Hyperspectral core imaging specialist
 Click here to go to the full PDF version of this issue, with any maps, photos or other artwork that appears in
some of the articles.
Terracore’s scanners identify and map mineralogy in drill cores and cuttings — passive, non-destructive spectroscopic methodology
Steve Sutherlin for Petroleum News
Hyperspectral core imaging is a passive and non-destructive spectroscopic method for identifying and mapping mineralogy in drill cores and cuttings, according to Terracore, a company that did not exist eight years ago.
Terracore, of Reno, Nevada, is in the business of gathering imaging data using technology that didn’t exist until fairly recently. It also provides software to analyze the results of that imaging.
Dave Browning is a co-author of the report, “Hyperspectral Core Imaging: Applications in Unraveling Deposit and Reservoir Mineralogy,” by Browning and Paul Linton, Phil Harris, Chris Sherry, which he presented May 31 at the technical breakout session at the state Geological Materials Center in Anchorage. The session focused on the potential for new investigative technologies and machine learning systems to better assist geoscientists and resource companies to meet the challenges of interpreting Alaska geology.
“Terracore is a merger of two different companies, one from South Africa and one from Reno, Nevada,” Browning said. It has offices in those locations.
“Both companies started in 2012 and merged in 2015,” he said, adding that the company has collected more than a million meters of core over the past four years. Most of the clients have been government or industry, particularly mining.
The company can provide the scanning service or clients can buy a system and Terracore will service it.
It offers three sizes of systems; a desktop unit; a mobile unit that sets up in half a day; and a lab instrument.
“With these systems, the sample sits on the table and goes underneath the camera,” he said. “We’re getting an entire core box at one time whatever the width of the sample ... we get through 200 or 300 boxes per day.”
Terracore’s IntelliCore software can be downloaded and used to explore open access data, Browning said, adding, “It’s available to anybody.
“We provide the image data as well as the numerical product so we can actually support the numbers behind these images in a spreadsheet, and that’s where our collaborative process comes in with companies like NER (New England Research Inc.), where we can create downhole plots,” he said. “The key thing here is that this is a visual archive preservation of the core.”
Energy reflectance Hyperspectral imaging measures the reflectance of energy off of the surface of the core, Browning said.
“We start in the visible range and we go into the infrared about 350 nanometers to 1000 nanometers along the EMR (electromagnetic range), and then we also have a short range which goes from 1000 nanometers to about 2.5 microns,” he said. “Each mineral - because it has a unique crystal structure - reflects or absorbs energy in their unique way so therefore we can create mineral maps, and we can also look at feature extraction which is looking at ... individual features.”
“It’s important that we’re using the right wavelength ranges doing mineralogical studies,” he said. “The advantage about the long-wave is it’s dealing with heat reflectance as opposed to light reflectance with the shortwave, so when you have a dark material such as oil and gas rocks we often don’t get a great signature off of the shortwave.”
When changes in the shortwave are very subdued, they get more definition added to them based on some of the long-wave data that is provided, he said.
“In terms of our mineral processing ... this is uninterpreted data, so this is very much similar to what people do with the rock typing we can create through artificial intelligence, create classes based off this spectral data spectral variance we can provide,” he said. “We can also interpret that data and those classes as well. The computer tells us these are very similar; we can go and look and say, okay, based on other data sets and talking to geologists, based on our experience, this is a muscovite or quartz or whatever it might be, and we can actually start to point out that mineralogy.
“Obviously this is very key because in two previous examples we saw today we’re talking about porosity and permeability, so what we try to answer with the mineralogical data is what’s causing this because it all comes back to the rock; it all comes back to the mineralogy,” Browning said. “It’s a really nice context we provide.
“With the rock typing, we can start to take these classes and create different domains ... to do this cluster analysis and get down to a really, really fine scale,” he said. “Our spatial resolution is at a maximum at 1 millimeter, we often operate below that - about a half millimeter, so we can go from there and start to scale-up between different data sets.”
The scanned data can be integrated into other data sets, he said.
“We’ve got spectral gamma ray resistivity, we’ve also got some porosity and permeability ... provided by NER - this was presented by a client of ours, then we have our mineralogy here as well; you can start to see these changes all the way across this log,” he said. “This is just another example of trying to understand sequence stratigraphy, so as we get these different datasets with what’s happening in the mineralogy, we start to see these really fine changes and start to explain the larger picture.
“In conclusion, the core is invaluable. As Kurt (Johnson of the GMC) was saying there’s $35 billion of exploration costs sitting here in this facility; we need to archive that; we need to understand it, and we need to combine these,” he said “We need to start driving exploration and get the most out of that.
“As new technology evolves, the buzzword right now is artificial intelligence, deep learning, machine learning; let’s collect these data sets and as those things evolve, we can continue to interpret,” he said. “We have that data set, it doesn’t decay like some of the rocks might, and we can continually reprocess that data to ask questions as we’re moving forward and questions come up.”
| 