). The main difference is related to the amount of hard supporting data in a FS versus a PEA.

The PEA will rely on many “reasonable assumptions” and it can be done in at least half the time of a FS. A FS will also build on previous study decisions, something a PEA doesn’t have access to since it is a first time snapshot of a project.

Normally preparing Chapter 16 is done under time pressure to deliver results as quickly as possible. Other study team members are waiting for its output to finalize their own engineering work.

1. Define the Mine

In a PEA, the first thing that must be conceptualized is whether this will be an open pit (OP) mine, underground (UG), or a combination of both.

There is always a mineral resource estimate available before doing a PEA. The way the resource is reported will indicate what type of mine this likely is. The geologists have already done some of the mining engineer’s work.

The mineral resource will suggest if this will be an OP or UG, a large or small operation, a long life or short life, and the likely processing method. The framework for the project is already set at the mineral resource estimate stage.

We can now write page 1 of Chapter 16.

2. Optimize the Pit Size and Shape

The first step for the mining engineer is a pit optimization analysis to define the approximate size and shape of the pit. The pit optimization step creates a series of nested economic pit shells for different metal prices. For example, the base case gold price may be $1800/oz, but we still want to see what size of pit would be economic at $1000/oz, $1200, $1300, etc. Normally one may run 50 different price scenario increments. The smaller shells may eventually be good starter pits to help improve NPV and payback time.

Before starting pit optimization, we require economic inputs from several people. The base case metal prices must be selected (normally with input from the client). The mining operating cost per tonne must be estimated (by the mining engineer). The processing engineers will provide the processing cost and recovery for each ore type.

The geotechnical engineers will provide approximate pit wall angles. All of these inputs have to be forecasted at a very early stage. We don’t yet know the size of the pit, the ore tonnage available, nor the actual plant throughput rate but one must still predict some costs. Hence these initial inputs might just be ballpark data.

In the final cashflow model you may eventually see slightly different metal prices, costs, or recoveries than used in pit optimization. That’s because that cashflow model inputs are generated by the study, while the optimization inputs are pre-study estimates.

The pit optimization step may also need to apply constraint boundaries. For example, if there is a nearby property limit or river, one may want to constrain the pit optimization to get no closer than 50 metres to the river or boundary. The pit shell optimizer may be free to expand the pit outwards in multiple directions, except that one direction.

Once the optimization is run, a series of nested pit shells are created, each with its own tonnes and grade. These shells are compared for incremental strip ratio, incremental head grade, total tonnes, and contained metal.

A decision must now be made on which shell to use for the mine design. Larger economic shells may have more tonnes, lower grade, and higher strip ratio. Smaller shells may have lower strip ratio and better grade.

For example, a smaller shell may have 10 year life containing 800,000 oz at a strip ratio of 2:1 while a larger shell may have 14 years, 1 million oz at a strip ratio of 3:1. Both are roughly the same economically. However, developing the larger shell may require more mining equipment capital yet have a lower average cost per tonne. Which shell do you choose?

There can be dozens of such shell to shell trade-offs and typically one doesn’t run schedules and cost models on all of them. The client will have input on whether they wish to move forward with 10 years 800,000 oz or the 14 years with 1 million oz. Sometimes selection is driven by investors having size expectations that need to be met.

Some people may say ‘Well… just run cashflow models for each case to see which is best”. The problem with doing too much analysis at this stage is that if you re-do the pit optimization with different recovery, operating costs, pit wall angles, you will get a different optimization result. It becomes a question of how much detail work to do on something that is based on very preliminary input parameters.

Assuming the mining engineers have now selected the preferred shell for mine design, they can move on to mine design. We can now write more of Chapter 16 to page 5.

3. Open Pit Design.

The mining engineer is now ready to undertake the pit design. The pit design step introduces a benched slope profile, smooths out the pit shape, and adds haulroads. Hence a couple of key input parameters are required at this time. The mining engineer will need to know the geotechnical pit slope criteria and the truck size & haul road widths. Let’s look at both of these.

Pit Slopes: Geotechnical engineers are responsible for providing the slope angle criteria to the mining engineers. The geotech engineers may have a lot or little information to work with. Perhaps they have geotechnical oriented core holes and they have undertaken some rock strength testing.

Perhaps the only information for the geotechnical engineers is rock quality data from exploration drilling. I have seen both situations at the PEA stage; the latter is more typical. In the feasibility study they would have geotechnical core hole data available. At the PEA stage, that is less likely, since no one yet knows the size and depth of the pit. We are only getting to that now.

Pit wall schematic

The geotechnical engineers will provide the inter-ramp slope angles, specified by catch bench widths and bench face angles. The engineers may subdivide slopes by rock type.

For example: the overburden wall is to be at 30 degrees, the underlying oxide rock at 40 degrees and the deeper fresh rock wall at 55 degrees. Additionally, the pit may be subdivided into pie shaped sectors, with differing slope criteria.

For example, the fresh rock on the west wall might have a 55 degree angle, but the east wall fresh rock may only allow 50 degrees and the south wall is 45 deg.

The more sectors and differing slope criteria, the more complex it is to do the pit design. Normally you don’t see geotechnical engineers signing off as QP’s for Chapter 16, although they had key input into the pit design.

Ramps: Next the mining engineer needs to select the truck size, even though the production schedule has not yet been created.

Trucks sizes can vary between 30t up to 350t. A double lane ramp width is approximately 4.5 times the truck width, including space for a ditch and an outer safety berm. A 90 tonne truck is 6.7 metres wide (haulroad of 30m) while a 350 tonne truck is 9.8 m wide (haulroad of 44 m wide). That’s a 14m width difference.

The haul road gradient is normally 10%, which means a 200 metre deep pit requires a ramp length of 2000 metres to get to the bottom. It can be difficult to fit a 2 kilometre ramp in a small pit without pushing the walls out to provide enough circumference to get to depth.

Ramps can spiral around the pit, or they can zigzag back and forth on one side of the pit (switchbacks). The mine engineer will decide this once they see the topography, pit size, and ore body orientation. Adding ramps in a pit design pushes the crest outwards and adds waste to be stripped.

Pit Phases: After the pit design is complete, the mine engineer will design multiple interior phases to distribute the waste and ore tonnages in the mining schedule. These phases are sometimes referred to as pushbacks, laybacks, or stages. At mine start-up, one doesn’t want to strip the entire top off of a large pit. A smaller pit within the large pit will allow faster access to ore.

This completes the open pit design and now allows one to write to page 10 of Chapter 16. However, the mining engineer is not done yet.

Conclusion

This ends Part 1. In Part 2 we will discuss the mining engineer’s next tasks; production scheduling; waste dump design; and equipment selection. The mining engineer QP will sign off and take responsibility for all the mine design work done so far. You can read Part 2 at this link “The Anatomy of 43-101 Chapter 16 – Mining (Part 2)“.

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Every few weeks we see another feasibility study completed. Normally the numbers will look fantastic. The feasibility study shows that a project could work, but will it really work?

A positive feasibility study is the moment a mining project is supposed to come alive. It’s the point where geology, engineering, and economics merge into a real (i.e. bankable) business case. However, it is also the starting point of an entirely different and more difficult path.

Stalled projects will experience several of the roadblocks simultaneously. A single roadblock might be surmountable, but multiple roadblocks may not be.

Many of these roadblocks will be identified during confidential third-party due diligence by potential partners, acquirers, or financiers. Hence investors may never learn the actual truth as to why the project is stalled and are left guessing why.

I have been part of due diligences on behalf of lenders and have seen the negative reasons never make it to the public eye. Sometimes the company may itself not know why a project was declined, although they can make an educated guess.

The following is a checklist of the various pitfalls leading to the study graveyard. Check off all those that you think apply to your favorite stalled project. Be honest. Typically, more than one will apply and they will tend to compound.

Geotechnical & Geological Risk

Review of the block modelling reveals concerns with grade, continuity, or metallurgical recovery.

Review of the expected geotechnical conditions reveals mining concerns associated with faulting, weak ground, karst, high water inflows.

Metallurgical complexity understated in feasibility (refractory ore, penalty elements).

Resource classification is suspect and a lot more infill drilling is required, at a high cost.

Hydrology issues, e.g. acid rock drainage severe and difficult to manage long term and becomes a corporate liability.

Technical & Engineering

Feasibility study found to be technically flawed upon review.

Project is very complex and will be difficult to build on budget and on time, as well as difficult to staff with qualified operating personnel.

Processing technology unproven at scale, ore sorting risk ,etc.

Infrastructure assumptions (power, water, road) prove more costly or difficult.

Mine plan optimistic on strip ratios, mining rates, or equipment productivity.

Tailings storage facility design issues, high risk, or siting problems.

Water rights access insufficient or contested.

Offsite infrastructure (road, rail, or port) inadequate and too costly to build.

Remoteness – labor costs and retention problems underestimated.

Permitting, Regulatory, and Social License

Environmental impact assessments likely to be rejected or endlessly delayed.

Perceived difficulties to get operating licenses, water licenses, or discharge permits.

Regulatory framework uncertain and changes mid-process (new environmental laws, mining codes).

Federal/state/provincial jurisdictions overlap creating jurisdictional gridlock.

Permits granted but successfully challenged in court by third parties.

Indigenous or First Nations consultation failures, failure to negotiate community benefit agreements acceptable to all parties.

Local community opposition leading to blockades or political pressure.

NGO campaigns attracting negative media attention that spooks investors or lenders.

Religious, cultural, or heritage site conflicts with site plan.

Mineral title disputes persist with overlapping claims or historical issues.

Land access agreements with surface rights owners difficult to acquire.

Political & Country Risks

Government instability, coup, or change of administration hostile to mining.

Retroactive tax increases, windfall profit taxes, or royalty rate changes.

Nationalization or forced renegotiation of mining agreements.

Corruption demands that the company is unwilling to meet.

Sanctions, war, or civil unrest making the region inaccessible.

Financing & Capital

Inability to secure project financing (debt or equity) due to financier risk appetite, commodity price outlook, or lender requirements.

Cost overruns discovered during reviews that make the economics unviable.

Declining commodity prices between feasibility and financing.

Owner balance sheet too weak to fund large construction; inability to attract a joint venture partner.

Royalty or streaming deals entered into that are too dilutive, making equity unattractive.

Corporate & Strategic

Management change leading to strategy pivot away from the project. The internal champion is gone.

Company acquired by a buyer with a different portfolio strategy — project shelved.

Board loses conviction or knows they cannot manage this; project deprioritized in favor of capital returns or other assets.

Key technical personnel depart, taking institutional knowledge with them.

High market cap of owner makes the acquisition cost high, when considering the capital cost to build must also be incurred by the acquiror. This will lower the return.

Market & Macro

Commodity price collapse, or forecasting an oversupply, makes the project sub-economic even with a positive study.

Input cost inflation (energy, steel, labor, reagents) erodes profit margins.

ESG-driven investor exclusions make it impossible to raise equity capital.

Offtake agreements cannot be secured on acceptable terms. This can be important in industrial and battery mineral projects. This may require focusing on downstream processing into upgraded specialty products, increasing risks and costs.

Conclusion

The list of potential production roadblocks is extensive. Moving from the study stage to production is very difficult and very few can do it successfully. A positive feasibility study is a necessary but far from sufficient condition for production.

When projects stall, it is likely due to multiple factors listed above. A ranking analysis may conclude the compounding effects of the perceived risks makes the project a no-go for financiers.

Some people will say be thankful that more projects don’t advance to production, because we would likely see more failures as the risks come to bear Ideally only the best projects are moving forward, but even there we can see mixed results.

In case you missed it, the last blog post was “The Double Life of Inferred Resources: Now You See It – Now You Don’t“. The entire blog post library can be found at https://kuchling.com/library/

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There is a unique paradox sitting at the heart of how the mining industry evaluates its projects. It’s called the Inferred Resource—now you see it, now you don’t.

What Are Inferred Resources?

Inferred resources represent the lowest confidence category of mineral resources; typically estimated in zones with limited sampling and unconfirmed geological continuity. They carry the highest geological uncertainty of the three resource categories.

In Preliminary Economic Assessments (PEAs), Inferred resources are allowed—but only under certain conditions:

They can be included in mine plans and economic models, which is the reason PEAs exist: to allow early-stage projects to test economic viability using all available resource data.

Inferred tonnes are routinely used to extend mine life or improve project economics in PEA studies. Investors must understand this risk and should examine the proportion of mined tonnage that is classified as Inferred. This breakdown is normally presented in the Technical Report.

Conversely, in Pre-Feasibility Studies (PFS) and Feasibility Studies (FS), the rules for Inferred material are different:

Inferred resources cannot be included in mineral reserve estimates or in the economic analysis underpinning a PFS or FS. Inferred “ore” is treated as waste rock.

Only Indicated and Measured resources can be converted to Probable and Proven mineral reserves.

Including Inferred material in a PFS mine plan would typically disqualify the study from being used for project financing or a production decision.

Some companies might include Inferred material in a PFS as “upside” or as a sensitivity case, but that analysis must be clearly distinguished from the base case.

The resource upgrade requirement (from Inferred to Indicated) creates some interesting dynamics:

Companies will look at ways to compensate for the Inferred material deduction. Cutoff grade changes and step out drilling are ways to mitigate this impact.

A large Inferred resource that cannot be upgraded without great cost (due to depth, remoteness, or lack of ore-zone continuity) can permanently stall a project at the PEA stage. Hence, one may see multiple PEAs completed on the same project (i.e., the PEA loop).

In closing, the peculiarity of the Inferred resource is that it is essentially a tiered permission structure. Inferred resources are useful for early economic screening, but they must be converted to higher-confidence categories before they can support a bankable study or project debt financing.

Permitting via a PEA

Companies sometimes will commence the permitting process based on their PEA study. There are some risks to doing this, and the Inferred resource creates one of these risks.