Webinar: Where Should We Drill Next? Digital Workflows for Exploration Drilling Projects

Exploration drilling is one of the most important and expensive parts of mineral exploration. Every drillhole requires a significant investment of time and money, and every drilling campaign involves uncertainty.

Decisions about where to drill, when to drill, and how to drill directly influence the geological model, confidence in the resource, and ultimately the economic potential of the project.

Today, exploration teams have access to more information than ever before, from drillhole databases, geophysical and geochemical surveys, and remote sensing data to increasingly detailed 3D geological models.

But more data does not automatically lead to better decisions.

The real challenge is bringing all of this information together and turning it into a clear, reliable, and defensible drilling strategy.

Modern geological information systems, 3D modelling, cloud-based collaboration, real-time data collection, and artificial intelligence are helping exploration teams evaluate targets more effectively, reduce uncertainty, and make better use of exploration budgets.

The key question remains: where should we drill next?

K-MINE develops software solutions designed specifically for the mining industry.

The platform supports workflows across geological modelling, exploration, open-pit and underground mine planning, scheduling, and other mining disciplines. Its modular structure allows companies to begin with the tools required for a specific project and expand the system as their needs develop.

K-MINE helps teams organize and interpret geological data, plan exploration drilling, evaluate targets, develop short- and long-term mine plans, and connect mining workflows within a shared digital environment.

The software is also supported by practical mining and consulting experience, helping ensure that its tools address real operational and engineering requirements.

K-MINE's consulting team includes Qualified Persons and professionals involved in technical studies, mineral resource estimation, and reporting under frameworks such as NI 43-101, JORC, and S-K 1300.

Why do exploration programs sometimes underperform even when they have experienced geologists and substantial budgets?

Exploration drilling is expensive. Every drillhole represents a significant investment, and every hole that misses its target can mean lost time, money, and opportunity.

The objective is not simply to drill more holes. It is to drill the right holes as accurately and efficiently as possible.

Achieving this requires combining geological expertise with modern digital tools.

One of the first challenges is geological uncertainty.

During the early stages of exploration, understanding of the deposit is naturally limited. Geological boundaries may be unclear, structures can be complex, and mineralization is rarely as continuous or predictable as we would like it to be.

As a result, important drilling decisions often have to be made using incomplete information.

Even a carefully planned drilling program can underperform when drillholes are positioned or oriented incorrectly.

Common problems include missing the mineralized zone, selecting the wrong collar location, drilling at an unsuitable angle or azimuth, and using drill spacing that is either too wide to provide sufficient confidence or unnecessarily dense for the project stage.

These mistakes reduce drilling efficiency and increase exploration costs.

Another major challenge is data quality.

When a drilling database contains survey errors, inconsistent geological logging, missing assay results, or weak QA/QC procedures, even a sophisticated geological model can produce unreliable results.

Data fragmentation creates another problem.

On many projects, geology, geophysics, resource modelling, and drill planning are managed by different teams using separate systems. Data may need to be repeatedly exported, converted, transferred, and validated.

This creates delays, increases the risk of errors, and makes collaboration more difficult.

Exploration budgets are also limited. The objective is therefore not simply to reduce cost, but to maximize the quantity and quality of geological information obtained from every metre drilled.

Time is another constraint. Exploration programs operate under deadlines, so teams need to make decisions quickly while maintaining confidence in their data and geological interpretation.

Integrating exploration data into a drilling plan means transforming raw geological, geophysical, geotechnical, and geomechanical information into practical drilling constraints and design criteria.

This helps ensure that each drillhole follows a safe and efficient trajectory, avoids known hazards, and intersects the intended geological or structural target as effectively as possible.

It can also reduce unnecessary drilling, operational delays, and non-productive time.

Before drilling decisions are made, exploration data should first be consolidated, validated, and standardized.

The process typically begins with geospatial data integration.

Surface geology, topography, remote sensing data, geophysical surveys, and existing drillhole information can be brought together within a central database or geological information system.

Drill logs, core logging records, downhole surveys, geotechnical measurements, and assay results should also be digitized, normalized, and checked for consistency.

This allows subsurface properties to be compared reliably across the entire exploration area.

Once the data has been organized and validated, separate datasets can be transformed into integrated geological, structural, and geomechanical models.

These models provide the foundation for identifying targets, evaluating drilling risks, and designing drillholes that collect the most valuable geological information.

Drill planning requires teams to transform three-dimensional subsurface models and two-dimensional surface constraints into practical, optimized drilling targets.

Geographic information systems can play an important role by integrating environmental information, infrastructure, geological data, geophysical information, and access constraints within a coordinated spatial environment.

Water bodies, protected habitats, environmentally sensitive areas, and cultural heritage sites can be mapped so restricted zones are identified early.

Digital terrain models can be used to evaluate slope conditions, site accessibility, and the suitability of potential drill pad locations.

Existing roads, power lines, property boundaries, land-access rights, and mining tenure can also be considered.

This allows planners to evaluate not only the geological value of a target, but whether it can be accessed safely, legally, and economically.

Geophysical survey results can be combined with interpolated geochemical anomalies from soil sampling, stream sediments, and rock-chip assays.

Structural interpretations, including faults and folds, can be integrated with airborne magnetic and gravity data.

Historical drilling results can also help identify mineralized and non-mineralized zones.

When these datasets are transformed from separate two-dimensional maps into an integrated three-dimensional environment, relationships may become visible that would otherwise be difficult to recognize.

This creates a stronger foundation for selecting drilling targets, designing drillhole trajectories, and prioritizing the next stage of exploration.

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, geomechanical, and drilling data, and identify areas where additional information is still required.

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

K-MINE also includes an AI-assisted tool for generating predictive 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.

Consider an area where 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 flanks or increase drilling density where additional geological confidence is required.

Historical exploration data can be consolidated into a composite table.

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

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

These parameters may include modelling iterations, trend parameters, azimuth, dip, and other characteristics describing the geometry and orientation of 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.

An AI-generated predictive block model may not satisfy the requirements for public mineral resource reporting.

One reason is that the calculation and interpolation methodology may not always be independently validated to the level required by the applicable reporting framework.

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

It can help geologists explore interpretations, identify areas of interest, and decide where additional drilling may provide the greatest benefit.

The geological model can also be combined with geomechanical information.

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

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

The 3D geological model can then be compared with potential drilling hazards such as weak or washed-out zones, overpressured intervals, fractured ground, and fault intersections.

Another major benefit of digital drill planning is drillhole trajectory optimization.

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

The drillhole planning tools within K-MINE can help optimize drillhole geometry to obtain useful, representative geological information while minimizing cost and operational risk.

Alternative trajectories can be evaluated against project safety and engineering constraints.

Before drillholes are designed, 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. Drillholes may therefore be oriented to intersect higher-grade zones or collect 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 a proposed trajectory.

A drillhole may be designed to avoid these features.

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

Each drillhole trajectory is defined using several key geometric 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, 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 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 can also be specified.

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

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

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

New drillholes or sections can also be generated relative to an existing collar, with the new collar offset by a specified distance and azimuth.

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

Several drillholes with different azimuths and inclinations can be designed from a single collar or drill station.

This makes it possible to test multiple targets while reducing surface preparation and equipment movement.

Real drillholes are rarely perfectly straight.

When planning deeper holes, particularly those exceeding approximately 200 metres, expected natural deviation can be incorporated into the design using historical information from nearby drillholes.

Downhole survey stations may also be defined at regular intervals, for example every 30 metres.

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 verifying that every proposed trajectory satisfies the project's engineering 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 helps teams estimate expected non-productive time, forecast drilling performance, and prepare more reliable drilling budgets.

Targets, trajectories, design parameters, technical risks, expected drilling time, and estimated costs can then be compiled into a comprehensive project report.

Once the drilling plan has been finalized, it can be exported as a set of tables, schedules, and graphical materials.

One output is the collar table, which contains the drillhole ID, planned X, Y, and Z coordinates, proposed azimuth, and planned dip.

Another output is the drilling schedule, which defines the planned drilling sequence, target depth for each drillhole, and estimated drilling time in days or shifts.

The system can also produce geological section views.

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

A technical drilling specification can also be prepared for the drilling contractor.

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

Together, these deliverables translate geological interpretation into clear, practical, and actionable instructions for the drilling team.

Another important question in exploration planning is drill spacing.

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

In practice, the answer is more complex.

If drill spacing is too wide, important geological features may be missed, local variations overlooked, or the continuity of the mineralized body overestimated.

If drillholes are spaced too closely, however, the drilling program may become unnecessarily expensive without producing a meaningful increase in geological confidence.

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

It 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.

Appropriate spacing depends on deposit type, geological complexity, orebody geometry, grade variability, the stage of exploration, project objectives, and the available budget.

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

Wide-spaced drilling is typically used during the early stages of exploration.

The objective is to confirm whether mineralization is present, estimate the potential scale of the target, and keep initial exploration costs under control.

During regional exploration, teams may initially be trying to answer one basic question: is there mineralization here?

In this situation, relatively wide drill spacing may be appropriate, sometimes in the range of 400 to 800 metres depending on the deposit type, target geometry, and available geological information.

The advantages are lower initial costs and the ability to test a large area relatively quickly.

The disadvantage is greater geological uncertainty.

With widely spaced drillholes, there is always a risk of missing narrow, discontinuous, or structurally controlled mineralized bodies.

Once a promising target has been identified, drill spacing can gradually be reduced.

The program may move from initial target testing to defining the geometry and continuity of the mineralized body and eventually to infill drilling.

At more advanced stages, drill spacing may be reduced to approximately 10 to 50 metres, although the appropriate distance always depends on the geology and project objectives.

Each stage provides additional information, improves confidence in the geological interpretation, and reduces uncertainty.

Rather than drilling the entire project area on a dense grid from the beginning, teams can start with wider spacing, evaluate the results, update the geological model, and determine where additional drilling will provide the greatest value.

This allows the exploration budget to be used more efficiently and supports the staged collection of information required for resource estimation and classification.

Regular drilling grids are no longer the only option available to exploration teams.

Increasingly, drillholes can be concentrated in the most prospective areas rather than distributed evenly across the entire project.

Priority areas may be identified through geological interpretation, geophysical anomalies, geochemical results, structural analysis, historical drilling, and 3D geological models.

Targeted drilling can be particularly useful during early-stage exploration when teams need to test specific geological concepts or anomalies without committing to a complete drilling grid.

It can also be used during later stages to test extensions, investigate structural controls, and close important gaps in the geological model.

In practice, many exploration programs combine wide-spaced, progressive, and targeted drilling.

For structurally controlled deposits, including many gold systems, drillhole orientation can be even more important than drill spacing.

Drillholes can be designed to intersect faults, shear zones, mineralized veins, and other controlling structures at the most informative angle.

Ideally, the drillhole should intersect the target as close to perpendicular as possible.

This helps geologists determine the true thickness, geometry, and continuity of the mineralized zone more accurately.

A single well-positioned and correctly oriented drillhole can provide more useful information than several holes drilled in the wrong direction.

Regular drilling grids have traditionally been popular because they are relatively easy to design, manage, and interpret.

For example, drillholes may be positioned on a 400-by-400-metre grid during early-stage exploration or a 50-by-50-metre grid during more detailed drilling.

This method can work well for large, relatively continuous deposits.

However, a regular grid does not always reflect the true complexity of the geology.

It may result in unnecessary drilling in low-priority areas while leaving important structural or geological questions unresolved.

Modern digital technologies make more flexible drilling strategies possible.

Modern geological software allows the geological model to be updated as new drilling results become available.

Instead of following a fixed drilling pattern throughout the entire campaign, the program can be adjusted after each stage of drilling.

Following each drilling phase, the team updates the geological model, evaluates the new information, identifies areas of remaining uncertainty, and determines where the next drillholes will have the greatest impact.

The drilling campaign therefore becomes a continuous learning process rather than a fixed plan developed months in advance.

Each new drillhole provides information that can influence the location, orientation, and priority of the drillholes that follow.

Digital drill planning tools make it possible to test alternative scenarios, update proposed trajectories, and redirect exploration toward areas where additional drilling is most likely to improve geological confidence and project value.

Risk-based drilling focuses on areas where geological uncertainty has the greatest potential impact on the project.

Teams may use uncertainty analysis, probabilistic models, scenario testing, geostatistics, and sensitivity analysis to determine where the geological model is least reliable and where additional information is most important.

The objective is not simply to drill the areas with the highest uncertainty.

It is to identify the uncertainties that create the greatest technical or economic risk and design drillholes that reduce those risks effectively.

Each drillhole can therefore be selected based on the value of the information it is expected to provide.

Value-driven drilling takes this concept further.

Under this approach, every proposed drillhole should make a meaningful contribution to the overall value of the project.

Before a drillhole is approved, the team can ask several questions.

How much will it reduce geological uncertainty?

Could it support a change in resource classification?

Will it materially improve the geological or structural interpretation?

Could the result influence the project's economic model?

Is the expected value of the information sufficient to justify the cost of drilling?

Instead of continuing to drill areas that are already well understood, teams can focus on parts of the deposit where new information could have the greatest impact.

This may include testing an uncertain extension of the mineralized body, resolving a structural interpretation, improving confidence in a high-value area, or collecting information needed to support future resource conversion.

There is no single correct drill spacing.

The optimal spacing is the spacing that provides the required level of geological confidence while minimizing unnecessary drilling.

Modern digital workflows help exploration teams quantify uncertainty, compare alternative drilling scenarios, prioritize drillholes with the greatest potential impact, and use exploration budgets more effectively.

Successful drill planning is not about drilling more holes.

It is about ensuring that every new drillhole answers an important geological question and contributes meaningful value to the project.

Mineral exploration is moving toward workflows where decisions are supported not only by geological experience, but also by integrated data, advanced analytics, and artificial intelligence.

Traditionally, exploration followed a sequence of separate stages.

Teams collected field data, interpreted the geology, constructed a model, planned a drilling campaign, waited for results, updated the model, and repeated the process.

This was often slow and highly manual.

Today, that process is becoming increasingly continuous.

As new drilling results become available, they can be integrated into the geological database. Three-dimensional models can be updated, uncertainty reassessed, and alternative drilling targets evaluated much more quickly.

AI can support this process by highlighting patterns, identifying information gaps, and helping teams evaluate areas where the next drillhole may provide the greatest value.

Exploration teams already work with enormous volumes of information, including drillhole data, assay results, geophysics, geochemistry, satellite imagery, drone surveys, spectral data, LiDAR, structural measurements, and historical mining records.

The challenge is no longer simply collecting data.

The challenge is bringing these datasets together within a connected digital ecosystem.

Integrated geological databases allow information to be managed in a shared environment instead of being scattered across separate spreadsheets, files, and software systems.

This allows project teams to work from validated information and a consistent geological interpretation.

It improves collaboration, reduces duplication, and makes it easier to respond when new information becomes available.

Artificial intelligence can process large volumes of information, recognize patterns that may not be immediately visible, and evaluate many possible scenarios much faster than could be done manually.

However, geology involves much more than pattern recognition.

Understanding how a deposit formed, recognizing structural controls, evaluating unexpected results, and deciding whether an interpretation makes geological sense still require the expertise, experience, and judgment of geologists.

The role of AI is not to replace the geologist.

It is to help geologists work more efficiently, test more scenarios, and focus their attention on decisions that require professional interpretation.

One important development is the concept of the geological digital twin.

A geological digital twin is a dynamic representation of a project that evolves as new information becomes available.

Instead of rebuilding the geological model only after each drilling campaign, the model can be continuously updated throughout the life of the project.

The geological interpretation is therefore no longer treated as a static result.

It becomes a living model that develops alongside the exploration program.

Drilling programs are also likely to become increasingly adaptive.

After each drillhole is completed, the system may help evaluate how much geological uncertainty has been reduced, which questions remain unresolved, and which next drilling options could provide the greatest value.

The objective is not simply to select the next accessible drill location.

It is to identify the next drillhole expected to provide the greatest geological and project value.

Ultimately, the future of exploration will not be defined by one individual technology.

It will depend on the integration of geological expertise, reliable data, 3D modelling, collaboration, real-time updates, and artificial intelligence within a continuous workflow.

The goal remains the same: reduce uncertainty, make better use of exploration budgets, and ensure that every new drillhole contributes meaningful information to the project.