Project Case Studies

These case studies show how I work across three connected mining challenges: aligning mine delivery with downstream development, making resource uncertainty visible, and connecting technical evidence with investment value. Project details and figures are generalised where confidentiality applies.


Mining financial model used for techno-financial analysis

Downstreaming Integration

Coordinating mine geology, ore supply, infrastructure, construction, and downstream requirements within a large integrated nickel-cobalt development.

Outcome: Contributed to programme completion approximately four months ahead of target while maintaining dilution at around 2% and strengthening operational safety performance.

Focus: Mine-to-Project interfaces · Ore Specification Control · Multifunction Coordination · Project Safety

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Conceptual geological block model illustrating resource confidence and drill spacing

Resource Risk Prediction

Investigating recurring differences between a resource model and production by combining reconciliation, drillhole-spacing analysis, density review, and field verification.

Outcome: Established a clearer and repeatable basis for identifying geological uncertainty, prioritising validation work, and improving confidence in resource and mine-planning decisions.

Focus: Resource Modelling · Ore Reconciliation · 3D Spatial Analysis · Geological QA/QC

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Techno-Finance Integration

Connecting resource confidence, mine-development assumptions, project economics, regulation, and downside risk within a traceable mineral-investment evaluation.

Outcome: Applied the approach across more than 15 mineral-opportunity reviews and supported two completed acquisitions while also identifying opportunities requiring further evidence or a no-go decision.

Focus: Technical Due Diligence · Mine Economics · Scenario Testing · Investment Evaluation

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Confidentiality: These case studies use public, personal, or appropriately anonymised information. Companies, sites, counterparties, commercial assumptions, and sensitive datasets are generalised or omitted.

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