Grade-Tonnage Curves – Worthy of a Good Look

Most of us have seen the typical “grade-tonnage” table or graph, showing ore tonnes and grade at varying cutoff grades. It is usually part of every 43-101 technical report in Section 14.  We may glance at it quickly and then move on to more exciting chapters. Section 14 (Mineral Resources) can be a very complex chapter to read with statistics, geostatistics, and mathematical formulae.  However the grade-tonnage curve aspect isn’t complicated at all.
The next time you see the grade-tonnage relationship, I suggest taking a few seconds to study it a bit further.   There might be some interesting things in there.

Typical Grade-Tonnage Information

Typically, one will see grade-tonnage data in 43-101 Technical Reports towards the back of Section 14 "Mineral Resources".  The information is normally presented in either of two ways; (i) a grade-tonnage table or (ii) a grade tonnage graph.  Examples of each are shown below.  The grade tonnage graph typically has the cutoff grade along the bottom x-axis and the two separate y-axes  representing the ore tonnes above cutoff and the average ore grade above cutoff.
typical grade tonnage table
typical grade tonnage curve
Rarely do you see both the table and curve in the report, although ideally one would want to see both.  Given the option, I would prefer to see the graph more than the table of numbers.  The trend of the grade-tonnage information is just as important as the values, maybe even a bit more important.  Unfortunately, a data table by itself doesn’t illustrate trends very well.

Useful Grade-Tonnage Curve Information

mining grade tonnage curveWhen I am undertaking a due diligence review or working on a study, very early on I like to have a look at the grade-tonnage information.  This could be for the entire deposit resource, within a resource constraining shell, or in the pit design.
The grade-tonnage information gives an understanding of how future economics or technical issues may impact on the mineable tonnage.
An example of a typical grade-tonnage curve is shown here.
The cutoff grade along the x-axis will be impacted by changes in metal price or operating cost. The cutoff grade will increase if metal prices decrease or if operating costs increase.
The question is how sensitive is the mineable tonnage to these economic factors. The slope of the tonnage and grade curves will help answer this question.
In the example shown, the tonnage curve (blue dots) is fairly linear, meaning the ore tonnage steadily decreases with increasing cut-off grade.  That is expected and is reasonable.
mining grade-tonnage curveHowever, if the tonnage curve profile resembled the light blue line in this image, with a concave shape, the ore tonnage is decreasing rapidly with increasing cutoff grade.   This is generally not a favorable situation.
It indicates that a significant portion of the tonnage has a grade close to the cutoff grade.  If that’s the situation, the calculation of the cutoff and the inputs used to generate it are important and worthy of scrutiny.  Are they reasonable?  Over the long term, is the cutoff grade more likely to increase or decrease?
The same logic can be used with the ore grade curve in the graph.  As  shown in this example, the ore grade increases steadily as the cutoff is raised.  This is because lower grade ore is being shifted from ore to waste, and hence the remaining ore has better quality.  If the cutoff is raised from 0.4 g/t to 0.5 g/t, then some material with a grade of about 0.45 g/t is moved from ore to waste.
I also like to compare the ratio of the average grade to the cutoff grade.  Its nice to see a ratio of 4:1 to 5:1 to ensure the overall average grade isn’t close to the cutoff.  In this example, the cutoff grade is 0.5 g/t and the average grade is 4.5 g/t, a ratio of 9:1.
The tonnage curve and grade curve provide information on the nature of the mineral resource. Study them both.

Reporting Waste Within a Shell

One complaint I have about reporting mineral resources inside a resource constraining shell is the lack of strip ratio information. This applies whether disclosing a single mineral resource estimate or variable grade-tonnage data.
In my view, the strip ratio is even more important to be aware of when looking at grade tonnage data.
The strip ratio within a shell will climb as an increasing cutoff grade results in a decreasing ore tonnage.  Sometimes the strip ratio will increase exponentially. The corresponding amount of waste remaining in that pit shell increases, hence the ratio of the two (i.e. strip ratio) can escalate rapidly.
mining strip ratio curveRegarding mineral resources, one should be required to disclose the waste tonnage and strip ratio when reporting resources inside a constraining shell. The constraining shell and cutoff grade are both based on defined economic factors such as unit mining costs, processing cost, process recoveries, and metal prices.  With respect to the mining cost component, the strip ratio is a key aspect of the total mining cost, yet it normally isn’t disclosed.
Its common to see mention that the mining cost is (say) $2.50/t, but if the strip ratio is 10:1, that equates to an effective mining cost of $27.50 per tonne of ore.   That’s an important cost to know, especially if one is pushing a pit shell deep to maximum the mineral resource tonnage.
Each mineral deposit resource model can behave differently.  Hence, in my view, the waste tonnage should be included when reporting mineral resource tonnages (or presenting grade-tonnage data) within a constraining shell.  This waste tonnage or strip ratio can be in the footnotes to the mineral resource summary table.

Spider Diagram Downsides

In 43-101 technical reports, the financial Chapter 22 normally presents the project sensitivities expressed in a spider diagram or a table format.
In a previous blog post I had discussed the flaws in the spider diagram approach.  That article link is at “Cashflow Sensitivity Analyses – Be Careful”.  The grade-tonnage curve helps explain why that is.
In the spider diagrams, we typically see sensitivities related to +/- 20% on metal prices and operating costs.    If either of these factors change, then in reality the cutoff grade would change.
If the metal price decreases by -20%, or the operating cost climbs by +20%, the cutoff grade must increase.  This adjustment is normally not made in the sensitivity analysis because it requires a lot of re-work.
Elevating the cutoff grade would shift the pit ore tonnage towards the right on the grade-tonnage curve, showing a decrease in mineable tonnes.   However, in the spider diagram logic, the assumption is that production schedule in the cashflow model is unchanged and simply the metal prices or operating costs are adjusted.  Therefore, the spider diagram can be a misleading representation of the downside risk, showing a more positive situation than in reality.

Conclusion

The grade-tonnage information is always presented in technical reports. It examines the sensitivity of the orebody size to changes in cutoff grade. The next time you see grade-tonnage data, don’t skip over it.  Take a minute to study it further to see what can be learned.
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Mining Under Lakes – Part 2: Design Issues

This is Part 2 of a blog post related to open pit mining within bodies of water. Part 1 can be found at this link “Mining Under Lakes – Part 1“, which provides a few examples where this has been done successfully. Part 2  focuses on some of the social and technical issues the need to be considered when faced with the challenge of open pit mining within a water body.
dike construction in waterThe primary question to be answered is whether one can mine safely and economically without creating significant impacts on the environment.
The answer to this question will depend on the project location and the design of the water retaining structure.
I have worked on several projects where dike structures were built. I have also undertaken due diligence reviews of projects where dikes would be required. Most recently I have participated in some scoping level studies where mining within a lake or very close to a river were part of the plan.
In some instances, the entire orebody is located in the lakebed. In others, the orebody is mainly on land but extends out into the water. Each situation will be unique. In northern Canada, given the number of lakes present, it would be surprising if a new mining project isn’t close to a river or lake somewhere.

Dike concepts consider many factors

Different mining projects may use different styles of dikes, depending on their site conditions. Some dikes may incorporate sheet piling walls, slurry cutoff walls, low permeability fill cores, or soil grouting. There are multiple options available, and one must choose the one best suited for the site.
The following is list of some of the key factors and issues that should be examined.

ESG Issues

One’s primary focus should be on whether building a dike would be socially and environmentally acceptable. If it is not, then there is no point in undertaking detailed geotechnical site investigations and engineering design. One must have the “social license” to proceed down this path.
Water Body Importance: Is there a public use of the water body? It could be a fresh water source for consumption, used for agricultural or fishery purposes, or used as a navigable waterway, etc. Would the presence of the dike impact on any of these uses? Does the water body have any historical or traditional significance that would prevent mining within it?
Lake Turbidity: Dike construction will need to be done through the water column. Works such as dredging or dumping rock fill will create sediment plumes that can extend far beyond the dike. Is the area particularly sensitive to such turbidity disturbances, is there water current flow to carry away sediments?
At Diavik, a floating sediment curtain surrounding the dike construction area was largely able to contain the sediment plume in the lake.
Regional Flow Regime: Will the dike be affecting the regional surface water flow patterns? If the dike is blocking a lake outflow point, can the natural flow regime be maintained during both wet and dry periods?

Location Issues

If there are no ESG issues preventing the use of a dike, the next item to address is the ideal location for it.
Water depth: normally as the dike moves further away from land, both the water depth and dike length will increase. The water depth at the deepest points along the dike are a concern due to the hydraulic head differential created once the interior water pool is pumped out. The seepage barrier must be able to withstand that pressure differential, without leaking or eroding. A low height dike in shallow water may be able to use a simpler seepage cutoff system than a dike in deep water.
Islands: Are there any islands located along the dike path that can be used to shorten the construction length and reduce the fill volumes? Is there a dike alignment path that can follow shallower water zones?
Diavik open pit dikesPit wall setback: Given the size and depth of the open pit, how far must the dike be from the pit crest? Its nice to have 200 metre setback distance, but that may push the dike out into deeper water.
If the dike is too close to the pit, then pit slope failures or stress relaxation may result in fracture opening and increase the risk of seepage flows or catastrophic flooding. The pit wall rock mass quality will be the key determining factor in the setback distance.
Maximizing ore recovery: If the ore zone extends further out into the lake, maximizing ore recovery may require using a steep pit wall along the outer sections of the pit. This may require positioning haulroads with switchbacks along other sides of the pit rather than using a conventional spiral ramp layout.
At Diavik (see image), the A154 north open pit wall was pushed to about 60 metres of the dike to access as much of the A154N kimberlite ore as possible. Haulroads were kept to the south side of the pit.
It may be possible to recover even more ore by pushing out the dike even further. However, this may result in a larger and costlier dike or even require a different style of dike. There will be a tradeoff between how much additional ore is recovered versus the additional cost to achieve that. There will be a happy medium between what makes both technical sense and economic sense.

Design Issues

Once the approximate location of the dike has been identified, the next step is to examine the design of the dike itself. Most of the issues to be considered relate to the geotechnical site conditions.
Lakebed foundation sediments: What does the lakebed consist of with respect to soft sediments? Soft sediments can cause dike settlement and cracking, or mud-waving of fill material.
Will the soft sediments need to be dredged prior to construction, and if so, where do you dispose of this dredge slurry, and what impact will dredging have on the lake turbidity?
Lakebed foundation gravels: Are there any foundation gravel layers that can act as seepage conduits beneath the dike? If so, will these need to be sub-excavated, or grouted, or cut off with some type of barrier wall?  Sonic drilling, rather than core drilling, is a better way to identify the presence of open gravel beds.
Upper bedrock fracturing: Is the upper bedrock highly fractured, thereby creating leakage paths? If so, then rock grouting may be required all along the dike path to seal off these fractures.
Major faults: Are there any major faults or regional structures that could connect the open pit with the lake, acting as a source of large water inflow?. At Diavik, we attempted to characterize such structures with geotechnical drilling before construction. Upon review, I understand there was one such structure not identified, which did result in higher pit inflows until it was eventually grouted off.
Water level fluctuations: In a lake or river one may see seasonal water level fluctuations as well as storm event fluctuations. The height of the dike above the maximum water level (i.e. freeboard) must be considered when sizing the dike.
Ice scouring: In a lake or river that freezes over, ice loads can be an important consideration. During spring breakup as the ice melts, large sheets of ice can be pushed around and may scour or damage the crest of the dike. The dike must be robust enough to withstand these forces.
Construction materials available on site: Is there an abundance of competent rock for dike fill? Is there any low permeability glacial till or clay that can be used in dike construction? If these materials are available on site, the dike design may be able to incorporate them. If such materials are not available, then a alternate dike design may be more appropriate, albeit at a cost.

Conclusion

Each mine site is different, and that is what makes mining into water bodies a unique challenge. However many mine operators have done this successfully using various approaches to tackle the challenge.
Even at the exploration stage, while you are still core drilling the orebody through the ice, you can start to collect some of this information to help figure it all out.
The bottom line is that while mining into a water body is not a preferred situation, it doesn’t mean the project is dead in the water. It will add capital cost and environmental permitting complexity, but there are proven ways to address it.
On the opposite side, I have also seen situations where a dike solution was not feasible, so ultimately there are no guarantees that engineers can successfully address every situation. Lets hope your project isn’t one of them.
There could be a 3rd part to this post that discusses issues associated with underground mining beneath bodies of water; however that is not my area of expertise.  I would be more than happy to collaborate on a article with someone willing to share their knowledge and experience on that subject.
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NPV One – Cashflow Modelling Without Excel

NPV One mining software
From time to time, I encounter interesting software applications related to the mining industry.  I recently became aware of NPV One, an Australian based, cloud hosted application used to calculate mineral project economics. Their website is https://npvone.com/npvone/
NPV One is targeting to replace the typical Excel based cashflow model with an online cloud model. It reminds me of personal income tax software, where one simply inputs the income and expense information, and then the software takes over doing all the calculations and outputting the result.
NPV One may be well suited for those not comfortable with Excel modelling, or not comfortable building Excel logic for depreciation, income tax, or financing calculations. These calculations are already built in the NPV One application.
I had a quick review of NPV One, being given free access to test it out. I spent a bit of time looking at the input menus and outputs, but by no means am I proficient in the software after this short review.
Like everything, I saw some very good aspects and some possible limitations. However, my observations may be a bit skewed since I do a lot of Excel modelling and have a strong comfort level with it. Nevertheless, Excel cashflow modelling has its own pro’s and con’s, some of which have been irritants for years.

NPV One – Pros and Cons

NPV One mining softwarePros

  1. NPV One develops financial models that are in a standardized format. Models will be very similar to one another regardless of who creates it. We are familiar with Excel “artists” that have their own modelling style that can make sharing working models difficult. NPV One might be a good standard solution for large collaborative teams looking at multiple projects while working in multiple offices.
  2. NPV One, I have been assured, is error free. A drawback with Excel modelling is the possibility of formula errors in a model, either during the initial model build or by a collaborator overwriting a cell on purpose (or inadvertently).
  3. With NPV One, a user doesn’t need to be an Excel or tax modelling expert to run an economic analysis since it handles all the calculations internally.
  4. NPV One allows the uploading of large input data sets; for example life-of-mine production schedules with multiple ore grades per year. This means technical teams can still generate their output (production schedules, annual cost summaries, etc.) in Excel. They can then simply import the relevant rows of data into NPV One using user-created templates in CSV format.
  5. As NPV One evolves over time with more client input, functionality and usability may improve as new features are added or modified.

Cons

Like anything, nothing is perfect and NPV may have a few issues for me.
  1. Since I live and breathe with Excel, working with an input-based model can be uncomfortable and take time to get accustomed to. Unlike Excel, in NPV One, one cannot see the entire model at once and scroll down a specific year to see production, processing, revenue, costs, and cashflow. With NPV jump to. If you’re not an avid Excel user, this issue may not be a big deal.
  2. In Excel one can see the individual formulas as to how a value is being calculated.  Excel allows one to follow a mathematical trail if one is uncertain which parameters are being used. With NPV One the calculations are built in. I have been assured there are no errors in NPV One, so accuracy is not the issue for me. It’s more the lack of ability to dissect a calculation to learn how it is done.
  3. With NPV One, a team of people may be involved in using it. That’s the benefit of collaborative cloud software. However that means there will be a learning curve or training sessions that would be required before giving anyone access to the NPV One model.  Although much of NPV One is intuitive, one still needs to be shown how to input and adjust certain parameters.
  4. Currently NPV One does not have the functionality to run Monte Carlo simulations, like Excel does with @Risk. I understand NPV One can introduce this functionality if there is user demand for it. There will likely be ongoing conflict to try to keep the software simple to use versus accommodating the requests of customers to tailor the software to their specific needs.

Conclusion

The NPV One software is an option for those wishing to standardize or simplify their financial modelling.
Whether using Excel or NPV One, I would recommend that a single person is still responsible for the initial development and maintenance of a financial model. The evaluation of alternate scenarios must be managed to avoid it becoming a modelling team free for all.
Regarding the cost for NPV One, I understand they are moving away from a fixed purchase price arrangement to a subscription based model. I don’t have the details for their new pricing strategy as of May 2023. Contact Christian Kunze (ck@npvone.com) who can explain more, give you a demo, and maybe even provide a trial access period to test drive the software.
To clarify I received no compensation for writing this blog post, it is solely my personal opinion.
Regarding Excel model complexity mentioned earlier, I have written a previous blog about the desire to keep cashflow models simple and not works of art. You can read that blog at Mine Financial Modelling – Please Think of Others”.
As with any new mining software, I had also posted some concerns with QP responsibilities as pertaining to new software and 43-101. You can read that post at the appropriately titled “New Mining Software and 43-101 Legal Issues”.

 

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Steeper Pit Slopes Can Save Money

We likely have all heard the statement that increasing pit wall angles will result in significant cost savings to the mining operation.
What is the potential cost saving?
The steeper wall angles reduce waste stripping volumes, which also provide other less obvious benefits.
I was recently in a situation where we undertook some comparative open pit designs using both 45 and 50 degree inter-ramp angles (“IRA”). I would like to share some of those results and discuss where all the benefits may lay.

Comparative Pit Designs

In this project, four separate open pits were designed with 45 and 50 degree IRA’s in an area with hilly topography. Some of the pits had high walls that extended up the valley hillsides. Its not hard to envision that waste stripping reductions would be seen along those areas with steepened walls.
The results of applying the increased  inter-ramp angle to each of the four pits is shown in the Bar Chart. Note that the waste reduction is not necessarily the same for each pit.  It depends on the specific topography around each pit.
However, on average, there was an overall 15% reduction in waste tonnage.
The Table shown below presents the cumulative tonnage for all four pits. The 50 degree wall results in a waste decrease of 25.4 million tonnes (15%), with a strip ratio reduction from 5.8:1 to 5.0:1.
There is also a very minor decrease in ore tonnage. This is because the 50 degree slopes did lose some ore behind the walls that is being recovered by the 45 degree slope.
In both scenarios the project life would be about 10 years at an assumed ore processing rate of 3 Mtpa.

4 Positive Impacts of Steeper Walls

In general one can typically see four positive outcomes from adopting steeper pit walls. They are as follows:
1. Cost Savings: The waste tonnage reduction over the 10 year life would be about 25.4 million tonnes. At a mining cost of $2.00/tonne, this equates to $50.8 million tonnes spent less on stripping. This could move the project NPV from marginal to profitable, since most waste is normally stripped towards the front part of the mining schedule with less discounting.
The next time you are looking at the NPV from an open pit project, take a quick look to see if the pit slope assumptions are conservative or optimistic. That decision can play a significant role in the final NPV.
2. Equipment Fleet Size: Over the 10 year life, the average annual mining rate would range from 20.5 Mtpa (45 deg) to 18.1 Mtpa (50 deg). On a daily basis, the average would range from 56,100 tpd (45 deg) versus 49,700 tpd (50 deg). While this mining rate reduction is not likely sufficient to eliminate a loader, it could result in the elimination of a truck or two.   This would have some capital cost saving.
3. Waste Dump Size: The 15% reduction in the waste tonnage means external waste dumps could be 15% smaller. This may not have a huge impact but could be of interest if waste storage sites are limited on the property. It could have a more significant impact if local closure regulations require open pit backfilling.
4. Pit Crest Location: The steeper wall angles result in a shift in the final pit crest location. The Image shows the impact that the 5 degree steepening had on the crest location for one of the pits in this scenario.
Although in this project the crest location wasn’t critical, there are situations where rivers, lakes, roads, mine facilities, or public infrastructure are close to the pit.  A steeper wall could improve ore recovery at depth while maintaining the same buffer setback distance.

Conclusion

Steeper pit walls can have multiple benefits at an open pit mining operation. However, these benefits can all be negated if the rock mass cannot tolerate those steeper walls. Pit wall failures could be minor or they could have major impacts. There are the obvious worker safety issues, as well as equipment damage and production curtailment concerns with slope failures.  Public perception of the mining operation also comes into play with dangerously unstable slopes.
Steepening of the pit walls is great in theory, but always ensure that geotechnical engineers have confirmed it is reasonable.
It is relatively easy to justify spending additional time and money on proper geotechnical investigations and geotechnical monitoring given the potential slope steepening benefits.
When designing pits, there is some value in looking at alternate designs with varying slope angles to help the team understand if there are potential gains and how large they might be.
In closing, I previously wrote a related blog post about how pit walls are configured to ensure safe catch bench widths and decisions as to whether one should use single, double, or triple benching. That earlier post can be read at this link. Pit Wall Angles and Bench Widths – How Do They Relate?
Feel free to share your personal experiences if you are aware of other benefits (or even downsides) to steeper pit walls that I did not mention.
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Resources, Resources, and Mineral Reserves

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

The issue

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

Suggestion

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

Examples

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

Conclusion

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

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

Excerpts from the article

Mining can be risky

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

Monte Carlo can be risky

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

Image from the 4-D Resources article

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

Conclusion

image from 4-D Resources article

Improvements are needed in the way risks are evaluated and explained to mining stakeholders. Improvements are required given increasing complexity in the risks impacting on decision making.
The probabilistic risk evaluation approach described above isn’t new and isn’t that complicated. In fact, it can be very intuitive when undertaken properly.
Probabilistic risk analysis isn’t something that should only be done within the inner sanctums of large mining companies. The approach should filter down to all mining studies and 43-101 reports.
It should ultimately become a best practice or standard part of all mining project economic analyses. The more often the approach is applied, the sooner people will become familiar (and comfortable) with it.
Mining projects can be risky, as demonstrated by the numerous ventures that have derailed. Yet recognition of this risk never seems to be brought to light beforehand.
Essentially all mining projects look the same to outsiders from a risk perspective, when in reality they are not. The mining industry should try to get better in explaining this.
Management understandably have a difficult task in making go/no-go decisions. Financial institutions have similar dilemmas when deciding on whether or not to finance a project.   You can read that blog post at this link “Flawed Mining Projects – No Such Thing as Perfection
UPDATE:  For those interesting in this subject, there is a follow up article by the same author published in January 2022 titled “Using Dynamic Financial Modeling to Enhance Insights from Financial Reports!“.
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Pit Optimization – More Than Just a “NPV vs RF” Graph

In this blog I wish to discuss some personal approaches used for interpreting pit optimization data. I’m not going to detail the basics of pit optimization, assuming the reader is already familiar with it .
Often in 43-101 technical reports, when it comes to pit optimization, one is presented with the basic “NPV vs Revenue Factor (RF)” curve.  That’s it.
Revenue Factor represents the percent of the base case metal price(s) used to optimize for the pit. For example, if the base case gold price is $1600/oz (100% RF), then the 80% RF is $1280/oz.
The pit shell used for pit design is often selected based on the NPV vs RF curve, with a brief explanation of why the specific shell was selected. Typically it’s the 100% RF shell or something near the top of the NPV curve.
However the pit optimization algorithm generates more data than just shown in the NPV graph.  An example of that data is shown in the table below. For each Revenue Factor increment, the data for ore and waste tonnes is typically provided, along with strip ratio, NPV, Profit, Mining cost, Processing, and Total Cost at a minimum.
Luckily it is quick and easy to examine more of the data than just the NPV curve.

In many 43-101 reports, limited optimization analysis is presented.  Perhaps the engineers did drill down deeper into the data and only included the NPV graph in the report for simplicity purposes. I have sometimes done this to avoid creating five pages of text on pit optimization alone, which few may have interest in. However, in due diligence data rooms I have also seen many optimization summary files with very limited interpretation of the optimization data.
Pit optimization is a approximation process, as I outlined in a prior post titled “Pit Optimization–How I View It”. It is just a guide for pit design. One must not view it as a final and definitive answer to what is the best pit over the life of mine since optimization looks far into the future based on current information, .
The pit optimization analysis does yield a fair bit of information about the ore body configuration, the vertical grade distribution, and addresses how all of that impacts on the pit size. Therefore I normally examine a few other plots that help shed light on the economics of the orebody. Each orebody is different and can behave differently in optimization. While pit averages are useful, it is crucial to examine the incremental economic impacts between the Revenue Factor shells.

What Else Can We Look At?

The following charts illustrate the types of information that can be examined with the optimization data. Some of these relate to ore and waste tonnage. Some relate to mining costs. Incremental strip ratios, especially in high grade deposits, can be such that open pit mining costs (per tonne of ore) approach or exceed the costs of underground mining. Other charts relate to incremental NPV or Profit per tonne per Revenue Factor.  (Apologies if the chart layout below appears odd…responsive web pages can behave oddly on different devices).

Conclusion

It’s always a good idea to drill down deeper into the optimization output data, even if you don’t intend to present that analysis in a final report. It will help develop an understanding of the nature of the orebody.
It shows how changes in certain parameters can impact on a pit size and whether those impacts are significant or insignificant. It shows if economics are becoming very marginal at depth. You have the data, so use it.
This discussion presents my views about optimization and what things I tend to look at.   I’m always learning so feel free to share ways that you use your optimization analysis to help in your pit design decision making process.
As referred to earlier, there is a lot of uncertainty in the input parameters used in open pit optimization.  These might include costs, recoveries, slope angles and other factors.  If you would like to read more, the link to that post is here.  “Pit Optimization–How I View It”.
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Gold Exploration Intercepts – Interesting or Not?

As a mining engineer, I am not usually called in to review a project that is still at the exploration stage. This is normally the domain of the geologist. However from time to time I have an interest in better understanding the potential of an early stage mining project. This could be on behalf of a client, for investing purposes, or just for personal curiosity.
At the exploration stage one only has drill interval data from news releases to examine. A resource estimate may still be unavailable.
The drill data can consist of long intervals of low grade or short intervals of high grade and everything in between. What does it all mean and what can it tell you?
The following describes an approach I use for examining early stage gold deposits. The logic can be expanded to other metals but would take more effort.
My focus is on gold because it has been the predominant deposit of interest over the last few years, and it is simpler to analyze quickly.

We All Like Scatter Plots

My approach relies on a scatter plot to visual examine the distribution of interval thicknesses and gold grades. Where these data points cluster or how they are distributed can provide some prediction on the overall economic potential of a project. Its not a guarantee, but only an indicator.
I try to group the analysis into potential open pit intervals (0 to 200 metres from surface) and potential underground (deeper than 200m) intervals. This is because a 20m wide interval grading 2.0 g/t is of economic interest when near surface, however of less interest if occurring at a 300m depth.
Using information from a news release, I create a two column Excel table of highlighted intervals and assay grades. The nice thing about using intervals is that the company has provided their view of the mineable widths.
If one is provided with raw 1-metre assay data you would have to make that decision, which can be a significant task. The company has already helped make those decisions.
Normally I tend to use the highlighted sub-intervals and not the main intervals since issues with grade smoothing can occur.
A large interval containing multiple high grade sub-intervals may see some grade smoothing.This happens if the grade between sub-intervals is very low grade or even waste. It takes a fair bit of effort to assess this for each drill hole, hence it is easier to work with the sub-intervals.
I have an online calculator (Drill Intercept Calculator) that lets you assess if grade smoothing is occurring.
When inputting the interval thickness, I prefer to use the true thickness and not the interval length. If the assay information does not specify true thicknesses, then I simply multiple the interval length by 0.70 to try to accommodate some possible difference in width. Its all subjective.
The assays can consist of Au (g/t) or AuEq (g/t) if more metals are present. If very high grades are encountered (greater than 10 g/t) I simply input 9.9 g/t into the Excel table so they fit onto my scatter plot. Extremely high grades can be sporadic and localized anyhow.
Finally I need to decide whether the project is located in a region of high operating cost, low cost or about average costs. High costs could be with a fly in/ fly out, camp operation, with diesel power, and seasonal access.
A low cost operation could be in temperate climate, with good access to local infrastructure, water, labour, and grid power. An average operation would be somewhere in between the two. Its just a gut feel.

Results

The following charts describe how it works, using randomly generated dummy assay data in this example.
In the Average cost scenario (left chart) the points are equally scattered both above and below the Likely Economic line. As one moves to a high-cost situation (middle chart) the curve moves upwards and more drill intervals now fall below the economic line.
This would give me an unfavorable impression of the project. The third graph is the Low-Cost scenario and one can see that more assays are now above the line. Hence the same project located in a different region would yield a different economic impression.
The economic boundaries (dashed lines) presented in the plots are based on my personal experience and biases. Other people may have different criteria to define what they would view as economic and uneconomic intervals.

Conclusion

There is not much that a layperson person can do with the multitude of exploration data provided in corporate news releases. However, by aggregating the data one can get a sense of where a gold project positions itself economically. The more data points available, the more that one can gather from the plot.
One should prepare separate plots for shallow and deep mineralization or for different zones and deposits on a property rather than aggregate everything together.
It may be possible to undertake a similar analysis with different commodities if one can summarize the assays into a single equivalent value or NSR dollar value. Unfortunately, exploration news releases don’t often include the poly-metallic interval equivalent grade or NSR value. Calculating these manually would add an extra step in the process, however it can be done.
If you want to try out the concept, I have posted the online spreadsheet to my website at the link Drill Intercept Potential where you can input Au exploration data of interest. Unfortunately, you cannot save your input data so it’s a one time event.   Anyone can do this – its not rocket science.
Let me know your thoughts, suggestions, or other ways to play with news release data.
If your project contains metals other than gold, then the rock (or ore) value will be based on the revenue from a combination of metals.   How to approach this in discussed in another blog post titled Ore Value Calculator – What’s My Ore Worth?

Great Bear Resources Example

Interesting the Great Bear Resources website allows one to download a data file with all their exploration intervals.  I have not seen another company provide this level of transparency.   I download their data file of over 1300 intervals and sub-divided them into major intervals and sub-intervals (more ore less).   The two plots below show the outcome.
The graph on the left is the sub-intervals showing that many points are above the “economic” line.  There are numerous data points along the top axis, indicating many sub-intervals at >10 g/t at widths ranging from 1 to 15 metres.  The graph on the right shows the major intervals.  While there are still many along the top axis, there are now more along the 40m width but at grades ranging from 1 g.t to 6 g/t.
One would surmise from these plots that overall there are many intervals above the line in the economic zone, showing the potential of the project.  It also shows that GBR have encountered many intervals likely sub-economic, but that’s the exploration game.

Great Bear Resources data

Examining polymetallic drill results in a similar manner isn’t as simple as this.   The mutiple metals of interest make the calaculations a bit more complex.   Another blog post discusses the approach I use for polymetallic, at this this link Polymetallic Drill Results – Interesting or Not?
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Two Mining Innovations – Load Scanning & Vehicle Tracking

The mining industry is always on the lookout for new innovations as it strives to keep up with other industries.  In that light, periodically I like to highlight new technologies that I become aware of.   I’m trying to help spread the word  about them, which in turn may assist them in their on-going growth and development.
In this article I want to briefly describe two hardware / software companies that are working on technologies related to mine equipment productivity.    In no particular order, the two companies are Loadscan and SedimentIQ.  SedimentIQ is more of a startup than Loadscan which has a longer operating track record.
These technology companies are both targeting the open pit and underground markets, looking to provide simpler and less costly productivity solutions. Their technologies may be well suited for small to mid tier mines that cannot afford or don’t require the comprehensive Minestar type fleet management systems.
For the record, I get no fee or commission for promoting these companies; I just like what they are doing.

Loadscan

Loadscan has been around for a few years, but I only became aware of it recently.  It is a technology that allows the rapid assessment of the load being carried in truck.  It does not rely on the use of load cells or weigh scales to measure the payload.
Instead Loadscan uses a laser scanner and proprietary software to three dimensionally map the surface of the truck payload and then calculate its volume.  The results will indicate how consistent and optimal truck loading is volumetrically.   One can then calculate the payload tonnage by applying a bulk density.
The Loadscan technology will assess whether trucks are being over or under loaded, whether the loads are off-centre, or whether there is excess carryback on the return trip.
Successive truck payloads can be tracked manually or with RFID tags.   A cloud based database and web based dashboard are used to store the data and summarize it. The output can include an image of each individual load.
What is interesting about this technology is that it is simple to install in an operation.  It does not require retrofitting of a truck.
Results are immediate.  Loadscan provided an example where a message readout board can let the shovel operator immediately know how well each truck was loaded, resulting in improved education and better performance efficiency.
One can also assess how much better shovel bucket factors are in well blasted rock versus in blocky rock.
The Loadscan system is already in use in several mines globally.  The vendors can provide more technical  data if you need it.
Their website is https://www.loadscan.com/

SedimentIQ

SedimentIQ is a new smartphone vehicle tracking platform that is trying to establish itself.  Their proposed technology makes use of a phone’s built-in GPS, Bluetooth, and accelerometer to track vehicle operation.  The phone’s sensor can measure vibrations produced by an operating truck or loader.
Vibration is a fingerprint of a vehicle’s activity.  Therefore using machine learning, the SedimentIQ app can produce an “activity score” that decides whether a machine is parked, idling, or performing productive work.  The phone is not connected to the machine diagnostics system, so its very easy to install, only needing a power source.
The system will be able to be used on any vehicle, including trucks, drills, loaders, graders, dozers, etc. The system has the capability to monitor equipment location and speed.
In an open pit environment it uses the phone’s GPS to monitor vehicle location. In an underground setting the phone reads inexpensive Bluetooth beacons mounted along the side walls to track location.
The app will identify delay and downtime based on equipment vibration levels.  The system currently requires no interaction with the operator, working in the background.  Hence it will not identify the cause of delay (i.e. blasting delay, breakdown, inter-equipment delay, etc).  I would expect that in the future they could add a feature for the operator to tag delay types on the touch screen.
The SedimentIQ software will aggregate the cycle time and delay information and upload it in real time to a cloud based database.  A web-based dashboard allows anyone with access to view the real time production data graphically or export it to Excel.
The SedimentIQ platform is less expensive than high end fleet management software.  Although it may not provide all the bells and whistles of the high end software, it may deliver just what you need to monitor productivity in your mining operation.  It relies on relatively inexpensive smart phones that are locked to the application.
I recall as a mining student doing time studies.  I rode the shift crew bus with pencil in hand, timing the travel  from the mine dry to the various shovels to measure start up times.  I recall sitting with a stopwatch timing shovel buckets and truck loading times.  Both of these tasks can be done for every shift, every truck by equipping the crew bus and mine trucks with the SedimentIQ tech.
The platform is currently being tested at a couple of trials mines and the founders are looking for more mines willing to adopt and further refine their technology.  Lets hope they can make a successful go of it.
The website is https://sedimentiq.com/.

Conclusion

Both of these innovative technologies can provide useful information to open pit and underground mine operations.  They are in the growth stage, looking for wider adoption.   Input from users, whether positive or negative, will assist them with on-going development and enhancements. Their websites obviously have more on what their technology offers, including presentations, white papers, and case studies.
It would be nice to meld these two technologies in some way to allow the SedimentIQ cycle times to also track payloads.
Check them out.  Try them out.

 

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

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

Transitioning isn’t easy

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

Conclusion

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

 

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