The Abstract Art of Resource Estimation or, What Went Wrong This Time? - Brent Cook, Joe Mazumdar
Kitco Commentaries | Opinions, Ideas and Markets Talk
Featuring views and opinions written by market professionals, not staff journalists.
Recent news of Pretium Resources’ (PVG.T, PVG.NYSE) 20% to 25% lower grade reconciliation at Brucejack underground gold project in northern British Columbia prompted us to figure out why investors in the mining sector are repeatedly faced with the nightmare of a problematic resource estimate.
The news dropped the share price by ~40% and excised ~C$950 million from its market capitalization. Unfortunately, this is not an isolated case. There is no shortage of examples of resource estimates falling short of expectations despite the fact that reserves had already been declared. We have discussed Fatal Flaws in past commentaries noting that faulty resource and reserve estimates are the number one cause of mining failures
The calculation of a mineral resource is a fundamental part of a project’s evaluation and, ultimately, determines the worth of the company that owns it. The goal of the exercise is to generate a plausible depiction of how the economic fraction of the metallic mineralization is distributed in the ground by combining geology, or at least the interpretation of it based on limited information, laboratory results (assays), and statistics.
Easy enough; so, why all the mistakes?
An independent Qualified Person (QP) is the person in charge of calculating the resources which are then categorized according to standards established by a governing body (in Canada, it’s the Canadian Institute of Mining or CIM) (Fig. 1). The degree of confidence of a resource estimate is directly related to the quality of the geological interpretation, the sampling protocol, and the statistical methods applied to support a mine plan. The higher the confidence, the lower the risk. Unless, of course, the confidence is misplaced.
(Figure 1: Relationship between mineral resources and mineral reserves, Source: CIM Standing Committee on Reserve Definitions [link here])
The process of resource estimation, particularly in the case of Pretium’s VOK deposit that hosts extremely erratic and spotty gold mineralization, is complex to say the least. The resource model relies on the interpolation of grade and tonnage between drill holes that have the diameter of a good bottle of Malbec, or less, and realistically represent anywhere from one in a million (0.0001%) to one in tens of millions (0.00001%) of the actual tonnage.
Deposits with good continuity are more predictable and easier to model. A good example is the Kamoa sediment-hosted copper deposit in the Democratic Republic of Congo operated by Ivanhoe Mines (IVN.T). Its layer-cake-like mineralization can be observed over hundreds of meters, (Fig. 2).
(Figure 2: The continuous and layered nature of the sediment-hosted copper mineralization at Kamoa resembles a layered cake, Source: Ivanhoe Mines)
Conversely, the variability of the gold mineralization at the Valley of the Kings (VOK) deposit is extreme. Exploration Insights discussed it at large back on June 16, 2013 where we concluded that “The deposit’s geological and structural complexity makes it virtually impossible to precisely predict where the economic gold grades will occur on the drill hole scale.” Several years later, our opinion remains unchanged.
Compared to the 2013 mineral resource estimate, the most recent estimate dated July 2016 reported an increase of ~60% in the Measured category to 1.9 million ounces plus an ~5% increase in the Measured and Indicated to 9.1 million ounces.
But that’s not the whole story. The maximum recorded grade from the ~79,700 drill samples used in the 2013 resource was >16,550 grams per tonne; however, the average grade was only 2.5-2.6 grams per tonne and the coefficient of variation (CV), which would be considered extreme at 5, was ~27.
[The coefficient of variation (CV) is the ratio of the standard deviation to the average of the assay results. In this case, 69.54/2.57 = 27. The higher the CV, the more variable and erratic the distribution of the mineralization is, the more difficult it is to model.]
Due to the high degree of variability, the resource at VOK is clearly driven by the top percentile of the grade (>99.5th percentile or >85 g/t Au). Based on the results from the bulk tonnage sample, we calculate that ~85% of the gold must reside within only 0.5% of the estimated tonnage. Therefore over or underestimating how often that raisin of gold occurs by just a few percent can have a major impact on the estimate.
A colleague has likened the gold mineralization at VOK to raisin bread, (Fig. 3): slice it too thin (low throughput rate), and you might not get any raisins (gold). To avoid missing out on extracting enough ounces, the underground throughput rate for the VOK deposit is fairly high (2,700 tonnes per day) and may get even higher (+40%, 3,800 tonnes per day). Although the bigger the slice, the higher the probability one will get a raisin, this comes at a cost with more dilution. No knowing where the raisins (gold) are guarantees Pretium’s quarterly production will be highly variable and unpredictable.
(Figure 3: Raisin bread [left] and very high grade gold mineralization from the Valley of the Kings [right], Source: Pretium Resources)
A few more examples - Too many to cover!
Trust no one, check everything
Flawed estimations of mineral resources are one of the primary causes of investor strife and recurring nightmares. Many investors in the mining sector, especially the gold sector where the goal is to extract parts per million, don’t recognize the impact of this risk when looking for new opportunities.
After spending the time reviewing these cases and discussing the topic with people that estimate mineral resources for a living, we put together a list of factors that we think directly impact mineral resource estimates and can serve as a guide for investors looking for potential flaws in a project:
In our case, we tend to avoid pointing fingers at the consultants when a resource estimate goes wrong and instead prefer to lay the blame squarely on the company that hired them.
That’s the way we see it,