AI-Assisted Drill Targeting: How K-MINE Uses Existing Exploration Data

Exploration teams are often expected to update geological models, revise drilling priorities, and support resource estimation while drilling is still in progress.

Decisions must be made quickly. But they must also be reliable, consistent, and supported by the available data.

Modern geological information systems, such as K-MINE, help teams move away from static planning and toward data-driven decision-making.

They allow geologists to visualize exploration targets in 3D, integrate geological, geophysical, geochemical, and drilling data, and identify areas where additional information is still required.

These systems can also help teams optimize drillhole orientation, improve drill spacing, update geological interpretations more efficiently, and increase confidence in exploration decisions.

The latest version of K-MINE also includes an AI-assisted tool for generating block models.

Using a CSV file containing composite data from existing drillholes or mine workings, the system can create a predictive model of the deposit within minutes.

This gives geologists a rapid initial interpretation that can be reviewed, refined, and incorporated into the broader exploration workflow.

The purpose is not to replace geological expertise. It is to give geologists a faster way to analyze available data, test interpretations, and make more informed decisions about where to drill next.

Let’s consider a practical example.

Imagine that one part of a deposit has already been explored in detail and the main orebody has been identified.

The next objective may be to investigate the deposit’s flanks or to increase drilling density in areas where additional confidence is required.

To predict lithology and grade distribution in these areas, historical exploration data can be consolidated into a composite table.

From this table, geologists can select the relevant lithological categories, geological domains, and commodity grades to be interpolated into the block model.

The user then defines the model boundaries, block dimensions, and other required parameters.

These parameters may include the number of modelling iterations, trend parameters, azimuth, dip, and other characteristics that describe the geometry and orientation of the mineralized bodies.

The resulting predictive model can help identify more precise drilling targets.

For example, it may highlight areas with a predicted increase in grade or zones where thick, continuous mineralized bodies may extend beyond the limits of existing drilling.

It is important to clarify that an AI-generated predictive block model may not satisfy the requirements for public mineral-resource reporting.

One reason may be the limited ability to independently validate the calculation and interpolation methodology as required by the applicable reporting codes.

However, because an AI-assisted model can be generated quickly and with relatively little effort, it can still be a valuable tool within an iterative target-generation workflow.

The model can later be combined with geomechanical information.

For example, acoustic logging data can be interpolated into the three-dimensional model to help map rock strength, pore-pressure conditions, and fracture gradients.

This allows geological and geomechanical constraints to be converted into specific drilling parameters and risk maps.

The three-dimensional geological model can then be compared with potential drilling hazards such as weak or washed-out zones, overpressured intervals, fractured ground, and fault intersections.

The next major benefit is drillhole trajectory optimization.

This includes optimizing the location, orientation, spacing, and length of the proposed drillholes.

The drillhole planning tool within K-MINE helps optimize drillhole geometry to obtain the maximum amount of useful, representative core while minimizing cost and operational risk.

It can also verify collision-avoidance requirements and generate alternative trajectories that satisfy the project’s safety constraints and engineering constraints.

Before drillholes are designed within a geological information system, the target intervals must first be defined.

Whenever possible, a drillhole should intersect the mineralized body at an angle close to perpendicular.

This improves the reliability of true-thickness calculations and produces more representative geological information.

A block model contains predicted commodity grades.

Therefore, drillholes may be oriented to intersect higher-grade zones or to collect the additional information needed to support the potential conversion of resources from inferred to indicated or measured categories.

The structural model must also be considered.

Fault wireframes, fractured zones, geological contacts, and areas of weak ground can all influence the proposed trajectory.

Drillholes may be designed to avoid these features.

Alternatively, the associated technical risks, such as lost circulation, unstable ground, or stuck drilling equipment, can be incorporated directly into the drilling plan.

Based on the three-dimensional geometry, each drillhole trajectory is defined using several key elements.

The first is the collar.

The collar represents the X, Y, and Z coordinates of the drillhole’s starting point, either at the surface or within an underground excavation.

For surface drilling, the elevation is typically obtained from a digital terrain model.

For underground exploration, it may be taken from the geometry of the relevant development or excavation.

The second element is the target, or the planned end point of the drillhole.

The target may be a wireframe surface, a point within a geological domain, or the boundary of a solid representing the mineralized body.

An overdrill distance may also be specified.

For example, a drillhole may be required to continue at least five to ten metres beyond the interpreted orebody and into waste rock.

This helps confirm the boundary of mineralization rather than ending the drillhole inside the mineralized interval.

Different drillhole-design modes allow the user to define the required geometric parameters, including azimuth, dip, and length.

New drillholes or sections can also be generated relative to an existing collar.

The new collar may be offset by a specified distance and azimuth.

To reduce the cost of preparing drill pads and relocating drilling equipment, cluster drilling or fan drilling is frequently used.

In this approach, several drillholes with different azimuths and inclinations are designed from a single collar or drill station.

It is also important to recognize that real drillholes are rarely perfectly straight.

When planning deeper holes, particularly those exceeding approximately 200 metres, the expected natural deviation can be incorporated into the design.

This can be based on historical information from nearby drillholes.

Mandatory downhole-survey stations may also be defined, for example, at thirty-metre intervals.

This allows the actual trajectory to be measured and the drilling plan to be adjusted as the hole advances.

The final stage of the design process is to verify that every proposed trajectory satisfies the project’s engineering constraints and operational constraints.

This may include modelling drilling hydraulics, torque, drag, and resistance based on the geomechanical properties of the rocks being drilled.

The system can also support forecasts of drilling duration and drilling cost.

A validated trajectory can be compared with information from nearby drillholes to establish baseline time-versus-depth curves.

This can help teams estimate expected non-productive time, forecast drilling performance, and prepare a more reliable drilling budget.

Finally, all of this information, including targets, trajectories, design parameters, technical risks, expected drilling time, and estimated costs, can be compiled into a comprehensive project report.

Once the drill plan has been finalized, it can be exported from the geological information system as a complete set of tables, schedules, and graphical materials.

The first output is the collar table.

This includes the drillhole ID, the planned X, Y, and Z coordinates, the proposed azimuth, and the planned dip.

The second output is the drilling schedule.

It defines the planned drilling sequence, the target depth for each drillhole, and the estimated drilling time measured in days or shifts.

The third output is a set of geological section views.

These two-dimensional sections show each proposed drillhole in relation to the three-dimensional orebody, geological boundaries, faults, and other important structural features.

Finally, the system can generate a technical drilling specification for the drilling contractor.

This may include the required drilling diameter, such as HQ or NQ, core-sampling intervals, downhole survey requirements, and any planned hydrogeological observations.

Together, these deliverables ensure that the geological interpretation is translated into clear, practical, and actionable instructions for the drilling team.

Now, let’s turn to another key question in exploration planning: how closely should our drillholes be spaced?

At first glance, it may seem that the closer the drillholes are, the better the geological model will become.

In practice, however, the answer is not that simple.

If drill spacing is too wide, we may miss important geological features, overlook local variations, or overestimate the continuity of the mineralized body.

On the other hand, if drillholes are spaced too closely, the drilling program may become unnecessarily expensive without providing a meaningful increase in geological confidence.

The objective is therefore not to drill as many holes as possible.

The objective is to collect enough geological information to support reliable decisions while avoiding unnecessary drilling.

There is no single drill spacing that works for every project.

The appropriate spacing depends on several factors, including the deposit type, geological complexity, orebody geometry, grade variability, the stage of exploration, project objectives, and, of course, the available budget.

Ultimately, the right drill spacing is a balance between geological confidence, project risk, and the cost of obtaining additional information.