How to get the most out of your borehole geophysical log

In September’s print edition of GDI, Jake Anderson, a geologist with Maptek, talked about the importance of collecting data. Now he examines how to utilise that data.
How to get the most out of  your borehole geophysical log How to get the most out of  your borehole geophysical log How to get the most out of  your borehole geophysical log How to get the most out of  your borehole geophysical log How to get the most out of  your borehole geophysical log

Borehole geophysics are a critical part of figuring out your lithology types and seam roofs and floors

Jake Anderson

There is no getting around it, data is key to the success of your mine. And your data collection starts during exploration. By collecting seemingly ancillary data during exploration, your mine can realise significant benefits down the road - such as increased resource model accuracy, an enhanced understanding of your deposit, and improved life-of-mine processes.

For example, your borehole geophysical log holds an immense amount of data that can deepen your understanding of your deposit. By fully utilising this data, and other exploration data, you can maximise the effectiveness of downstream operations.

A closer look at the borehole geophysical log

For anyone working in sedimentary deposits like coal or even industrial minerals, borehole geophysics are a critical part of figuring out your lithology types and seam roofs and floors.

With access to this data, you can identify material types and mineable units.

 igure 1  eophysical og downhole log with gama yellow density green and resistivity blue curves Figure 1 - Geophysical ‘E-Log' downhole log with gama (yellow), density (green) and resistivity (blue) curves


An example of a canned data set for a borehole geophysical log can be seen in figure 1. There are three different curves all dominated by different material types referenced on the left-hand side of the image. The first curve (in yellow) is the gamma curve, which is the nuclear gamma affinity. Higher gamma readings indicate higher clay content material. The second (in green) is the density curve and the third curve (in blue) is the resistivity curve, which helps you identify the presence of water in your drillhole.

The typical way to use this data is to primarily look at the gamma curve in relation to the density curve - as a secondary point of reference. By comparing these two curves you can make your picks for the roof or floor measurement of that unit.

While this method is great for identifying material types, it only uses about 33 per cent of your available borehole geophysical log data. This means that almost 70 per cent of your borehole geophysical log data is being wasted.

But this does not have to be the case. You can use the additional data from these curves to help increase the accuracy of your resource model and make the most of your borehole geophysical log.

Increase density accuracy with your geophysical log

As you probably know, creating an accurate resource model is key for any exploration geologist. This model is the foundation for your mine plans and schedules, and if it is not accurate, it could negatively impact downstream activities.

One factor that can impact the accuracy of your resource model is your reserve density. Most operations will use a default density which is a perceived average density for all material types based on expensive, time-consuming lab samples. And, if you are lucky, you will have maybe 10 per cent of your holes sampled.

But, if you use the data collected for your borehole geophysical log, you will not only be using data you already have on hand (say goodbye to waiting for lab results) you will also have samples at regular intervals with a high confidence factor.

"Why?" you might ask. Due to nuclear regulations, the device used to measure the geophysical log is calibrated daily. This means that samples are taken every day using calibrated instruments, making it highly accurate. We have seen instances where clients collect samples at every 0.1m, giving our clients an immense amount of accurate data that benefits the entire exploration programme and beyond.

Incorporating density

The density curve is traditionally used to only make lithology picks. But, if we take density measurements from the roof and floor picks, and create a weighted average based on the unit thickness, that value will become the borehole geophysical log unit density.

For example, let us say we are working with a sandstone unit and the same canned data set we cited earlier. With the traditional method, you would use an average sandstone material density of 2.6SG, but with using data from your borehole geophysical log, you would have a more accurate density of 2.49SG (per this example).

 ensity curve of the og Density curve of the E-Log


Now, let us take a look at how these two densities affect how we calculate reserves. Oftentimes, when calculating reserves within a resource model, you either take density samples from a lab that originated from a number of drill holes at one given point in time or you use a blanket density.

But what if we use the more accurate density derived from our borehole geophysical log?

For this example, we will use the same reserve block, with a volume of 18,726,411.21m3.

When using a default density of 2.6SG, and the reserve block contains a volume of 18,726,411.21m3 and a mass of 48,688,669.1.

When using a borehole geophysical log density of 2.49SG, and the reserve block contains a volume of 18,726,411.21 m3 and a mass of 46,628,763.9.

By using the borehole geophysical log density, the calculation of your reserve mass is 4.2 per cent more accurate.

Exploration accuracy = downstream success

While we achieved a 4.2 per cent difference in density with a canned data set, you are likely to see a slightly different result when using your specific data.

Ideally, the difference between using a default density and your borehole geophysical log density should be five per cent or less. If your difference is greater than five per cent your confidence in the model could be uncertain. And to help ensure you are as accurate as possible, you want to maintain a high level of confidence as this difference could be the deciding factor on whether you are within your company's standards for tolerated difference between model tonnes and production tonnes.

If you can better forecast your reserve density, you can make better operational predictions, and more realistic plans for operation to drive downstream process efficiency and effectiveness.

So, why not start fully using your exploration data today?