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.

In mining, reserve estimation is not only about creating a visually impressive block model. A model may look perfect inside the software, but mining reality is far more complex. -. If geotechnical conditions are ignored, slopes can fail. -. If hydrogeology is underestimated, water inflow can stop operations. -. If dilution and mining recovery are not considered properly, the actual ore delivered to the plant may never match the estimated reserve. This is why resource and reserve estimation should never stand alone. Geology, geotechnical engineering, hydrogeology, ore control, mining selectivity, and operational practicality must work together from the beginning. Because at the end of the day: The best reserve is not the biggest number on paper.It is the reserve that can actually be mined safely, consistently, and profitably. #Mining #Nickel #Geology #ResourceEstimation #ReserveEstimation #BlockModel #Geotechnical #Hydrogeology #OreControl #MinePlanning #MiningEngineering #OpenPitMining

DRILL SPACING DETERMINES RESERVE ESTIMATION In mineral exploration and mining, drill spacing plays a critical role in determining the accuracy and reliability of reserve estimation. The distance between drill holes directly affects data density, geological confidence, and orebody interpretation quality. The section shown above represents an area with adequate drill density, where geological interpretation can be performed with higher confidence. With sufficient data spacing, ore boundaries, thickness variations, and mineralization continuity become clearer and more representative of the actual deposit conditions. Proper drill spacing provides several major advantages: • Higher geological confidence • Better ore continuity interpretation • More accurate tonnage and grade estimation • Reduced geological uncertainty • Stronger mine planning and production forecasting A dense and well-structured drilling pattern also improves the quality of geological domain modeling, allowing reserve calculations to become more reliable and economically realistic. On the other hand, poor drill spacing can lead to: Overestimation or underestimation of reserves -. Grade dilution -. Incorrect ore boundaries -. Higher operational risk -. Misleading economic valuation This is why reserve estimation must also consider: -. Safety Factor -. Loose Material Factor -. Mining Recovery -. Dilution Control These modifying factors are essential to minimize operational losses, geological uncertainty, and discrepancies between model tonnage and actual production. Ultimately, accurate reserve estimation is not only a technical achievement — it is a foundation for sustainable mining economics, reliable production planning, and stronger investor confidence. Closing Statement “Good data density creates strong geological confidence, Strong geological confidence creates reliable reserves, Reliable reserves create sustainable mining economics.” #Mining #Geology #CoalMining #OreMining #ReserveEstimation #ResourceModeling #MinePlanning #Geostatistics #NickelMining #CoalExploration #MiningEngineer #GeologicalModeling #JORC #KCMI #MiningIndustry

A well-designed block model is one of the most important foundations in nickel resource estimation. In lateritic nickel deposits, mineralization can change rapidly both laterally and vertically. Because of this, block size and model detail play a critical role in how accurately the resource can be identified and quantified. If the block model is too coarse, important geological variability and narrow high-grade zones may be smoothed or even missed entirely. On the other hand, a properly designed block model with appropriate block dimensions allows us to better capture: 1. grade continuity, 2. geological boundaries, 3. mineralization trends, 4. and the true geometry of the orebody. The slicing section shown here represents how actual field data is translated into a detailed block model using real nickel data. Each block carries geological and geochemical information that contributes to the final resource estimation. At the end of the day, resource modeling is not simply about filling blocks inside software — it is about understanding how detailed the model should be so the resources can be identified as accurately and maximally as possible. “A good block model does not only visualize the deposit — it reveals its true potential.” #NickelMining #Geology #OreReserve #MiningEngineering #ResourceModeling #BlockModel #TechnicalInsight

A large nickel resource does not always become a large mineable reserve. In mining, a mineral resource only represents the geological potential in the ground. To become an ore reserve, the material must pass through multiple technical, economic, geotechnical, and operational considerations. Factors such as: 1. pit slope constraints, 2. groundwater conditions, 3. mining recovery, 4. dilution, 5. haul road access, 6. operational practicality, 7. and economic cut-off grade can significantly reduce the amount of recoverable ore. This is why two deposits with similar resources may produce very different reserve outcomes. In reality, reserve estimation is not simply about maximizing tonnage — it is about identifying what can realistically, safely, and profitably be mined. All field data may be the same ingredients, but the final result depends on how the “chef” interprets and processes the data with the right understanding, experience, and mining judgment. At the end of the day: “The best reserve is not the biggest one — but the most realistic and recoverable one.” #NickelMining #OreReserve #MiningEngineering #GeotechnicalEngineering #MinePlanning #TechnicalInsight

In nickel ore reserve estimation, two geologists can work with the exact same drilling dataset — yet produce completely different reserve results. Why? Because reserve estimation is not only about software, interpolation methods, or calculations. It is highly dependent on geological interpretation, experience, assumptions, and geological sense. The same drillhole data can lead to different: 1. geological domains, 2. ore boundaries, 3. continuity models, 4. and ultimately, different reserve tonnages. In reality, geological data is like ingredients in a kitchen. The drilling database may be the same, but the final “flavor” depends on how the chef prepares and interprets the ingredients. A good geologist is not only someone who collects data, but someone who understands how to “cook” the data into a realistic, mineable, and reliable reserve model. At the end of the day: “Reserve is not only built from data, but from interpretation.” #NickelMining #Geology #OreReserve #MiningEngineering #GeotechnicalEngineering #TechnicalInsight

Nickel ore reserve estimation is a complex process that transforms original drilling data into a mineable and economically feasible reserve through several stages, including data validation, geological interpretation, resource modeling, and mining modification factors. The workflow begins with raw drilling information such as lithology, assay results, geological structures, geotechnical data, and hydrogeological conditions. These datasets are then validated through QA/QC procedures before being interpreted into geological domains, wireframes, and block models using estimation methods such as IDW or Kriging. Afterward, mining, geotechnical, hydrogeological, and economic constraints are applied to convert mineral resources into ore reserves that are technically and economically mineable. However, reserve estimation is not purely a mathematical or software-driven process. The final reserve result strongly depends on geological interpretation and the geological sense of the person building the model. Even when using the same drilling dataset, different geologists or engineers may produce different reserve outcomes due to differences in experience, understanding of lateritic nickel deposits, interpretation philosophy, and assumptions regarding ore continuity and geological boundaries. In reality, reserve models are not only built from data, but also from the quality of geological understanding behind the interpretation itself. from now on, I will share my Knowledge for just sharing :D Cherss

Jour 28/30 du Challenge 📘 Fiche Exercice N°28 : Visualisation et cartographie avancée sous TMAP – De la représentation spatiale à la production cartographique géostatistique ✍️ Ing. NGUEYAP AMBANI AUGUSTIN SERGE (Ingénieur Géologue – Spécialiste) 💡 Cette fiche est un outil concret pour : - Découvrir les fonctionnalités avancées de TMAP pour la visualisation spatiale - Produire des cartes géostatistiques professionnelles adaptées à l’exploration et à l’environnement - Relier les résultats aux applications pratiques en gestion des ressources et communication scientifique - Exploiter des scripts R/Python pour automatiser la production cartographique 👉 Pourquoi c’est utile ? Parce qu’elle vous permet de passer de la simple analyse à la production cartographique avancée, directement exploitable dans vos projets. 🙏 Merci à celles et ceux qui aiment 👍, commentent 💬 et partagent 🔄 ce contenu. Votre soutien nous permet de continuer à proposer des formations de qualité.

Jour 27/30 du Challenge 📘 Fiche Exercice N°27 : Quantification de l’incertitude spatiale – Analyse des écarts-types d’estimation en krigeage ✍️ Ing. NGUEYAP AMBANI AUGUSTIN SERGE (Ingénieur Géologue – Spécialiste) 💡 Cette fiche est un outil clé pour : - Comprendre la notion d’incertitude spatiale en géostatistique - Analyser les écarts-types d’estimation issus du krigeage - Relier les résultats à la fiabilité des cartes et modèles - Exploiter des scripts pratiques en R/Python pour calculer et visualiser l’incertitude 👉 Pourquoi c’est utile ? Parce qu’elle permet de passer de la simple estimation à une évaluation robuste de la précision, indispensable en exploration minière, hydrogéologie et environnement. 🙏 Merci à celles et ceux qui aiment 👍, commentent 💬 et partagent 🔄 ce contenu. Votre soutien nous permet de continuer à proposer des formations de qualité.

Jour 26/30 du Challenge 📘 Fiche Exercice N°26 : Estimation des propriétés mécaniques du sol – Étude géostatistique de la variabilité de la densité et cohésion ✍️ Ing. NGUEYAP AMBANI AUGUSTIN SERGE (Ingénieur Géologue – Spécialiste) 💡 Cette fiche est un outil essentiel pour : - Comprendre la variabilité spatiale des propriétés mécaniques du sol - Appliquer les méthodes géostatistiques pour estimer densité et cohésion - Relier les résultats à des applications géotechniques (fondations, stabilité des talus, infrastructures) - Exploiter des scripts pratiques en R/Python pour reproduire les calculs et visualisations 👉 Pourquoi c’est utile ? Parce qu’elle fournit des outils opérationnels pour l’ingénierie civile, minière et environnementale, en transformant des données de terrain en modèles fiables pour la prise de décision. 🙏 Merci à toutes celles et ceux qui aiment 👍, commentent 💬 et partagent 🔄 ce contenu. Votre soutien nous permet de continuer à proposer des formations de qualité.