Recently I’ve been thinking a lot about the relationship between sample quality and drilling data.

Now of course, sampling is the most important part of any exploration program. Without samples, there can’t be a discovery. But how do we get those samples?

By drilling.

So I was surprised by how little I was able to find when I tried to take a deeper dive into the relationship between drilling data management and sample quality and what exploration teams are doing about it. And I’ve been left wondering why this has not been talked about more widely in the industry.

I figured that maybe I need to be the one to start talking about it, and why it’s actually super important. And I want to share with you what I’ve learned, and invite you to join me in questioning the acceptability of using spreadsheets for drilling data too.

If that’s of interest to you - let’s get into it!

The impact of high quality drilling data on sampling

“The greatest opportunity to achieve and maintain quality of the drilling sample is at the time of drilling.” (Hughes 2018)

Exploration teams want to have a high level of confidence in their resource estimation and reporting.

Great drilling data helps to form this foundation by acting as evidence that the process of acquiring samples was executed correctly, and that the samples can be traced back to their point of origin.

When key drill hole information like collar locations, dip, azi and depth are correct, you can be sure that the samples you’ve drilled are indeed representative of the intended target, eliminating the risk of mistakenly attributing samples to the wrong location, subsequently impacting the integrity of your logging and assay data.

Reliable drilling data allows for verification of the sample’s location and a thorough examination of sample recovery and the factors that may have influenced sample quality down the hole.

By understanding the drivers behind fluctuations in sample quality, such as who the driller was, the ground conditions, penetration rate, rig type, or bit used, appropriate modifications can be made in a timely manner, ensuring the integrity of the sample before it’s too late.

Because what would be worse than over or underestimating what’s beneath the surface?

Examples of hidden clues in drilling data that impact on sample quality

Diamond core drilling

Core recovery is a leading source of sampling errors in diamond core drilling. Poor core recovery from your drillers means a loss of material, and a poorly representative sample. But a core recovery of greater than 100% can mean your drillers aren’t documenting their runs correctly - which may correspond to the associated intervals and reported location of the sample down the hole (oops).

RC drilling

If the pen rate of your RC intervals is too high, the sample can be accidentally pulverised. This can compromise quality and accuracy through loss of particles to dust or unintentional particle mixing between intervals.

RC sample bias can also be revealed through variations in bag weights. Drilling events such as flooding, bit change and weather have the potential to create loss of fine particles in the bag through water drainage or affect sample return, which are detailed in the drillers’ runsheet or plod.

So, why doesn't this data doesn’t belong on spreadsheets - and what's the impact of the status quo?

While spreadsheets have been a common tool for managing drilling data, they come with inherent limitations that hinder efficiency and accuracy - and your ability to control the quality of sample coming out of the hole.

But what does that actually look like for your team as part of your daily operations?

Moving slowly

In today’s operating environment with tight budgets and deadlines, staying agile is key. Entering drilling data into spreadsheets simply takes too long. By the time your geos have processed and analysed it, the drill has already moved on - and you’ve lost a potential opportunity to respond to what’s coming out of the ground and get closer to discovery.

Inaccurate data

Spreadsheets don’t have any sort of control to prevent inaccurate data from being entered by geologists making accidental typos or entering data into the wrong cells. But this is inevitable when you’re having to manually enter up to thousands of values per sheet.

If you’re lucky, you’ll find out later if you get a #REF! Error when using a formula - but not all typos are easily revealed in this way, meaning they could make it all the way to your database and reporting.

This could have consequences down the road, for example if a twinned hole was planned based on the wrong hole ID or bit type, or even reputation damage from mistakes ending up in your JORC Table 1 or drill hole appendix (yikes).

Now that we’ve discussed some of the shortcomings of spreadsheets, you might not be convinced that the efficiencies gained from modern software outweigh the savings of continuing to use Excel.

Let’s explore that.

How spreadsheets create false economies for mining teams

“False economies by attempting to save money in data collection should be avoided because poor quality data obtained cheaply can be expensive later on.” (AusIMM, Monograph 23)

We discussed some of the limitations of using spreadsheets for your drilling data on sample quality, but what about the hidden costs - and how can they come back to bite mining teams later on?

It’s cheap…..up front

In terms of balance sheet costs, Excel is extremely cheap - especially by mining standards. But what doesn’t show up in that budget is the amount of time it takes to format it for drilling data, conduct manual checks and build formulas to validate data, and to fix mistakes. Time that gets taken away from important things like the geology itself.

Not to mention, this wrangling time isn’t easily scaled in line with your drill program if you find something. Either your geos will have to spend more time in the cells, or you’ll have to hire more geos to keep up with the workload.

… but it can come back to bite you later

One of the big problems with using Excel to store your drilling data is that it’s extremely difficult to prove that no errors were made in the data entry. There’s also no change log to support an auditable data history (I’ve written another blog about this and how it relates to Section 3 of Table 1 JORC reporting).

And whilst yes, you could allocate extra time to running validation checks before importing data into your database later - wouldn’t it be better to collect the right data in the first place, rather than trying to fix it after the fact?

Plus, if you do make a hit later, you could run into trouble with future drill hole planning over something as simple as the wrong bit type being recorded. And whilst yes, you could fudge it - that would be poor practice (not ideal).

So, what can you do about it?

The good news is that there are ways to get around this. Here are a few options that you could consider.

Outsource responsibility to your drill contractor

Putting the onus on your drilling contractor to handle the capture and management of drilling data is a potential solution. Ultimately, it would come down to the level of trust you have with your drill contractors in their data capture and management processes.

We have a list of digital drill contractors who use modern software to capture accurate drilling data in the first instance and share it with you in your preferred format.

The key issue with choosing this alternative is that it’s dependent on which contractor you work with - and if you’re working with multiple contractors on a project, you might not be able to convince them all to work in the same way.

Spend more time wrangling data

Yes it’s possible, but I wouldn’t recommend it. Spending more time trying to hunt down errors as opposed to preventing them is a band-aid fix, not a solution.

Build your own system

Building custom software can be an incredibly expensive and time consuming process. Your options here would include hiring a development company to build it for you (and having to hold their hand most of the way because they aren’t exploration specialists) or hiring your own in-house engineering team. Oh - and don’t forget the cost of ongoing development and maintenance.

We’ve seen many mining companies spend hundreds of thousands of dollars and years of work on building their own software -only to shelve it before it even gets to the team. Strongly avoid it if you can!

Use a commercially available product built to solve the problem

Commercially available software as a service products like CorePlan are built to help you with drilling data validation and quality control.

These products are intermediary solutions that prevent errors from entering your database so that you can rely on it as your ultimate source of truth. The process involves using a structured data capture and management process to help prevent and catch errors like the wrong bit type, gaps in intervals, missing survey data and more at the source - the drill plod.

Add in a touch of automation along the way and your geologists can stop spending excessive time on data wrangling, spend more time understanding what’s beneath the surface and make more confident assertions in their results reporting.

“But I spent a lot of time building that”

Ok maybe you’ve read this far but you aren’t ready to let go just yet. You may have spent years developing the perfect spreadsheet with an awesome colour scheme and macros and you’re emotionally attached. Who wouldn’t be!

But the thing is - modern practices are changing in line with developing technology. Now that there is the capability to control drilling data better, it’s inevitable that, due to the relationship between drilling and sampling quality and reporting, this will enter the chat at some point in more places like the Table 1 - and it’ll become a jump or be pushed situation.

So it might be time to break up with that spreadsheet and start moving forward - before you get left behind.

Interested in reading more about drilling data management?

Follow us on LinkedIn or check out 5 strategies that geologists can use to nail data capture and management on the road to JORC resource.

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Drilling data on spreadsheets: A false economy - and sampling nightmare?

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