Consequent to a previous article, we have been asked to discuss briefly the financial implications of poor drilling. When we talk about drill and blast, the conversation usually leans towards explosives and their application, while the drilling component gets far less attention. Many operations still track drilling the way they always have: cost per metre drilled. It’s neat. It’s familiar, yet it’s dangerously incomplete. Drilling doesn’t exist in isolation. Every metre drilled sets the conditions for blast performance, downstream productivity, and ultimately profitability. When drilling is optimised solely to reduce the drill contractor’s invoice, costs are often pushed—quietly but significantly—into every stage that follows. That’s why Total Drilling Cost matters far more than drill cost alone. What Is TDC (Really)? Total drilling cost is not just: Drill contractor rates Consumables Maintenance Fuel and labour It also includes the consequences of drilling quality: Hole deviation and collar accuracy Burden and spacing variability Sub-drill consistency Redrilling Misfires and blast inefficiencies Poor fragmentation impacting loading, hauling,crushing Increased dilution, losses, or downstream bottlenecks A cheap metre drilled badly is one of the most expensive metres you’ll ever mine. The False Economy of “Cheap Drilling” We've seen this pattern repeatedly: Drill rates are pushed down Penetration rate becomes the primary KPI QA/QC is reduced Operators are incentivised on metres, not quality On paper, drilling cost improves. In reality: Blast performance becomes inconsistent Powder factor increases to compensate Dig rates slow Crusher throughput drops Maintenance costs rise Reconciliation gaps widen The operation spends far more trying to fix the blast than it saved on drilling. Shifting the Conversation: From Cost to Value High-performing operations look at drilling differently: Cost per effective hole, not cost per metre Accuracy compliance, not production metres Drill-to-blast alignment, not isolated KPIs System performance, not departmental optimisation They understand that drilling is a precision activity, not a volume race. What to Measure Instead If you want to understand your true drilling cost, ask: How much rework are we carrying due to poor hole placement? What is the variance between planned and achieved burden? How often do blast designs require modification to compensate for drilling? What is the downstream cost of poor fragmentation? Are drill KPIs aligned with blast outcomes—or working against them? These answers rarely sit in one department. Final Thought Drilling is the first act in value creation. Get it right, and everything downstream becomes easier. Get it wrong, and no amount of blasting “optimisation” will save you. If you’re still judging drilling purely on cost per metre, you’re likely paying far more than you think.

Reviewing mining projects from discovery through to operations, there’s one issue that comes up time and time again: resource classification. Teams spend countless hours on estimation methods and geological interpretation, only to fall short when it comes to correctly classifying the resource. Why does it happen? Optimism bias (often driven by board or funding expectations) Time pressure at the end of a study Inexperience with classification standards Or simply underestimating how critical classification really is As an external reviewer, here are a few practical principles that consistently separate robust resource models from risky ones: 🔹 Let drill spacing lead the classification Be strict. If there are spacing gaps, classify them accordingly. No “spotted dogs”. 🔹 Avoid circles around drill holes Resources aren’t radial. Use orebody knowledge and geological controls to define realistic shapes. 🔹 Data integrity matters QAQC results, core recovery, and missing samples must be reflected in the classification. Confidence in tonnes starts with confidence in data. 🔹 Respect the limits of your estimation parameters Search radii, variography, and interpolation choices should cap — not inflate — classification confidence. 🔹 Classify waste properly If the drill spacing supports a measured ore classification, the same logic applies to internal waste. Mining companies want to know where measured waste is — ironically, we mine far more waste than ore. Resource classification isn’t a tick-box exercise. It’s a risk statement, and decision-makers rely on it more than we often acknowledge. Get it right early, and everything downstream — mine design, scheduling, economics — becomes stronger. Happy to discuss or hear how others approach this challenge in their projects.

[PT] A precisão é a base da segurança. Na mecânica das rochas, saber se um maciço agirá como um meio contínuo ou um conjunto de blocos discretos depende da geometria das descontinuidades. Nossa metodologia usa o espaçamento — um dado real de campo — para calcular volumes de blocos tetraédricos e prismáticos com precisão matemática. Menos incerteza, mais estabilidade. [EN] Precision is the foundation of safety. In rock mechanics, knowing whether a rock mass will act as a continuum or a discrete set of blocks depends on discontinuity geometry. Our methodology uses spacing—real field data—to calculate tetrahedral and prismatic block volumes with mathematical precision. Less uncertainty, more stability.

A short post to remind mine planning engineers that grade is just a proxy for value. You need to prove to yourself that it is a good proxy to use. Years ago, I used to present a value creation and #CutOffGrade seminar. I would start the seminar with the following three questions as a thinking exercise: Q1: Consider two blocks of ore (everything else being equal), which block has higher value: (a) 100 tonnes @ 2.0% Cu. (b) 100 tonnes @ 2.5% Cu. The obvious answer would be (b), as it contains 2.5 tonnes of copper, versus (a) with 2.0 tonnes of contained copper Q2: Consider the same two blocks of ore, but now with recovery information (and again everything else being equal), which block has higher value:: (a) 100 tonnes @ 2.0% Cu - with 80% recovery. (b) 100 tonnes @ 2.5% Cu – with 85% recovery. Again, the obvious answer would be (b), as it has 2.1 tonnes of recovered copper versus (a) with 1.6 tonnes of recovered copper. Q3: Now consider the same two blocks of ore, with recovery information and throughput information (and again everything else being equal), which block has higher value: (a) 100 tonnes @ 2.0% Cu - with 80% recovery, and SAG mill throughput of 100 tph (so soft ore, and/ore well fragmented). (b) 100 tonnes @ 2.5% Cu – with 85% recovery, and SAG mill throughput of 70 tph (so hard ore, and/or poorly fragmented). Now, the answer flips to (a), as (a) has a recovered copper per hour ‘value’ of 1.6 tonnes Cu per hour, and (b) has a ‘value’ of 1.49 tonnes Cu per hour. So, in this third situation, the lower grade, lower recovery ore provides 7% greater ‘value’ in time – and it is what we produce in time that ultimately determines the value (NPV is after-all a measure of ‘$s in time’). This leads to the ‘cash flow grade’ concept, quite well described by Dr Brett King in a paper he write back in 1999. (I loved this paper when I first read it, as I was ~90% there myself - and felt frustration that Brett had written it first!) The concept also leads to the necessary identification of system bottlenecks that need to be exploited (#TheoryOfConstraints) to increase the ‘value flow’. These concepts are effectively what Gerald Whittle has effectively based his business on: the cash flow grade concept – combined with TOC – and using software to solve complex systems that can result. So – grade is just a proxy for value. Sometimes it can be a good proxy. Other times it can be a poor proxy. I have only ever once seen a feasibility study use a cash flow grade for the mine schedule. And I will be first to acknowledge it is not an easy thing to use. It can’t be directly measured like a grade. It must be calculated from multiple factors – of which grade is just one factor. And we usually often don’t have reliable models for those other relevant factors. My advice: do as much economic value modelling of your ore value as you can, and find some approach to cut-off value that is practical and makes sense, AND captures some of that time value. References King, B. 1999, “Cash Flow Grades - Scheduling Rocks with Different Throughput Characteristics”, Proc. Conf. Optimising with Whittle, Perth 1999.

Key Takeaways The U.S. is 100% import reliant for several critical minerals used in AI-related infrastructure. Core data center components—from circuitry to magnets—depend heavily on foreign-sourced materials. The artificial intelligence boom is driving an unprecedented buildout of data centers across the United States. Behind every AI model and cloud server sits a complex web of minerals that make modern computing possible. From semiconductors to cooling systems, these materials form the backbone of digital infrastructure. This visualization breaks down the critical minerals used in AI data centers—and how reliant the U.S. is on imports for each. The data for this visualization comes from the U.S. Geological Survey (USGS). Semiconductors: America’s Biggest Vulnerability Semiconductors are the “brains” of AI data centers—and they are highly import dependent. The U.S. is 100% reliant on imports for arsenic, fluorspar, gallium, germanium, indium, and tantalum used in chip production. It also imports 85% of its platinum and 36% of its palladium needs, both critical for chip manufacturing. While silicon, the base material for chips, has less than 50% import reliance, many of the trace elements that enable advanced computing are entirely foreign-controlled. Data center component Critical mineral U.S. import reliance (%) Server boards and circuitry Silver 64% Gold 0% Copper 45% Tin 73% Tantalum 100% Palladium 36% Platinum 85% Heat sinks Aluminum 47% Copper 45% Semiconductors and microchips Arsenic 100% Fluorspar 100% Gallium 100% Germanium 100% Indium 100% Palladium 36% Platinum 85% Silicon 75% Boron 0% Rare earth elements 80% Circuitry and Server Components Beyond chips, server boards and circuitry require a range of conductive and precious metals. The U.S. imports 64% of its silver and 73% of its tin, both vital for soldering and electrical conductivity. Copper—essential for wiring and connectivity—has a 45% import reliance. Tantalum, used in capacitors, is 100% imported. Gold stands out as a rare exception, with 0% net import reliance, offering a small pocket of domestic security in an otherwise globalized supply chain. Cooling Systems and Data Storage AI servers generate massive heat loads, making cooling systems crucial. Heat sinks rely on aluminum (47% import reliance) and copper (45%). Meanwhile, data storage components such as magnets and drives depend on rare earth elements, with 80% import reliance. Barite—used in storage-related applications—has also more than 75% reliance. China’s Commanding Share Currently, China dominates the production of most of the critical minerals used in data centers. This near-monopoly has become a major concern for other nations, with the U.S. government currently pushing for increased domestic production of these materials. In addition to being the leading producer, China also controls much of the refining capacity for many of these minerals. For example, around 90% of rare earths are refined in China. In the race to dominate AI, access to critical minerals may prove just as important as technological leadership.

To document is to show how important is to do bench master for an optimal drill and blast activities in mining operations if it is hard rock.

[PT] Do ponto de vista matemático, podemos interpretar o processo de cominuição como uma sucessiva redução do tamanho médio das partículas através da adição de energia ao sistema. E estas partículas podem ser descritas através de uma distribuição granulómetrica, partindo da rocha intacta até um eventual produto de moagem. Para mais detalhes, confira o post completo aqui na plataforma ZVENIA. [EN] From a mathematical point of view, we can interpret the comminution process as a successive reduction in the average particle size through the addition of energy to the system. And these particles can be described by a granulometric distribution, starting from the intact rock to a possible grinding product. For more details, check out the full post here on the ZVENIA platform. https://zvenia.com/z-posts/estado-de-fraturamento-e-fragmentacao-de-macicos-rochosos-tese-de-doutorado-2020/

[PT]Há quem diga até hoje que a onda de choque produzida pelo demonte de rochas com explosivos não induz danos microestruturais na matriz da rocha, reduzindo portanto, sua resistência mecânica e consequentemente seu pré-requisito energético para cominuição. Provar este fato é mais simples do que parece, e basta medirmos a velocidade de pulso utrassônico das amostras de rochas antes e depois da detonação. Para mais detalhes, confira o post completo aqui na plataforma ZVENIA. [EN] Some people still argue that the shock wave produced by rock blasting with explosives does not induce microstructural damage to the rock matrix, thus reducing its mechanical strength and, consequently, the energy required for comminution. Proving this fact is simpler than it seems; all we need to do is measure the ultrasonic pulse velocity of rock samples before and after detonation. For more details, check out the full post here on the ZVENIA platform. https://zvenia.com/z-posts/estado-de-fraturamento-e-fragmentacao-de-macicos-rochosos-tese-de-doutorado-2020/