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.

Mine Reconciliation – Standardization Please

Date: May 7, 2025 Author: Ken Kuchling Comments: Leave a reply

Mine through mill reconciliation, in my view, is an under appreciated topic that could benefit from more conversation amongst industry members. Unfortunately, reconciliation is sometimes viewed as a time consuming frustrating activity, with what some consider less than verifiable results. However given the ongoing innovation that we are seeing in mining, reconciliation may need to play a bigger role than ever.

The mining industry is implementing more and more technology in the mining cycle.

For example, this can range from AI assisted resource modelling, down hole logging, blast movement tracking, GPS controlled dig limits, MineSense bucket grade tracking, load scanning, truck dispatch control, smart mining and edge computing, online grade analyzers, belt weightometers, drone surveying of stockpiles, and real time process controls.

Lots of different innovations are continually being adopted by the mining industry, contrary to what some may say.

The question is does all this innovation improve the overall performance of a mining operation, and if so, by how much? It can cost a lot of money to implement the new technology, is there a payoff?

One cannot answer those questions if one doesn’t undertake proper mine reconciliation. A concern might be that mining is innovating faster than the ability to assess the results of that innovation. To monitor it, you need to measure it.

What is Mine Reconciliation

Mine reconciliation is the process of comparing and aligning the estimated production with the actual production from mining and processing. It requires assessing the accuracy of pre-mining predictions against actual results to identify inconsistencies in the system and hopefully improve it, be it resource estimation, mine planning, or process efficiency. Comparisons can be made between multiple stages in the mining system, as shown in the image below.

Mine reconciliation requires information such as initial predictions from exploration data and geological models, actual measurement: data from mining sources, such as blast holes, stockpile samples, or mill feed. As well it will need data on the final product being shipped off site. Do the metal quantities balance out throughout the mining operation?
Mine reconciliation tends to aggregate over longer time periods (monthly, quarterly or annually) due to short term impacts of material handling in stockpiles and plant circuits and the labour time needed to collect the input data.
Mine reconciliation ultimately attempts to assess how well the delivery of the final metal product relates to the initial resource model (i.e. what the project decision was originally based on)? It is also tool to evaluate the impact of any innovation implementation on the operation.
Reconciliation will help mine operators highlight issues and optimize extraction, manage costs, and ensure compliance with regulatory or investor expectations. Factors such as poor resource estimation, excessive dilution and ore loss, inaccurate sampling, etc. can cause discrepancies, making reconciliation an important part of any operation.
The reconciliation process is also used to derive Mine Call Factors. These factors are used to modify the forecasts from long range models, short range models, and grade control models to better represent the actual performance the operation will likely see. Large call factors suggest something is amiss in the “forecast to actual” progression. The first problem is to identify the causes. The list of the common sources of error can be lengthy. Then, once identified, the second problem is how to fix them.

Harry Parker initially suggested various reconciliation parametrics and labelled them F1, F2, F3. In a 2009 paper by Fouet titled “Standardising the Reconciliation Factors Required in Governance Reporting”, indicates that Rio Tinto had decided upon fifteen (15) different possible reconciliation correlations (see image below). Each one provides an insight on the efficiency of the operation in one way or another. I have seen modified versions of the reconciliation relationships, so it appears there may be no industry standard at this time. Fouet was asking about industry standardization in 2009 (an excellent paper to read by the way).

Mining Codes Getting Involved

It appears that the JORC Code may be recognizing the importance of reconciliation. In an August 2024 Exposure Draft JORC is suggesting the following text: “Where an Ore Reserve has been publicly reported for an operating mine, the results of both production reconciliation and any prior estimate comparison must also be included in the annual Mineral Resources and Ore Reserves statement. Refer to Clause 2.36. The relationships and variables being reconciled must be described in plain language or depicted graphically and must include reconciliations of both the Mineral Resources and Ore Reserves.”

Interestingly, it appears that NI43-101 has not yet jumped on the bandwagon about the importance of disclosing reconciliation results. However, it may just be a matter of time before it becomes one of their disclosure requirements.

If more regulatory focus will be put on mine reconciliation disclosure, then perhaps more industry standardization is warranted. This would help better define some of the terminology and “F factors” shown in the diagrams above to ensure consistency and help avoid each mine doing reconciliation in their own way.

Excel versus Cloud Based Reconciliation

Each mine site may be unique with respect to; ore sources; terminology; ore types; mining methods; stockpiling philosophy; processing methods; technology availability; and personnel capability. So often the easiest approach for mine reconciliation is based on the Excel spreadsheet. (Reconciliation is generally not an easy undertaking).

Spreadsheets can be built site specific, based on an operation’s unique characteristics.

Spreadsheets are often built by a user for that user. They are tailored for the tailor. In my experience, typically the modeler is the only one comfortable with an Excel model’s logic, since all of us may think differently. Unfortunately, with the spreadsheet approach, it becomes more difficult to standardize an industry wide reconciliation process.

An alternate solution to spreadsheets is to use a cloud based standardized software package. Toronto based Minebright has one option, called Pit Info (see link for more info). There are a few other reconciliation software applications available. They tend to be cloud based, hence multiple people can have access to the input modules or output modules. (I would like to thank the Minebright people for steering me towards some of the technical papers on this subject).

The cloud based approach may help make reconciliation a group effort instead of a tightly controlled internal function. It may also help standardize reporting from a company’s multiple operations, reporting from the mining industry globally, or simply for consistent JORC reporting.

The downside to the cloud approach is the mine site teams must learn the software and tailor it to their operation. However, once that hurdle is passed, personnel changes become less onerous due to the model consistency. I have seen cases where a person doing the Excel reconciliation task has left their job, and hence forward the reconciliation effort came to a halt. The people remaining may be too busy or simply don’t want to have to figure out the Excel logic of someone else.

The other nice thing about a cloud software approach is that when improvements are rolled out, every user gets the same update. The “wisdom of crowds” will result in learnings and suggestions that will tend to improve the application functionality over time. There are a lot of smart people out there, and it would be nice to see them working together rather than individually, as the open source software community has demonstrated.

With AI, we also may get to the point where cloud based mine reconciliation platforms can use learnings from other projects, and help identify where the likely technical shortfalls are at a mine site and why production is not reconciling. Let’s ask AI do some of the thinking for us to get to the bottom of a problem.

Conclusion

Mine reconciliation is becoming more and more important, but it can be a forgotten aspect. Sometimes this is because it is difficult to do properly. However, that doesn’t mean it shouldn’t be attempted. The more one can learn about one’s operation, the more likely it can stay efficient, relevant, and in business. The more one knows whether technology improvements are making a difference, the more one may be willing to take on even more new technology.

Shifting some people (like me) away from Excel based solutions can be a challenge, but this is an area where it makes sense. Years ago, many engineers did mine production scheduling using Excel, but we have gradually moved away from that, thankfully. Reconciliation should maybe follow that path.

Disclaimer: Mining is a global business, and perhaps more progress is being made on reconciliation standardization than I am aware of sitting here in my Toronto office. Mine operations around the world are at the forefront of developing new systems. Perhaps we are seeing great things being done in the area of mine reconciliation, or maybe we are not. Please share your experiences if you’re comfortable doing that.

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.

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/

Don’t Cut Corners, Cut Cross-Sections Instead

Date: October 4, 2023 Author: Ken Kuchling Comments: Leave a reply

This article is about the benefit of preparing (cutting) more geological cross-sections and the value they bring.

Geological sections are one of the easiest ways to explain the character of an orebody. They have an inherent simplicity yet provide more information than any other mining related graphic.

Some sections can be simple cartoon-like images while others can be technically complicated, presenting detailed geological data.

Cartoon-stylized sections are typically used to describe the general nature of the orebody. The detailed sections can present technical data such as drill hole traces, color coded assays intervals, ore block grades, ore zone interpretations, mineral classifications, etc.

Sections provide a level of clarity to everyone, including to those new to the mining industry as well as those with decades of experience.

This article briefly describes what story I (as an engineer) am looking for in sections. Geologists may have a different view on what they conclude when reviewing geological sections.

I will describe the three types of geological sections that one can cut and what each may be describing. The three types are: (1) longitudinal (long) sections; (2) cross-sections; (3) bench (level) plans. Each plays a different role in helping to understand the orebody and mining environment.

There is also another way to share simple geological images via3D PDF files. I will provide an example later.

Longitudinal (Long) Sections

Long sections are aligned along the long axis of the deposit. They can be vertically oriented, although sometimes they may be tilted to follow the dip angle of an ore zone.

Long sections are typically shown for narrow structure style deposits (e.g. gold veins) and are typically less relevant for bulk deposits (e.g. porphyry).

The information garnered from long sections includes:

The lateral extent of the mineralized structure, which can be in hundred of metres or even kilometers. This provides a sense for how large the entire system is. Sometimes these sections may show geophysics, drilling to defend the basis for the regional interpretation.
Long sections will often highlight the drill hole pierce points to illustrate how well the mineralized zone is drilled off. Is the ore zone defined with a good drill density or are there only widely spaced holes? As well, long sections can show how deep ore zone has been defined by drilling. On some projects, a few widely spaced deep holes, although insufficient for resource estimation purposes, may confirm that the ore zone extends to great depth. This bodes well for potential development in that a long life deposit may exist.
Sometimes the long section drill intercept pierce points can be contoured on grade, thickness, or grade-thickness. This information provides a sense for the uniformity (or variability) of the ore zone. It also shows the elevations of the higher grade zones, if the deposit is more likely an open pit mine, an underground mine, or a combination of both.

Cross-Sections

Cross-sections are generally the most popular geological sections seen in presentations. These are vertical slices aligned perpendicular to the strike of the orebody. They can show the ore zone interpretation, drill holes traces, assays, rock types, and/or color-coded resource block grades.

As an engineer, my greatest interest is in seeing the resource blocks, color coded by grade. Sometimes open pit shells may be included on the section to define the potential mining volume. The engineering information garnered from block model cross-sections includes:

Where are the higher-grade areas located; at depth or near surface?
If a pit shell profile is included, what will the relative strip ratio look like? Are the ore zones relatively narrow compared to the size of the pit?
How will the topography impact on the pit shape? In mountainous terrain, will a push-back on pit wall result in the need to climb up a hillside and create a very high pit slope? This can result in high stripping ratios or difficult mining conditions.
Does the ore zone extend deeper and if one wants to push the pit a bit deeper, is there a high incremental strip ratio to do this? Does one need to strip a lot of waste to gain a bit more ore?
Are the widths of the mineable ore zones narrow or wide, or are there multiple ore zones separated by internal waste zones? This may indicate if lower-cost bulk mining is possible, or if higher cost selective mining is required to minimize waste dilution.
How difficult will it be to maintain grade control? For example, narrow veins being mined using a 10 metre bench height and 7 metre blast pattern will have difficulty in defining the ore /waste contacts.
Cross-sections that show the ore blocks color coded by classification (Measured, Indicated, Inferred), illustrate where the less reliable (Inferred) resources are located and how much relative tonnage may be in the more certain Measured and Indicated categories.

When looking at cross-sections, it is always important to look at multiple cross-sections across the orebody. Too often in reports one may be presented with the widest and juiciest ore zone, as if that was typical for the entire orebody. It likely is not typical.

Stepping away from that one section to look at others is important. Possibly the character of the ore zones changes and hence its important to cut multiple sections along the orebody.

Bench (Level) Plans

Bench plans (or level plans) are horizontal slices across the ore body at various elevations. In these sections one is looking down on the orebody from above.

Level plans are typically less common to see in presentations, although they are very useful. The level plans may show geological detail, rock types, ore zone interpretations, ore block grades, and underground workings.

The bench plan represents what the open pit mining crews would see as they are working along a bench in the pit. The information garnered from bench plans that include the block model grades includes:

Where are the higher-grade areas found on a level? Are these higher grade areas continuous or do they consist of higher grade pockets scattered amongst lower grade blocks?
Do the ore zones swell or pinch out on a bench? A vertical cross-section may give a false sense the ore zones are uniform. The bench plan gives an indication on how complicated mining, grade control, and dilution control might be for operators.
Do the ore zones on a bench level extend out beyond the pit walls and is there potential to expand the pit to capture that ore?
On a given bench what will the strip ratio be? Are the ore zones small compared to the total area of the bench?

As recommended with cross-sections, when looking at bench plans, one should try to look at multiple elevations. The mineability of the ore zones may change as one moves vertically upwards or downwards through a deposit.

Never mind cross-sections – give me 3D

While geological sections are great, another way to present the orebody is with 3D PDF files to allow users to view the deposit in three-dimensions. Web platforms like VRIFY are great, but I have been told they sometimes can be slow to use.

3D PDF files can be created by some of the geological software packages. They can export specific data of interest; for example topography, ore zone wireframes, underground workings, and block model information. These 3D files allows anyone to rotate an image, zoom in as needed and turn layers off and on.

You can also create your own simplistic cross-sections through the pdf menus (see image).

A simple example of such a 3D PDF file can be downloaded at this link (3D DPF File Example). It only includes two pit designs and some ore blocks to keep it simple.

The nice thing about these PDF files is that one doesn’t need a standalone viewer program (e.g. Leapfrog viewer) to view them. They are also not huge in size. As far as I know 3D PDF files only work with Adobe Reader, which most everyone already has. It would be good if companies made such 3D PDF files downloadable along with their corporate PowerPoint presentations.

Conclusion

The different types of geological sections all provide useful information. Don’t focus only on cross-sections, and don’t focus only on one typical section. Create more sections at different orientations to help everyone understand better.

In 2019 I wrote an article describing the lack of geological cross-sections in many 43-101 technical reports. The link to that article is her “43-101 Reports – What Sections Are Missing?”

Geological sections are some of the first items I look for in a report. Sometimes they can be hidden away in the appendices at the back of the report. If they are available, take the time to actually study them since they can explain more than you realize.

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Grade-Tonnage Curves – Worthy of a Good Look

Date: July 16, 2023 Author: Ken Kuchling Comments: Leave a reply

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.

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

When 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.

However, 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.

Regarding 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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NPV One – Cashflow Modelling Without Excel

Date: May 21, 2023 Author: Ken Kuchling Comments: Leave a reply

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

Pros

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.
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).
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.
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.
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.

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.
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.
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.
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”.

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/

Steeper Pit Slopes Can Save Money

Date: April 14, 2023 Author: Ken Kuchling Comments: Leave a reply

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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Clays and Mining – Friends or Foes?

Date: August 24, 2022 Author: Ken Kuchling Comments: 3 Replies

Overburden is a generalized termed used to describe unconsolidated material encountered at a mine. It can consist of gravels, sands, silts, and clays and combinations of each. Usually overburden is not given much focus in many mining studies. Very often, the overburden as a unit, is not adequately characterized.

This blog will explain why proper characterization can be an important issue, particularly the clay component.

I also want to share some personal mining experiences with clays, all types of clays. There is more to them than meets the eye; a fact often not apparent to those involved only in hard rock mining.

Clays have unique geotechnical properties that can make for challenging situations and require special consideration in project design. Many simply view clay as a sticky cohesive material – no big deal. So let’s examine this a bit further. I tried to avoid geotechnical lingo where possible, since this blog isn’t being written for geotechnical engineers.

There are several types of clays, or clay-like materials that can be encountered in mining. Here are the ones that I have been lucky (or unlucky) enough to have dealt with over the years.

Normally consolidated clays
Over-consolidated clays
Sensitive (or quick) clays
Swelling clays
Saprolite clays
Kimberlite clays (muds)

What are the challenges?

Each of the clays listed above can be found in different locations, have unique properties, will behave differently, and can create specific mining challenges. Clays can also cause problems in process plant circuits, but that is a subject outside my area of expertise.

Normally consolidated clays

These are the clays most people are familiar with, i.e. a sedimentary deposit of very fine particles that have settled in a calm body of water. Normally consolidated clays are generally not a problem, other than having a high moisture content. As such, they can be very sticky in loader buckets, truck boxes, and when feeding crushers.

When wet, they can become sloppy and difficult to handle efficiently. They can creep and run when placed into waste dumps. For these reasons, engineers must be aware if a large proportion of the overburden will consist of clays so they can avoid surprises.

Over-consolidated clays

These clays have undergone greater vertical compression in their history than in their current condition. For example, perhaps they were once pre-loaded and compressed by a mile of glacial ice sheet during an ice age, which has subsequently melted.

Clays in general consist of very fine plate like particles, as shown in this sketch. In over-consolidated clays, these particles have been flattened and tightly compressed as in the right image. The result is that the clay may be dense, have a good cross bedding shear strength, but very low shear strength along the plates. This characteristic is analogous to the lubricating properties of graphite, which is facilitated by sliding along graphite plates.

My experience in working with over-consolidated clays was at the Fort McMurray oil sands mining operations. In that region the Clearwater clays formed part of the overburden sequence above the oil sands. Stripping these clays with trucks and shovels was not exceptionally challenging. They had low moisture content and were stiff. The challenge really came when needing to build on top of them, for example building a waste dump or tailings dam.

The cross-bedding shear strength was good, with peak friction angles exceeding 25 degrees. However after any creep or deformation, the peak shear strength was gone and the residual friction angle would now control stability. The residual friction angles could drop as low as 6 degrees (very weak) and, upon surcharging the clay could maintain high internal pore pressures. Due to these factors it was not uncommon to see tailings dams or waste dumps with 15:1 (H:V) downstream slopes. This compares to the 3:1 slopes one may normally see at hard rock mine sites.

Building a 15:1 dam or dump is much less volume efficient than building a 3:1 embankment. It also doesn’t take much instability to cause an embankment to creep along a foundation with only a 6-degree friction angle. Hence the over-consolidated clays presented a unique engineering challenge when working in the oil sands.

Sensitive (quick) clays

Referring to the clay particle sketch shown above, quick clays represent a card house structure (on the left image). These clays were often deposited in a quiet marine environment, where electrical charges prevented the clays from settling uniformly. Instead, the clay particles tend to stack up like a house of cards. The large void spaces are filled with water, whereby moisture contents can exceed 100% by weight.

When these clays are disturbed by vibration or movement, the house of cards structure collapses. Combined with the excess void water, these clays will flow…. and flow a lot. This video shows a slope failure in quick clays in Norway. Try to stop that failure once it has initiated.

My experience with sensitive clays was at the former BHP bauxite mining operations along the northern coast of Suriname. There were Demerara clay channels up to 20m thick over top of many of their open pits. The bucketwheel excavators used for waste stripping would trigger the quick clay slope failures, sometimes resulting in the crawler tracks being buried and unfortunately also causing some worker fatalities.

I recall walking up towards a bucketwheel digging face as the machine quietly churned away. About 70 metres from the machine, we would see cracks quietly opening all around us as the ground mass was starting to initiate its flow towards the machine. Most times the bucketwheel could just sit there and dig. Instead of the machine having to advance toward the face, the face would advance towards the machine.

To address the safety issue, eventually mine-wide grids of cone penetration tests were used to define the Demerara clay channels. Dredges were then brought in to remove these channels before allowing the bucketwheels to strip the remaining sands and normally consolidated clays.

Swelling clays

In some locations, mines may contain swelling clays. The issue with these clays is that they can absorb water rapidly, swell by 30%, and become extremely soft to operate on. If they form part of the ore zone and find their way to the tailings pond, one may find they don’t want to settle out in the pond. Water clarification and clean water recycle to the plant can become an operational issue. Mineralogy tests will indicate if one has swelling clays (smectites, montmorillonites, bentonite). The swelling clays do have a functional use however, discussed later.

Kimberlite clays (muds)

The formation of the diamond deposits in northern Canada often involved the explosive eruption of kimberlite pipes under bodies of water. The lakebed muds and expelled kimberlite by the eruption would collapse back into the crater, resulting in a mix of mud and kimberlite (yellow zones in the image). This muddy kimberlite could be soft, weak, and difficult to mine with underground methods.

Normally as one descends deeper into the kimberlite pipe, the harder primary kimberlite dominates over the muddy material. An upside is that the muddy kimberlite can be scrubbed fairly easily during processing, with the very fine clay particles being washed away.

Clays can’t be all bad?

Encountering clays at a mine site can’t be always negative? There must be some benefits that clays can provide? Well there are a few positive aspects.

Saprolite clays

At many tropical mining operations (west African gold projects for example) the upper bedrock has undergone weathering, resulting in the fresh rock being decomposed into saprolite. This clay-rich material can exceed 50 metres in thickness, can be fairly soft and diggable without blasting. This is an obvious mining cost benefit.

As well, grinding circuits can easily deal with saprolite. For example, if a 1000 tpd grind circuit is designed for the underlying deeper bedrock, it may be able to push through 1400 tpd of saprolite. This would yield a 40% increase in mill throughput for little added cost. This will boost early gold production. However as the blend of saprolite to fresh rock declines over the years as the pit deepens, the plant throughput will decrease to the original design capacity.

One concern with saprolite sometimes is its sticky nature. A truck load of saprolite ore dumped on a crusher grizzly may just sit there. Possible some prodding or water flushing may be required to get it moving. Nevertheless, this is normally an easily resolvable operating issue.

Clay core dams

One of the ways miners build water retention or tailings dams is to use mined waste rock. The issue with this is that a dam built solely with waste rock will leak like a sieve, which can lead to piping failure. One solution is to build an internal clay core in the center the dam to act as a seepage barrier. Having on-site access to good quality clean clay fill is a benefit when such dams are required. If the clay fill isn’t available at site, then more complex synthetic liners or internal seepage control measure must be instituted.

Compacted clay fill can also be used as a pond liner material for water retention ponds.

One can also purchase rolls of geosynthetic clay liners (GCL), whereby a thin layer of dried swelling clay is encapsulated between two sheets of geotextile. Once the liner is laid out and re-hydrated with water, the clays swell and will act as an impervious liner. The installation approach is somewhat simpler than for HDPE liners and such liners can be self-healing if punctured. A downside is that the transport weight of these GCL liners can be significant.

See, there are some positives with having clay at site.

Conclusion

All clays are not the same. The mining of clays can create unique challenges for mining engineers and operating personnel. Whenever I see study happily mention that their open pit mine waste consists of “free digging overburden”, I say to myself “Be careful what you wish for”.

One must ensure that the overburden is properly characterized, even though it may be viewed as an unimportant or uninteresting material. Determine whether it consists of gravels, sands, silts, tills, or clays, or combinations thereof. It can make a big difference in how it is mined, disposed of, and whether it can have any secondary uses on site. In many studies that I have reviewed, the overburden tends to be forgotten and does not get the technical respect it deserves.

Please feel free to share any thoughts on your experience in working with clays.

We had to build mine haul roads across large swamps underlain by soft clays. One option was to use geo-textiles or swamp vegetation to assist us. Another option was to place the sand fill hydraulically. You can read how we did this at the following blog post “Using Pumped Sand to Build Mine Roads“.

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Let A.I. Help Target Your Infill Drilling

Date: November 8, 2021 Author: Ken Kuchling Comments: Leave a reply

From time to time I come across interesting new tech that I like to share with colleagues. The topic of this blog relates to solving the problem of defining an optimal infill drill program.

In the past I have worked on some PEA’s whose economics were largely based on Inferred ore. The company wanted to advance to the Pre-Feasibility (PFS) stage. However, before the PFS could start they would need additional drilling to convert much of the Inferred resource into Measured and Indicated resources.

I’ve seen similar experience with projects that are advance from PFS to FS where management has a requirement that the ore mined during the payback period consist of Measured classification.

The Problem

In both cases described above, it is necessary for someone to outline an infill drill program to upgrade the resource classification while also meeting other project priorities. The goal is to design an infill drill program with minimal time and cost yet maximize resource conversion. Possibly some resource expansion drilling, metallurgical sampling, and geotechnical investigations may be required at the same time.

I’m not certain how various resource geologists go about designing an infill drill plan. However, I have seen instances where dummy holes were inserted into the block model and then the classification algorithm was re-run to determine the new block model tonnage classification. If it didn’t meet the corporate objectives, then the dummy holes may be moved or new ones added, and the process repeated.

One would not consider such a trial & error solution as optimal. It may not necessarily meet the cost and time objectives although it may meet the resource conversion goals.

The Solution

The DRX Drill Hole and Reporting algorithm developed by Objectivity.ca uses artificial intelligence to optimize the infill drilling layout. It intends to match the QP/CP constraints with corporate/project objectives.

For example, does company management require 70% of the resource in M&I classifications or do they require 90% in M&I? Each goal can be achieved with a different drill plan.

The following description of DRX is based on discussions with the Objectivity staff as well as a review of some case studies. The company is willing to share these studies if you contact them.

The DRX algorithm relies on the resource classification criteria specified by the company QP. For example, the criteria could be something like “For a block to qualify as Measured, the average distance to the nearest three drill holes must be 30 m or less of the block centroid. For a block to qualify as Indicated, the average distance from the block centroid to the nearest three holes must be 50 m or less. For a block to qualify as Inferred it will generally be within 100 m laterally and 50 m vertically of a single drill hole.”

The DRX algorithm will use these criteria to optimize drill hole placement three dimensionally to deliver the biggest bang for the buck. Whatever the corporate objective, DRX will attempt to find an optimal layout to achieve it. The idea being that fewer well targeted holes may deliver a better value than a large manually developed drill program.

The DRX outcome will prioritize the hole drilling sequence in case the drill program gets cut short due to poor weather, lack of funding, or the arrival of the PDAC news cycle.

The DRX approach can also be used to optimally site metallurgical holes and/or geotechnical holes in combination with resource drilling if there are defined criteria that must be met (by location, ore type, rock type, etc.). The algorithm will rely on rules and search criteria developed by experts in those disciplines. It does not develop the rules, it only applies them.

DRX can also help optimize step-out drilling, such that the step-out distance will not be beyond the range that negates the use of the hole in a resource estimate. It can also consider geological structure in defining drill targets.

By optimizing the number of drill holes and their orientation, the company may see savings in drill pad prep, drilling costs, field support costs, and sample assaying.

One can even request drilling multiple holes from the same drill pad to minimize drill relocation costs and safety issues in difficult terrain.

A large benefit of DRX is to be able to examine what-ifs. For example, one may desire 85% of the resource to be M&I. However, if one is willing to accept 80%, then one may be able to save multiple holes and associated costs. Perhaps with the addition of just a few extra holes one could get to 90% M&I. These are optimizations that can be evaluated with DRX.

An Example

In the one case study provided to me, a $758,000 manually developed drill program would convert 96.6% of the Inferred resource to Indicated. DMX suggested that they could achieve 96.7% for $465,000. Alternatively they could achieve 94% conversion for $210,000. These are large reductions in drilling cost for small reductions in conversion rate. This may allow the drill-metres saved to be used for other purposes.

For that same project, a subsequent study was done to convert Indicated to Measured in a starter pit area. DRX concluded that a 5000-metre program could convert 62% of Indicated into Measured. A 12,000-metre program would convert 86%, A 16,000-metre program would achieve 92%.

So now company management can make an informed decision on either how much money they wish to spend or how much Measure Resource they want to have.

Conclusion

Although I have not yet worked with DRX, I can see the value in it. I look forward to one day applying it on a project I’m involved with to develop a better understanding of what goes in and what comes out. DRX hopes to become to resource drilling what Whittle has become to pit design – an industry standard.

The use of the DRX algorithm may help mitigate situations where, moving from a PEA to PFS, one finds that the infill program did not deliver as hoped on the resource conversion. Unfortunately, this leaves the PFS with less mineable ore than anticipated and sub-optimal economics.

New tech is continually being developed in the mining industry. Hopefully this is one we continue to see forward advancement. It makes sense to me and DRX could be another tool in the geologist toolbox. Check out their website at objectivity.ca

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Mining Financial Modeling – Make it Better!

Date: October 20, 2021 Author: Ken Kuchling Comments: Leave a reply

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!“.

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.

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 have not provided sufficient geological cross-sections or 3D images, and investors wish to create their own.

Highlighted Interval Data in CSV Format

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

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

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.

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.

The mining industry is implementing more and more technology in the mining cycle.

Lots of different innovations are continually being adopted by the mining industry, contrary to what some may say.

The question is does all this innovation improve the overall performance of a mining operation, and if so, by how much? It can cost a lot of money to implement the new technology, is there a payoff?

One cannot answer those questions if one doesn’t undertake proper mine reconciliation. A concern might be that mining is innovating faster than the ability to assess the results of that innovation. To monitor it, you need to measure it.

What is Mine Reconciliation

Mine reconciliation tends to aggregate over longer time periods (monthly, quarterly or annually) due to short term impacts of material handling in stockpiles and plant circuits and the labour time needed to collect the input data.

Mine reconciliation ultimately attempts to assess how well the delivery of the final metal product relates to the initial resource model (i.e. what the project decision was originally based on)? It is also tool to evaluate the impact of any innovation implementation on the operation.