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This website is an anaoymous exploration of mining industry. 

The writer is a mining engineer trying to get by in the world of finance.

Here we catalogue this exploration.

Sometimes we’ll explore interesting items pertaining to due diligence, company trends and performance, or assets.

Whenever possible we’ll maximize the use of data in the hopes that we bring a unique perspective and rigor in mining analysis.

Hope you enjoy.


One Reason Why Miners Always Disappoint

I think it’s fair to say that we’ve all grown accustomed to miners overpromising and underdelivering. Whenever a new project is coming online there is a palpable uneasiness that is detectable in analysts, management, and investors. Will the project hit the guidance throughput? Mining Rate? Recovery?

I don’t want to group all projects into the underdeliver bucket but there is certainly a large percentage that not only fail to deliver on ramp-up projections but also in ultimate capacity. The chart below shows actual vs. projected mining rates for a large project that was recently constructed. Now I get that management can be overly optimistic and ramp-ups can be challenging. You’ll notice that the year 1 tonnage is way off of the projection but, by year 2 and 3, the company has closed much of the gap. What I’m more interested in is the levelling off of production and the inability to ever achieve expectations regarding ultimate mining rates.

Management will often highlight specific variables that are performing worse than expected. Shovel downs are decreasing availability. Pioneering is impacting productivity. A large weather storm reduced operating time. These are all possibilities. What I’m interested in, however, is why production falls short of budget when all variables are as expected.

But how can this be? How can production be less than expected when all inputs are as expected? Well, I think that the companies contracted out to perform mining studies underestimate volatility’s role in determining production rates.

Let’s look at a simple example:

The capacity table shows one shovel that is paired with three trucks. You’ll notice that truck and shovel capacity is fairly balanced, both around 25K tons mined per shift. You could expand the time period to encompass a year and this is how most feasibility studies determine equipment requirements. Now they would use variable parameters for each of these factors based on the specific conditions in the mine during the period (haul distance) and planned maintenance.

Ok, nothing wrong here, right?


These assumptions grossly underestimate the influence randomness within each variable. Fluctuations in, say, availability have asymmetric impacts on the production of the mine. -10% one day and +10% the next does not average out to zero impact on the mine. It averages something less.

This histogram of shift-by-shift availability reflects the average that is shown in the table (77%). This histogram reflects the outcome of 730 simulations (1 year’s worth of shifts) of another probability density function that is based on actual data.

These availability values were used to calculate shift production for an entire year. The chart below shows the mine’s production as a function of shovel availability. You’ll notice that tons mined per shift increases linearly as shovel availability is increased -the mine is shovel limited-. Once availability exceeds 70%, however, the mine is limited by truck capacity.

This is the root of the issue. Mine production falls short when shovel availability is lower than average and cannot sufficiently increase production when availability is higher than average. This asymmetry makes it effectively impossible for feasibility parameters to be achieved.

Average simulated production is barely over 20K tons per shift. A far cry (-18.5%) from the 25K tons per shift that would be predicted by the study. Coincidentally, this is the same delta between actual and planned tonnage in Y5 of the earlier chart.

Mines are fragile. They are hurt by volatility and cannot make up production on the positive side because rates are capped by a new bottleneck. It’s my opinion that this static mindset, when it comes to production scheduling, is the root cause of lots of the industry’s issues.

So what do we take away from this very simplified example?

Well, it pays to have buffer (excess) capacity. Miners with small equipment fleets are the most susceptible to volatile operating parameters as they don’t have other units to average out the shift-by-shift outcomes. In general, a static approach to forecasting will overestimate long term production.

Beta and Price to NAV

So I woke up this morning thinking about gold producer price to NAV5% discounts/premiums. I’ve struggled to understand the intuition behind being priced at a premium to NAV5% as this implies that the appropriate discount rate for the stream of cash flows is less than 5%. How can this be given that gold mining is inherently risky. Arguably more so than the typical business in the S&P 500.

Well there are a couple of explanations for this pricing behavior:

  • Analyst NAV assessments underestimate the future stream of cash flows
    • This is possible but I doubt this is the answer as any future resource conversion is going to occur so far in to the future that it would be discounted to oblivion
  • Analysts use a flat price deck when creating the NAV forecasts
    • You could make the case that a nominal price deck should be used but this change would be offset by the use of a nominal discount rate; probably not the answer.
  • NAV5% is not the correct discount rate to be used for specific gold producers.
    • I think this is the underlying logic driving the price to NAV logic.

In the gold space, small companies typically trade at a discount to their NAV. Generally, discount to NAV is negatively correlated with size; smaller company, larger discount. The intuition behind this equilibrium makes sense as larger companies are more diversified (operationally and jurisdictionally). Senior producers generally trade at a premium to NAV and streaming companies trade at an even higher premium.

Being priced at a premium is a challenging concept to understand. At the extreme, this can imply that the purchase of the security will provide cash flows that are less than the purchase price. This is very similar to negative interest rates. Unlike negative interest rates, however, the pricing of these securities is not manipulated by quantitative easing and central banks. The “rational” investor is doing the pricing.

So how can a discount rate of zero (or negative) be justified. The capital asset pricing model states that the equity risk premium is the risk free rate + beta * equity risk premium. The current T-bill rate is around 1% so this implies that the beta of these investments must be zero or slightly negative. This train of thought led me to start looking into beta and the correlation of gold to the broader economy.

So what is beta? I like to think of it as a leverage factor for stock returns. A beta of 1.25 means that the stock will move 125% of the move of the general market. Beta can also be thought of as “risk” factor. A high beta implies high risk -higher volatility- and investors will require a higher rate of return for an investment in the security.

If we look at the beta of monthly returns from 1973 to the end of 2018 we see that gold has exhibited a negative beta with respect to the S&P500. Conversely, copper, oil, and silver have positive betas. These relationships make sense as these other commodities are more related to economic growth and, in turn, the S&P500.

The beta values are misleading as they understate gold’s performance relative the S&P500 during times of instability.

Because, as you’ll see in this chart, gold generally hovers around a beta of zero during periods of relative calm and spikes in other occasions (2008).

This chart that gold acts as insurance during calamity, increasing in value by more than the S&P falls.

Is Grade King? Post 2

This post is a continuation of yesterday’s dive into the relationship between company-wide grade and cash flow from operations. Spoiler alert, there was no relationship. This fact isn’t surprising given the multitude of other factors that impact a company’s cash flow from operations.

I decided to get a little more granular and look at this relationship on an asset basis.

The chart shows that increased production correlates with lower AISC, an expected result given economies of scale. Large scale project require large capex and, as such, require low operating costs to provide attractive rates of return. This makes sense. 

If we compare AISC for open pit operations vs. processed grades we see the trend that we were looking for! Increased grades results in lower AISC. Finally.

Similarly with UG operations, higher grade and lower opex.

Hope you find this interesting.  

Is Grade King?

The purpose of this analysis is to answer the question: “Is Grade King?”

I’ve heard this line often and am interested in correlating mined grade to cash flow from operations to see if there is any relationship.

I’ll start with a broad assessment, ignoring the obvious impact of mine type (UG/OP).

The chart shows a couple of interesting points. Pretium and Kirkland Lake are really in a league of their own in terms of reserve grade for producers with >200K oz per annum of production.

The chart isn’t that useful with these higher reserve grade operators. Let’s take them out.

The chart, with a 5 g/t cap looks as follows:

Moreover, unfortunately, there really isn’t any relationship between reserve grade and CFO/oz (darn). This isn’t that surprising as reserve grades aren’t mined grades and there are countless other factors that impact the profitability of an operation. I was hoping that grade would be king-enough to prove some relationship.

This investigation will require further analysis. While we’re at it. Let’s look at 2018 production.

The chart shows that, as expected, more gold production = more CFO. It’s interesting to note which operators fall above or below the trend line. Kirkland Lake, Newcrest, and Sibanye stand out as a strong performers while Anglo Gold Ashanti is a laggard.

The last chart we’ll look at is a comparison between 2018 CFO per oz produced vs. current enterprise value per ounce. Now we wouldn’t expect CFO to directly translate to enterprise value but I think that this chart is interesting because it provides commentary regarding how much reserve value is going to be captured by the company (or at least the market’s perception of it). The shading of the dots reflects the comparison between these two metrics.

The variation in this metric is very impressive. Take New Gold for example, in 2018 the CFO per oz was close $550 while the company’s enterprise value per reserve ounce is only $100/oz. As mentioned, there are innumerable other items to consider (asset quality, assets not in production, CFI, capital structure).

Not really sure how to conclude this. Relating grade to cash flow is going to be tricky.

Reserve and Resource Additions in 2018

Most of the miners have released 2018 production results and reserve and resource updates. I thought it would be interesting to see which properties added or lost the most ounces during the year.

Out the list there are a couple development projects that saw some serious jumps:

  • Cerro Blanco
  • Cote
  • Kharmagtai
  • Transvaal
  • Misisi
  • Camino Rojo
  • Block 14
  • Curraghinalt
  • Rackla
  • Windfall
  • Eskay Creek
  • Valentine Lake

Would be interesting to look at the corresponding jump in share price of the single asset owners.

Franco Nevada Continued

Mining is risky. With a commodity output, they operate in an environment of perfect competition. It’s capital intensive and they don’t need to contend with depreciation alone, there’s also depletion. Pit walls can fall, people can get injured, and governments can penalize mines at will (nationalization, permitting, fines).

In a previous post I looked at Franco Nevada’s negative retained earnings and meager returns on capital invested. Given historic performance I thought it would be interesting to look at implied returns from prospective investments and the sensitivity of project failure.

Let’s say that Franco invests $250M in eight projects and the expected post-tax return is 4%. Well, what needs to go wrong to obliterate the return of the portfolio.

Well, this would do it:

  • 1 project fails immediately
  • 1 project fails at year eight
  • 1 project fails at year ten
  • 1 project ends two years early

What are the odds that this could happen? That’s an interesting question. It’s certainly higher than 5%. I imagine 50% is closer to reality. There’s also the impacts with grade issues and ramp up.

So what is going on with Solgold?

Yesterday Newcrest announced that they were increasing their stake in Solgold (owner of Cascabel property in Ecuador (85%)). This will take Newcrest’s ownership of Solgold to 15.33%. Newcrest had previously purchaed shares of Solgold in 2016 and 2017. What makes this interesting is that BHP has also shown interest in the property, purchased 100,000 shares in October. Newcrest will not be the larget shareholder in the company, followed by DGR global (11.24%) and BHP (11.18%). 

So what is going on here? Well SolGold has an enterprise value of US$810M so this company is not cheap. Cascabel must be one hot ticket. Yup sure is...

Resource update in November showed 10,900,000 tonnes of copper and 23.2M oz of gold. Wow. Amazing. The resource contains 2.95B tonnes at 0.52% Cu.  Perhaps even more interesting, there’s a high-grade core of 420M tonnes at 1.47%Cu; almsot $100/tonne rock at $3/lb Cu. 

The November 2018 investor presentation highlights the fact that modern exploration activities have allowed the for the discovery of the property. Drill results contain some of the best porphyry copper gold intervals ever recorded. For example. Hole 12, 1560m at .59% Cu and 0.54 g/t. 

The image above (from November 2018 investor presentation) shows the gargantuan extent of the deposit. Based on the geochem data it looks like there are a bunch more targets as well. 

Given Newcrest’s experience with block caving, it’s not surprising how interested they are with the property. Look at the benchmarking!

Solgold also owns multiple subsidiaries with a 3,200 km2 land package.

Well. Solgold is pretty amazing. That’s what’s going on with Solgold.

Mining In Armenia

What is the state of mining in Armenia? How should foreign investors view this country? I’ll spend some time reviewing some recent developments in the country and formulate a view.


  • Gained independence in 1991
  • Had been governed by Serzh Sargsyan since 2007 as part of the right-wing republican party
  • Serzh Sargsyan was reelected for a fourth term in April 2018, peaceful protests started which were concerned about what was starting to look like an indefinite rule
  • Serzh steps down and the leader of the Civil Contract Party is elected (Nikol Pashinyan)

It would be impossible to look at Armenia without investigating the state of affairs with Lydian. Lydian received approval to build the project (Amulsar) in 2014 but construction has been impacted by local protests. 

In August, Armenia’s inspectorate for Natural Protecion and Mineral Resources suggested that the environmental assessment be re-evaluated. 

The inspectorate stated that new ecological factors should be considered for the property. Specifically, there are new sightings of red plants and animal species. 

But get this, so the head (Artur Grigoryan) of the Environmental and Mining Inspection agency directs Lydian to refrain from any mining activities until the ministry can conclude if these new “red plants” are actually at the site. So Grigoryan sends in his team. The team concludes that these new organisms cannot live at this site and are not there. Lydian appeals the original directive, but the appeal is heard by Grigoryan. Obviously, he rejects the appeal which is predicated on information from his own ministry. So now Lydian has challenged Grigoryan’s position through an administrative court. The court has accepted the appeal which suspends Grigoryan’s edict. Great! It doesn’t matter though because the place is still blockaded.  

It’s pretty amazing that a country with 16.8% unemployment is challenging industry and preventing the creation of hundreds of jobs. 

Seems to me that Armenia is not a jurisdiction you want to be developing a project in. 

The McNulty Curve

In general, mining companies have a poor track record of delivering projects on time or on budget. Today, for the first time, I heard about the McNulty Curve; a graph which predicts the level of pain that companies will endure when ramping up a project.

Some previous studies on project cost overruns highlighted a few key reasons for process plant failure (defined as major cost over-run or inability to achieve design capacity):

  1. insufficient effort was devoted to understanding process chemistry
  2. insufficient continuous pilot-scale testing was conducted
  3. the plant lacked parallel process streams and/or in-line spare equipment units
  4. the design incorporated sequential unit operations that either were first-of-a-kind or the largest ever built or both

Terry McNulty advanced this concept and derived ramp up curves for various types of processes. He defined the types as follows:

  1. Series 1
    1. The owners relied on mature technology.
    2. Standard types of equipment were selected.
    3. Thorough pilot-scale testing was done on potentially risky unit operations.
  2. Series 2 
    1. If the technology was licensed, the project was one of the first licensees.
    2. Some equipment was a prototype in size or application.
    3. Pilot-scale testing was incomplete or was conducted on non-representative samples.
    4. Process conditions were unusually severe or corrosive.
    5. Non-innovative parts of the flowsheet received inadequate attention
  3. Series 3
    1. There was very limited pilot-scale testing and important steps were ignored.
    2. Feed characteristics such as mineralogy were poorly understood.
    3. During process development, product quality received little attention.
    4. There were serious design flaws.
    5. Engineering, design, and construction were on a “fast track” with inadequate planning to offset added risk.
  4. Series 4
    1. If continuous tests were run, they were only to make the product.
    2. Equipment was downsized or design criteria were compromised to reduce cost overruns.
    3. The flowsheet was unusually complex with prototype equipment in two or more unit operations.
    4. Process chemistry was poorly understood. 

McNulty highlights a few other factors that were correlated with project over runs, some of which are particularly relevant for the mining industry.

  1. Corporate management had a promotional or overly aggressive attitude.
  2. The owners had very little day-to-day engineering input.
  3. Driving forces underlying the project were ill-conceived.
  4. The ore receiving and preparation areas received little attention.
  5. Translation of the testwork to design criteria was flawed.

Sources: Most of this information was from a paper called Minimization of Delays in Plant Startups. It can be found here: