Increasing orebody complexity, restrictions in ground access, and longer lead times for disturbance approvals, have generated the need for the Resource Development Team within Rio Tinto Iron Ore (RTIO), to explore optimised drill design techniques that deliver resource conversion value while maximising program outcomes. In response to this challenge, the Team initiated a case study to produce an AI assisted/designed drill program using a non-machine learning AI.
The case study involved the assessment and implementation of an AI generated drill program at one of RTIO’s Pilbara iron ore deposits, located in the Hamersley Province of Western Australia. The Mineral Resource is currently classified as 100 per cent Inferred. Access constraints inherent to topography, and the presence of exclusions zones have prevented adequate drilling coverage, resulting in lower resource confidence and a poor definition of the orebody extents and geometry. The drill campaign, planned in Q1 2023, needs to consider access constraints, complex topology and the limitations related to additional ground clearance, while addressing the requirement of improving orebody knowledge.
The drill plan had to address two objectives. The first and main aim of drilling was to upgrade the Resource Classification to allow for possible conversion to Ore Reserve. The input for this objective was a volumetric representation of the areas currently not meeting pre-defined quantitative Resource Classification upgrade criteria. The second objective was to better define the orebody extents and geometry. For the second objective, the input to the drilling optimiser was a volumetric representation (blocks) of the areas of high uncertainty in the mineralisation/waste boundary, which were defined using an indicator conditional simulation technique.
The drilling optimiser output was a range of compliant drill plans. A set of KPIs based on economic and orebody knowledge criteria were used to assess the plans and to select the most optimal drill design to be drilled in 2023. The geology and management teams were able to make better informed, objective decisions using the trade-off between cost and orebody knowledge gained. As part of the assessment, the AI generated plans were compared to our current planning practises. The AI assisted plans, when gauged against the project KPIs, showed clear and measurable improvements in both meeting project objectives and in managing operational risk.