Mineral Exploration Drilling Data Quality & QA/QC

Mineral exploration drilling can generate valuable information about underground geology, mineralization and potential resource development. However, the usefulness of a drilling program depends on more than the number of boreholes completed or the volume of samples collected. Mining companies also need reliable geological logging, representative sampling, accurate drillhole surveys, laboratory quality control and well-managed exploration databases.

In Saudi Arabia, the Saudi Geological Survey’s National Geological Database includes drilling information such as borehole locations, drilling methods, depths, lithology, geological structures, mineralization, sampling and chemical analyses. These data categories illustrate why drilling information must be collected and documented systematically.

For exploration managers, project geologists and mining companies, the key question is not simply whether drilling is complete. It is whether the resulting data are sufficiently reliable, traceable and appropriate for the decisions the project needs to make.

This guide explains how to evaluate mineral exploration drilling data, identify common quality issues and establish practical checks throughout the drilling workflow.

1. Start With the Purpose of the Drilling Program

Before reviewing individual drillholes, confirm what the exploration program is intended to establish.

Different objectives require different drilling methods, sampling approaches and levels of geological detail. Early-stage exploration may focus on understanding regional geology or testing an exploration target. More advanced programs may investigate mineralization continuity, geometry and other characteristics needed for technical evaluation.

A drilling data review should begin with the following questions:

What geological question is the drilling program intended to answer?
Which drilling methods were used, and why were they selected?
Were the planned drillhole locations and depths appropriate for the exploration objectives?
What geological, geotechnical or analytical information was required?
Were sampling and quality control procedures documented before fieldwork began?
Are the final deliverables suitable for the next stage of technical assessment?

These questions help reviewers assess whether the collected information addresses the original exploration objectives.

A large dataset is not automatically a useful dataset. Its value depends on geological relevance, data quality, completeness and the suitability of the methods used to collect it.

2. Verify Drillhole Location, Depth and Survey Information

Drillhole location data provide the spatial foundation for geological interpretation. If collar coordinates, coordinate reference systems or downhole surveys are inaccurate or inconsistent, the interpretation of underground geology may be affected.

For each drillhole, review the following information where applicable:

Data field	What to verify
Drillhole ID	Unique and consistent identification
Collar coordinates	Correct coordinate system, units and recorded location
Planned and actual depth	Differences between the drilling plan and completed hole
Drillhole orientation	Recorded azimuth and inclination
Downhole surveys	Survey measurements at the required intervals
Final hole depth	Agreement between drilling records and the database
Hole status	Completed, abandoned, suspended or otherwise classified

Why coordinate consistency matters

Exploration datasets may combine information collected by different teams, contractors or survey systems. Coordinates must therefore be interpreted using the correct reference system and units.

For example, coordinates entered in the wrong system or imported with incorrect units can make a drillhole appear in the wrong location on a geological map. This may lead to misleading spatial comparisons.

A practical quality check is to compare collar and survey information with the original field records and the project’s approved coordinate reference system.

Any discrepancies should be investigated and documented rather than corrected through undocumented assumptions.

3. Review Geological Logging for Consistency

Geological logging converts physical core or drilling samples into structured records that can be compared across drillholes.

Depending on the project, logging may describe lithology, alteration, mineralization, weathering, structural features, recovery and geotechnical characteristics.

The objective is to produce records that are consistent enough for the intended geological interpretation.

Geological logging checklist

Review whether:

Logging intervals cover the relevant drilled intervals.
Recorded depths are consistent with drillhole records.
Lithology codes follow an approved project dictionary.
Geological descriptions use consistent terminology.
Changes in lithology and alteration are recorded at appropriate intervals.
Mineralization observations are documented without confusing visual appearance with laboratory-confirmed grade.
Core photographs or other relevant records are available when required.
Missing intervals, damaged core or uncertain observations are clearly identified.

Common logging problems

Inconsistent terminology: Different geologists may use different descriptions for similar rock types. An approved logging dictionary can reduce this inconsistency.

Depth mismatches: Geological intervals may not align with drill runs, sample intervals or recorded hole depths. These differences should be reconciled.

Unsupported interpretations: Visible mineralization does not, by itself, establish the economic grade or continuity of a deposit. Geological observations should be distinguished from analytical results.

Incomplete records: Missing intervals or unclear codes can complicate later database validation and interpretation.

The level of logging detail should match the project’s objectives and the requirements of subsequent geological or technical studies.

4. Assess Sample Recovery and Representativeness

Sample recovery and representativeness are important considerations when evaluating drilling data.

For diamond core drilling, core recovery is generally assessed by comparing the length of recovered core with the corresponding drilled interval. For other drilling methods, the appropriate recovery or sample collection measures depend on the method and equipment used.

However, recovery percentage alone does not establish that a sample is representative.

Material may be preferentially lost or gained during drilling, collection or sample preparation. Depending on the geology and mineral characteristics, this can introduce bias into the resulting data.

A review should consider:

How recovery was measured and recorded.
Whether intervals with poor recovery were identified.
Whether recovery patterns correspond to particular rock types or geological conditions.
Whether the project investigated possible relationships between recovery and assay results.
Whether any limitations were recorded in the final dataset.

For example, if fine material or particular mineral-bearing fractions are lost disproportionately, the recovered sample may not accurately represent the original interval.

The implications depend on the drilling method, geological conditions and mineralization style. Suspected sampling bias should be investigated by qualified technical personnel rather than assumed to be negligible.

5. Check Sampling Procedures and Sample Traceability

Sampling is the link between geological observations and laboratory analytical results. Errors at this stage can reduce confidence in the data even when drilling and logging have been performed correctly.

A reliable sampling workflow should maintain traceability from the original drill interval through sample preparation and laboratory submission.

What should be checked?

Sample intervals: Confirm that sample boundaries, depths and identifiers are consistent with the geological logs and project procedures.

Sample identification: Verify that labels and database records correspond to the correct drillhole and interval.

Sample preparation: Confirm that the preparation method is documented and appropriate for the material and analytical objectives.

Sample handling: Review packaging, storage, transport and dispatch records as applicable.

Chain of custody: Establish whether sample transfers and laboratory submissions can be traced through the documented workflow.

Sample reconciliation: Check for missing, duplicated, incorrectly labelled or unaccounted-for samples.

Why traceability matters

Consider a sample whose laboratory result appears unusually high. Before interpreting the result geologically, the team should confirm that the sample belongs to the correct drillhole and interval, that the laboratory submission was correctly recorded and that the result was imported into the correct database field.

This does not mean the result is incorrect. It means that the result should be verified before it is used to support a technical conclusion.

6. Review Assay Quality Assurance and Quality Control (QA/QC)

Laboratory results are central to many mineral exploration decisions. Quality assurance and quality control procedures help assess whether sampling and analytical results are sufficiently reliable for their intended use.

The specific QA/QC program should be designed around the project’s mineralization, sampling methods, analytical techniques and reporting requirements.

Common control materials include:

Certified reference materials or standards: Used to assess analytical accuracy against established reference values.
Blanks: Used to investigate potential contamination during relevant stages of the workflow.
Duplicates: Used to evaluate precision and variability at the sampling or analytical stages being tested.
External laboratory checks: Used where appropriate to investigate analytical agreement or discrepancies.

These controls answer different questions. A blank does not test the same thing as a certified reference material, and a duplicate does not automatically establish analytical accuracy.

A practical QA/QC review process

Step 1: Confirm the control program. Check which controls were included, where they were inserted and which procedures applied.

Step 2: Review laboratory certificates. Verify sample identifiers, analytical methods, units, detection limits and reported results.

Step 3: Assess control results. Compare results with the project’s documented acceptance criteria.

Step 4: Investigate failures. Determine whether an out-of-range result reflects contamination, analytical problems, sample variability, data entry errors or another cause.

Step 5: Document corrective actions. Record the investigation, decision, responsible person and any required reanalysis or data qualification.

Step 6: Verify database updates. Ensure that any replacement, corrected or qualified result is traceable to the original record.

Acceptance limits should be established by qualified personnel for the specific QA/QC program. There is no single universal threshold that is appropriate for every mineral, laboratory method and sample type.

Importantly, a successful QA/QC review increases confidence in the data but does not independently establish that a mineral deposit is economically viable.

7. Validate the Exploration Database Before Interpretation

A geological database brings together information from drilling, logging, sampling, surveys and laboratory analyses. Before using it for geological modelling or resource evaluation, the project team should confirm that its records are complete and internally consistent.

A useful database review includes the following checks:

Validation check	Potential issue identified
Unique drillhole IDs	Duplicate or conflicting records
Collar-to-survey matching	Missing or incorrectly linked survey data
Depth interval checks	Overlapping intervals, gaps or intervals beyond final hole depth
Sample ID matching	Laboratory results linked to the wrong sample
Assay units and detection limits	Inconsistent analytical values or units
Missing values	Incomplete records that require investigation
Code validation	Unrecognized lithology, alteration or mineralization codes
Change history	Corrections that cannot be traced to source records

Automated validation rules can identify many inconsistencies, but they cannot resolve every geological question. A record may pass a database check while still requiring technical review.

For this reason, database validation should combine automated checks with reconciliation against original logs, survey files, sample records and laboratory certificates.

8. Common Red Flags in Mineral Exploration Data

The following issues should trigger further investigation before the data are relied upon for important decisions.

Unexpected high-grade results without supporting QA/QC: Review control results, sample identification, laboratory documentation and geological context.

Repeated missing survey records: Determine whether the available spatial information is sufficient for the intended interpretation.

Poor or undocumented sample recovery: Assess potential sample bias and whether the affected intervals require qualification.

Inconsistent geological codes: Standardize terminology where justified and preserve the original records and change history.

Unexplained gaps in sample numbering: Reconcile field records, dispatch lists and laboratory submissions.

Conflicting final hole depths: Check drilling logs, survey data and database entries before using the affected intervals.

Unrecorded corrections to assay results: Establish which values were changed, why they were changed and which results are approved for use.

A red flag does not automatically invalidate an entire exploration program. The appropriate response depends on the nature, scale and significance of the issue. Findings should be documented and assessed by the responsible technical team.

9. A Practical Drilling Data Review Checklist

Before accepting an exploration dataset for further technical work, project managers can use this checklist as an initial screening tool.

Drillhole information

Collar coordinates and coordinate reference system verified.
Drillhole orientation and available downhole surveys reviewed.
Planned and actual depths reconciled.
Final hole status documented.

Geological information

Geological logging intervals checked for completeness.
Lithology and alteration codes reviewed for consistency.
Recovery records assessed.
Relevant core or sample photographs reviewed where required.

Sampling and assays

Sample IDs reconciled with drillhole intervals.
Sample preparation and handling procedures documented.
Chain-of-custody records checked where applicable.
Laboratory certificates reconciled with the database.
QA/QC results assessed against approved project criteria.
Failed controls and corrective actions documented.

Database and reporting

Duplicate IDs and invalid intervals investigated.
Units, detection limits and missing values checked.
Changes to source data are traceable.
Outstanding limitations are recorded.
Dataset acceptance is documented by the responsible technical personnel.

This checklist is a screening framework, not a substitute for a project-specific QA/QC protocol, technical audit or competent-person review where required.

10. How Saudi Arabia’s National Geological Database Supports Exploration Context

The Saudi Geological Survey’s National Geological Database provides a useful starting point for understanding the types of geological information available in the Kingdom.

Its drilling database describes information including borehole locations, drilling methods, depths, lithology, geological structures, mineralization, sampling and chemical analyses. The wider portal also provides access to geological maps, mineral occurrence information and other geoscience datasets.

Exploration teams can consult the Saudi Geological Survey National Geological Database and its Mineral Occurrences Database when researching available geological information.

Regional datasets can help provide context for exploration planning, but they do not replace project-specific field investigation, verification or technical interpretation. Data availability and suitability should be assessed for the particular exploration objective.

11. How Geological and Drilling Support Contributes to Better Data Quality

Reliable exploration data depend on coordination between drilling contractors, field geologists, sampling personnel, core shed teams, laboratories and database specialists.

Each part of the workflow contributes to the final quality of the dataset. Clear drilling records help geologists interpret subsurface conditions; consistent logging supports comparisons between drillholes; traceable samples connect field observations to laboratory results; and validated databases support subsequent technical evaluation.

For mining companies in Saudi Arabia, these activities should be planned as connected parts of the exploration workflow rather than isolated tasks.

Darkstone’s website provides further information about its Mining Services in Saudi Arabia, Geological Services project and RC & Core Drilling Program for the PHOS-3 Project.

These pages can help readers explore related service and project information. Specific service capabilities, project responsibilities and deliverables should always be confirmed against the relevant project scope.

Frequently Asked Questions

What is mineral exploration drilling data?

Mineral exploration drilling data include drillhole locations, depths, orientations, geological logs, sample records and laboratory analyses collected during exploration. Depending on the project, the dataset may also include recovery measurements, geotechnical information and other technical records.

Why is QA/QC important in mineral exploration?

QA/QC procedures help assess the reliability of sampling and analytical results. Appropriate controls can identify potential contamination, analytical bias, variability and other problems that may affect confidence in the dataset.

Does high core recovery guarantee reliable assay results?

No. Core recovery is one aspect of sample assessment. Reliable results also depend on sample representativeness, handling, preparation, laboratory methods and appropriate QA/QC procedures.

How can a mining company identify errors in drilling data?

A company can reconcile drillhole records, geological logs, sample identifiers, survey information and laboratory certificates. Database validation rules and documented QA/QC reviews can help identify inconsistencies requiring further investigation.

Can regional geological data replace field exploration?

No. Regional datasets can help establish geological context and support exploration planning, but project-specific drilling, sampling, verification and interpretation may still be necessary.

When is drilling data ready for further geological evaluation?

The dataset should be sufficiently complete, traceable and appropriate for the intended purpose. Outstanding issues should be investigated or explicitly documented, and acceptance should follow the project’s technical procedures.

Conclusion

The value of mineral exploration drilling depends on the reliability of the information collected throughout the program. Accurate drillhole surveys, consistent geological logging, representative sampling, documented QA/QC procedures and validated databases all contribute to better-informed exploration decisions.

For mining companies working in Saudi Arabia, a structured drilling data review can help identify gaps early, improve traceability and clarify whether the available information is suitable for further technical evaluation.

The goal is not simply to collect more data. It is to establish a reliable evidence base that supports geological interpretation, responsible project planning and informed decisions at each stage of mineral exploration.

Explore Darkstone’s Mining Services in Saudi Arabia and related geological project information to learn more about mining support activities in the Kingdom.

How to Evaluate Mineral Exploration Drilling Data in Saudi Arabia: A Practical QA/QC Guide