Rapid Resource Evaluation for Copper-Gold Development

MEC Mining Case Study: Turning Complex Legacy Data into Clear Investment Insights

Project Stream: Conceptual / Preliminary Works

Project Stage: Resource Evaluation

Type of Mine: Open Pit

Region: International — South America

Commodity: Copper / Gold

Our client required a rapid evaluation of multiple Resource models to assess their potential to support a defined production profile.

The study mandate was to translate Spanish technical reports, import the available block models and assess the Resource potential against a target of 50 Mt @ 1% CuEq.

Several factors made the assessment particularly challenging:

• Large and complex legacy block model formats.
• Technical reports provided in Spanish.
• Different historical grade estimation methodologies.
• Topography and block model data requiring careful interpretation.
• Proportional rotated sub-blocked models requiring detailed attribute management.
• Missing or incomplete information around model origin, extents and block sizes.
• The need to establish appropriate cost models for a LATAM contract mining operation.

The ability to interpret and work through these data challenges quickly was critical to supporting robust investment decisions.

1. AI-assisted technical report conversion – MEC used AI-assisted processes to convert a large volume of Spanish technical reports into English, allowing the team to rapidly understand the available technical information and identify the data required for evaluation.

2. Specialist block model interpretation – The size and format of the block models meant they could not be managed through standard Excel-based workflows. MEC loaded the models into specialist mining software and carefully interpreted key model properties, including origin, extents, block size and relevant attributes. This was particularly important for proportional rotated sub-blocked models, where understanding how the model had been constructed was critical to maintaining data integrity.

3. Development of relevant cost models – MEC developed cost models using its in-house database, with escalation applied where required to bring the cost basis up to date. The team also undertook research into neighbouring operations and reviewed relevant NI 43-101 technical reports for comparable assets. This provided a basis for developing cost assumptions relevant to the local operating environment and enabled like-for-like comparisons with equivalent operations.

4. Rapid pit optimisation – Once the Resource models and supporting cost information were established, the models were imported into Deswik Pseudoflow. Pit optimisation allowed MEC to rapidly assess the potential of each Resource model and provide the client with information to support investment decisions. MEC’s strategic planning team was able to tailor the optimisation approach to the client’s preferences and required level of detail.

The rapid evaluation process has supported the client in assessing multiple potential assets and progressing opportunities.

Key outcomes included:

• Multiple Resource models evaluated within a consistent assessment framework.
• Complex legacy block models successfully interpreted and imported into specialist mining tools.
• Relevant cost models established using MEC’s in-house data and regional benchmarking.
• Pit optimisation used to rapidly assess Resource potential.
• Several assets subsequently purchased by the client.
• Purchased assets progressing into detailed study phases, where MEC will be heavily engaged.
• MEC continuing to support further rapid Resource evaluations for the client.

MEC’s ongoing involvement includes:

• Preparation of study frameworks.
• Development of the project management team.
• Further rapid Resource evaluations.
• Consideration of additional LATAM Resource areas.

As the purchased assets progress into detailed studies, MEC will continue to support the client through the next stages of project development.

MEC Mining helps clients turn complex Resource data into clear, practical insights that support better project decisions.