The use of a multi-sensor core scanner workflow as the backbone of a digital core repository

Geological samples are the ground truth for all geoscience applications. These samples are mostly in the form of cores but can also be chips/cuttings, core plugs or hand specimens. The value of an individual core sample is often difficult to quantify. The cost of obtaining a core sample could be tens to millions of dollars (hand specimen collected in the field to deepwater oil and gas core) and the uses for this one sample are various (educational, scientific sampling, geochronological, resource extraction, engineering, etc.). The value a person/department/institution/company places on a core sample is a function of its application and intended use. As a result, determining the monetary value of a core is complicated (Williams 2021). But despite this, the perceived importance and the notion that geological samples should be archived for future reference is ubiquitously agreed. This agreement is recognizable through the establishment of national (e.g. the British Geological Survey National Geological Repository (NGR), UK or United States Geological Survey Core Research Centre, USA) and commercial (e.g. the core storage services that Stratum Reservoir AS offer in Norway (Stratum Reservoir 2019) or Solintec (Solintec 2022) offer in Brazil) geological repositories. Ultimately, geological samples are a physical record of the subsurface and therefore the source data for the ground-truthing of a resource company's or nation's subsurface wealth and geo-heritage. Therefore, geological samples are, arguably, priceless.

The role of the core repository is to preserve geological samples and make its collection accessible to scientific, industrial and educational communities. However, a core repository is a physical asset where the information held within is only accessible with prior planning and agreement. Herein lies the challenge and drives the requirement for core digitization. A digital dataset can transform a physical collection into an accessible and interrogatable database allowing the core repository to be engaged with by the communities it serves more easily. Large-scale core digitization campaigns have already been established using core scanning technologies such as the UK Geoenergy Observatories core scanning facility based at the British Geological Survey (UK Geoenergy Observatories 2023), National Virtual Core Library (NVCL) in Australia (Huntington 2016; AUSCOPE 2022) or the more recent National Digital Drill Core Archive of Finland (NDCAF) (Geological Survey of Finland 2021).

This study adds to the concept of core digitization by presenting a workflow that uses multi-sensor core scanners to create a digital record that can be accessed on demand using cloud technologies and analysed to discover new potential from archived cores. To illustrate how the workflow operates, a core digitization case study using Geotek's Multi-Sensor Core Logger (MSCL), hyperspectral and X-ray imaging technology is presented from archived core samples from Well 211-23-8Z(8S1) located within the Osprey Field, UK Continental Shelf (UKCS).

Core scanners have long been recognized (e.g. Schultheiss and McPhail 1989; Schultheiss and Weaver 1992; Croudace et al. 2006; Rothwell and Rack 2006; Tappert et al. 2011; Germay et al. 2014; Martini et al. 2017) as a valuable method for:

1. visualizing a quantified geological stratigraphy;

2. providing a depth co-registered multi-parameter dataset for detailed analysis;

3. core to wireline log correlation;

4. stratigraphic correlation across multiple locations; and

5. intelligent sub-sample selection.

The utilization of multiple sensor technologies on core scanners maximizes data recovery from every geological sample and has been shown to be valuable in most forms of geoscience application including:

1. geotechnics (e.g. Campbell et al. 2008; Shreeve et al. 2017);

2. oil and gas (e.g. Germay et al. 2014; Fellgett et al. 2019; Hunt et al. 2020);

3. mineral exploration (e.g. Vatandoost et al. 2008; Linton et al. 2013; Ross et al. 2013; Sjöqvist et al. 2015);

4. geometallurgy (e.g. Dominy et al. 2018);

5. marine geology (e.g. Rothwell and Rack 2006; Croudace and Rothwell 2015);

6. palaeoclimate (e.g. Francus et al. 2009; Croudace and Rothwell 2015); and

7. environmental impact (e.g. Schillereff et al. 2016; Smith et al. 2020).

A core repository can hold hundreds of kilometres of geological samples, and the digitization of these samples is a logistical and technical challenge. Digitization of core repositories is not a new concept, with institutions already deploying their virtual geological databases such as the NVCL (AUSCOPE 2022) or the UK's National Geoscience Data Centre (NGDC) (British Geological Survey 2022; Damaschke et al. 2023). Multi-sensor core scanning technology complements these existing databases and where digitization has not commenced offers an automated methodology to start creating the digital record.

The advantage of multiple datasets is a wide catalogue of measurements and images of the geological stratigraphy but at the cost of data storage and complexity in data delivery requirements. A multi-sensor core digitization programme therefore needs to be carefully planned to ensure a fit-for-purpose digital record is acquired that meets the demand from a core repository user group.

A workflow is presented here that leads to a core repository through the stages of management, analysis, archiving, visualizing and discovering, ultimately ending at the achievement of a digital core record or repository (Fig. 1).

The biggest challenge to a core digitization programme using core scanning technology is defining the scope of work: how many data should be acquired per metre of core? The simplest answer is all data at the highest resolution. Whilst the technical argument for this approach is strong – maximize data recovered from every sample – practically it is almost unattainable. This is because of logistical challenges which are:

1. time: high-resolution data always take longer to acquire per point;

2. data size: high-resolution data are always more expensive; and

3. core quality: not all core samples are suitable for all analyses; for example, heavily fractured or friable cores are not suitable for physical property measurements but can be imaged.

Core quality can be difficult to assess ahead of starting scanning as it is often not known. As a result, decisions about where to take a measurement might have to be made in real-time based on simple criteria that are related to the physics of the measurements themselves and core quality type. Table 2 establishes a core quality index for the categorization of cores and references where data quality will be good or poor because of core quality. Simple core quality classifications can be used in this way to help ensure that only good or useable data are acquired and ultimately stored. However, if pre-existing core images or information from the core exist, then automated approaches to core quality can be conducted (e.g. Fellgett et al. 2023 or Harraden et al. 2019). Making good decisions about which cores are scanned with which technologies saves (1) time by not scanning a core that will produce erroneous data and (2) data storage costs by not archiving erroneous data.

The objectives of core digitization programmes are not always the same and therefore not all sensors offered by multi-sensor core scanners are suitable for all workflows. National and commercial repositories will have different mandates for the preservation and data access rights of the core and resultant data stored. Consequently, the digitization objectives will be different between repositories. Rationalization of which sensors to deploy is therefore achieved by considering the objective alongside the application of the parameters offered by the sensors. However, whilst a restricted scope of work of any multi-sensor core scanner analysis program does improve the logistical challenges of data storage and acquisition, it has the negative impact of limiting the ability to discover new trends and patterns in data not previously considered. The core digitization workflow in this paper encourages the selection of groups of parameters based on their application, for example physical properties, geochemistry or imaging technology, rather than the individual merits of one or other datasets from isolated sensor technology.

In addition to sensor selection, a scope of work needs to consider the number of measurements per metre or resolution required. The data resolution of a multi-sensor core digitization scope of work is a balance between maximizing the data acquired and the logistical challenges of acquiring the data. This balance of competing priorities can be rationalized into two levels of scanning: exploratory level and detailed level (Fig. 2).

The sample selection for each level can be predefined ahead of the work starting if there is a good understanding of which cores are of good quality or have the highest scientific value. Cores that are good quality and of high scientific value would for example be candidates for detailed level scanning, for example, a key borehole where unique geological stratigraphy was sampled and this stratigraphy only exists within this one borehole. Cores with poorer core quality that record previously sampled stratigraphy would be selected for exploratory level only.

Optimized data acquisition using multi-sensor core scanners turns an underutilized resource of information – geological samples – into accessible digital data. The core scanners provide a stable and consistent measurement platform that enables core digitization projects to be scaled and quality controlled.

Core scanners are portable instruments and can be installed into a variety of environments including warehouses and fixed or containerized laboratories enabling the scanning to occur within or close to the storage location of the core. The workflow of the digitization of an individual sample using multi-sensor core scanners is project-specific and dependent on the sensor groups selected. An example workflow used by the UKGEOS project shows how multiple multi-sensor core scanners are deployed to extract the maximum data from the core whilst maintaining good curatorial and quality control procedures (Damaschke et al. 2023).

In all methods, success in the upscaling of any project to the size of a core repository is in reducing the handling of samples as much as reasonably practicable when transferring core between instruments or around the laboratory area. Equally, data transfer and processing are often a large hurdle and burden, as even with good rationalization of the scope of work multi-sensor core scanners will produce thousands to hundreds of thousands of data points per day translating to hundreds of megabytes to hundreds of gigabytes. These data are the fuel for future analysis and the ultimate product of the core repository. Therefore, attention to core handling, data acquisition quality control and data preparation for archiving are fundamental processes of the analyse part of the workflow.

Geological data should be considered as an asset (Yacopetti and Mundell 2010) of a company, country or academic institution. These geological data are acquired at great expense and are foundations upon which business plans or scientific findings are based and should therefore be carefully organized (Hawtin 2013). Digital data acquired from multi-sensor core scanning will quickly become an unmanaged asset (Redman 2008) without a geoscientific focused database (Yacopetti and Mundell 2010) and strong data visualization to allow users to navigate through the data. Data navigation is particularly important as Redman (2008) estimates that users spend on average 30% of their time trying to find the data they are looking for, and half the time they are unsuccessful.

A physical repository has a limited bandwidth to provide access to cores/data. There is a physical restriction to the number of users per day and the amount of core that can be viewed or extracted from the repository at any given time. Whereas a digital repository can deliver more data to more users faster and as a result enhance access of the geological data asset.

Cloud-based services offer technology that can help core repositories deliver data to their users in a practical way. Cloud storage and computing delivers on-demand applications and computing resources to users without the costs of infrastructure management (Oikonomou et al. 2021). In addition, cloud storage is scalable (Burrell et al. 2019) to allow growth as datasets become bigger and more data are added. Furthermore, the advances of cloud computing power has offered new opportunities to visualize large datasets (Yang et al. 2013) and gives users the ability to use their own open-source code to process their data without the need to download (Oikonomou et al. 2021).

Core repositories will have many levels of data confidentiality from restricted data to open access. Security of data is therefore essential, and as a result the International Organization for Standardization (ISO) are publishing new codes of practice for cloud services to measure organizations and software products against (ISO/IEC 27017:2015).

There are a range of commercial cloud-based geoscientific software platforms available including Imago (http://www.imago.live/), Aeon's Public Data Repository (http://www.aeon-gs.com/pdr) or Coreshed (http://www.coreshed.com/). In this case study, multi-sensor core scanner data are uploaded to the Geotek Limited cloud-based data storage and visualization software Atlas. Atlas allows users to create graphical logs to visualize the multi-modal image data. Simultaneously, users can now create their own data visualizations that are specific to their project or application (Fig. 3)

Data visualization tools do not replace the need for a physical library but they do augment the core repository user experience by showcasing digital data alongside the physical sample and enable the user to more precisely define the samples they need to view.

Multi-sensor core scanners deliver geochemical, physical property, mineralogical and image datasets all depth co-registered with each other. These multi-variable data are perfectly suited to data science techniques to unlock new understandings of the subsurface that hitherto would have been enormously challenging when using subjectively derived datasets such as geological descriptions or historical hand-derived data. There are numerous examples of multi-sensor data being used in machine learning (Cate et al. 2017; Hill and Uvarova 2018), quantification models using hyperspectral imaging (Hunt et al. 2020) or rock typing using wireline logs (Hall 2016).

In all cases the authors take advantage of a broader characterization of the rock mass from the multi-sensor core scanners and use a range of parameters to help improve the outcome of their models. Ultimately, using multi-sensor data in this way enables repository end users to extract more scientific value from the core material without having to conduct any additional destructive tests and thus preserving the geological samples for the future. If destructive tests are required then these multi-sensor data now allow for more intelligent future subsampling of the core and the prevention of over-sampling.

To illustrate the use of the core digitization workflow, multi-sensor core scanning was conducted using Geotek's MSCL, a SpecCam IV hyperspectral camera and X-ray imaging technology (Table 3) on 100 ft of slabbed 4 in. rock core drilled in 1985 from Well 211-23-8Z(8S1) from the Osprey Field, Dunlin Cluster, UK North Sea (Ball and Gluyas 2020). This new digital stratigraphy was uploaded and visualized within a cloud-based database called Atlas and then analysed using an unsupervised multi-variant classification model to generate a new wholly data-driven lithological interpretation.

The Dunlin Cluster is located 160 km NE of the Shetland islands and was producing for 42 years until 2015 when the decision was made by the then owner Fairfield Energy to decommission (Ball and Gluyas 2020). In 2019 core samples from the field were acquired by Geotek Limited from the Fairfield Energy archive.

Well 211-23-8Z(8S1) samples primarily the Brent Group formations, which comprise a mixture of shales, siltstones and sandstones of the Upper, Mid and Lower Ness formations. These sediments were deposited from a prograding Mid Jurassic delta within a deepening basin nestled between the East Shetland Basin and the Viking Graben (Ball and Gluyas 2020). The Dunlin reservoirs were split into two (Tarbert/Upper Ness and Lower Ness) by the shales and siltstones of the Mid Ness Formation, which acted as an impermeable barrier between the two reservoirs (Braithwaite et al. 1989). The core interval of Well 211-23-8Z(8S1) samples the Upper, Mid and Lower Ness formations of the Brent Group and a small component of the overlying Heather Formation. Whilst all the available core was digitized to deliver a modern virtual record of this archive core, this case study will focus mostly on the Ness Formation of the Brent Group and particularly the regionally significant Mid Ness Formation. The new multi-sensor core scanner data are used to discover new observations from a sequence of sedimentary rocks that are already characterized (original core descriptions, laboratory data and reports available from the UK National Data Repository provided by the Oil and Gas Authority and/or other third parties) and understood (Mitchener et al. 1992).

Mid Jurassic petroleum reservoirs are still commercially important with active producing fields (information provided commercially by the UK Oil and Gas Authority and/or third parties) such as the Statfjord Field (Gibbons et al. 2003), Ninian Field (Van Vessem and Gan 1992) and North Cormorant Field (Bater 2003). In addition, decommissioned oil and gas fields are also considerations for future carbon capture storage (Ajayi et al. 2019). As a result, archived cores sampling the Brent Group can still be used to inform current commercial projects or be used to investigate future geoscience applications.

The objective of the digitization programme was to extract the rock composition and physical property data to produce a data-driven lithological interpretation that aligns and adds to the existing geological understanding of Brent Group sediments and specifically the Mid Ness Formation. To achieve this goal, Well 211-23-8Z(8S1) samples underwent multi-sensor core scanning using the equipment and sensor groups outlined in Tables 2 and 3 to acquire physical properties and geochemistry with imaging technology of hyperspectral infrared spectrometry and X-ray radiography.

The core scanning scope of work was rationalized into exploratory and detailed levels. Exploratory level included complete X-ray fluorescence (XRF) scanning with natural gamma activity, point magnetic susceptibility, electrical resistivity and core imaging using visible light and X-ray radiography of the cored sequence. These sensors were selected based on the core quality available and their application to rock characterization to generate a geochemical stratigraphy. In total over 350 000 data points (0.4 GB) and 140 images (23.9 GB) representing the physical and geochemical properties of the core were acquired during exploratory level scanning.

Detailed level scanning was then conducted within the Mid Ness Formation between 9120 ft and 9162 ft using high-resolution (1 cm per point) physical property measurements (density and P-wave velocity) with high-resolution (0.5 × 0.5 mm pixel size) SpecCam short-wave infrared red (SWIR) hyperspectral infrared imaging. Detailed level scanning added over 675 000 data points (0.1 GB) and 216 images of various interpreted minerals (12.0 GB). The number of data points is so high because each row of pixels within a hyperspectral image, for every interpreted parameter, was averaged into a single point. This operation enables a comparison between the hyperspectral data and the MSCL results with depth. These additional data from the detailed level scanning were used to characterize the changes in clay and sand, specifically the clay minerals, as well as to better understand the physical properties of the Fe-rich beds identified from the XRF scans. In this case the detailed level scanning scope was reactive to data identified from the exploratory scans and ensured only additional data that would add value to the study were acquired.

The full exploratory and detailed level data are presented in Figure 4 and as an expanded detailed level only section within Figure 5 as graphical logs. In these graphical logs the laboratory data are overlain against the new MSCL data. There is good agreement between the laboratory density and MSCL density within the Mid Ness Formation. However, in the Upper Ness Formation the density is lower compared to the laboratory results. This could be due to saturation differences between core at the time of scanning versus the time of the laboratory measurements. These saturation differences would be most notable within the porous sand facies of the Upper Ness Formation compared to the clay-rich Mid Ness Formation.

The downcore multi-parametric profiles showcase the variation of physical properties and geochemical parameters within the Brent Group. The exploratory level XRF scanning shows that the sandstones of the Heather and Upper Ness formations are rich in K compared to the siltstone and sandstone within the Mid and Lower Ness formations (Fig. 4). Conversely the Mid and Lower Ness Formation sandstones have higher Al and Ti contents compared to the Upper Ness Formation. Furthermore, the geochemical stratigraphy highlights that the clay-rich horizons correlate well with elevated magnetic susceptibility, natural gamma and Fe, S, Ti and Al with corresponding decreases in Si (Fig. 4). These XRF data also identified Fe-rich cemented horizons within the Mid Ness Formation that are not described within the historical data (Figs 5 & 6). The point magnetic susceptibility profile closely follows the Ti and Al concentration from the XRF with the lowest values recorded within the top of the Lower Ness Formation and top of the Upper Ness Formation (Fig. 4). This relationship closely relates to lab permeability measurements where the highest permeability readings are where magnetic susceptibility, Ti, Al, Fe and S are lowest.

The core was imaged using three different wavelengths of light (visible, SWIR and X-ray). Each wavelength of light interacts with the core differently to deliver a different perspective. Exploratory level scanning utilized high-resolution (200 pixels per cm) visible light images to deliver a visual record of the core. These images allowed for the recognition of sub-millimetre sedimentological features as well as documenting the current core quality (Fig. 7). In addition, we have used an X-ray radiograph technique to provide rapid (3–5 mins per m) and low data volume (c. 200 MB per m) 2D X-ray visualization of the core (Fig. 7b). The X-ray radiographs show sedimentary structures that are not observed within the visual or infrared images and, in addition, highlight where small-scale fracturing within the clay-rich units has occurred, which has resulted in poor data recovery from sensors susceptible to fracturing such as P-wave velocity and attenuated gamma density.

Detailed level scanning results are graphically displayed in Figure 5. The SWIR hyperspectral images provide a unique visualization of the mineral abundance and lithology (Fig. 3). The onset of clay minerals from 9125 ft is a notable signal of the start of the Mid Ness Formation shales. The clay is mostly composed of illite, kaolinite and mica that together make up 30–40% of the volume of the rock mass. The density within the Mid Ness Formation is c. 2.5 g cm⁻³ but the high-resolution MSCL data show that the formation has lower density sand beds intercalated within the predominantly clay-rich formation. These beds have a rise in porosity of between 5 and 10% and are characterized by elevated Si and reductions in Fe, S, clay minerals and magnetic susceptibility. The hyperspectral images show that the clay within the Mid Ness Formation is not equally distributed with the highest concentrations of minerals bound within sub-parallel to inclined thin to thick laminations. Furthermore, where the visual images of the core look predominantly like sand, the hyperspectral, physical property and geochemistry data indicate that these sandstone beds remain clay rich.

Lastly, the detailed level scanning was able to increase the data observations from the Fe-rich cement within the Mid Ness Formation (Fig. 6). These cement beds are c. 10–30 cm thick and have a very high density of c. 3.45 g cm⁻³ with a high P-wave velocity of c. 4500 m s⁻¹ meaning that porosity is almost zero within these beds.

Ahead of any analysis of multi-sensor core scanner data it is important to consider the core quality, acquisition settings and specifications of the sensors deployed on multi-sensor core scanners. Particular attention to resolution (pixel size), area of illumination (size of measurement area), dwell time (time spent per measurement point) and finally mode of measurement (surface measurement, transmission or volume measurement) should be noted as any differences between datasets might not always correlate to changes in geological strata.

In this study the data acquired are representative of the material measured. The multi-sensor core scanner data separates the two reservoirs of the Brent Group not only by the onset of the clay-rich Mid Ness Formation but also by the composition of the sandstones. The Upper Ness Formation and Heather Formation sandstones are K-rich (10–15%) compared to the sandstones of the Lower Ness Formation. The elevated K suggests a depositional environment and sediment source rich in detrital K-feldspar (Glasmann 1992). The Upper Ness Formation is interpretated as upper delta plain deposits with clay-rich horizons formed from lagoonal shoals (Richards 1986; Baumann and O'Cathain 1991) and would explain a detrital input of K-feldspar. However, in the Lower Ness Formation the K content is 5–8%, suggesting that less detrital K-feldspar was supplied into the system. This is consistent with the Lower Ness Formation being deposited within a back barrier environment (Baumann and O'Cathain 1991) that had a more restricted detrital K-feldspar input.

The correlation between clay-rich sediment containing illite, low permeability and magnetic susceptibility is seen in other North Sea reservoirs (Potter et al. 2004). However, unlike other case studies, we have identified a significant proportion of kaolinite and Fe, particularly within the Mid Ness Formation. It is therefore unlikely that only illite is responsible for the magnetic susceptibility response and more likely that the clay-rich facies and formations are also associated with other Fe-rich magnetic minerals. The Mid Ness Formation was deposited within a restricted lagoonal setting (Richards 1986; Baumann and O'Cathain 1991). The elevated S content as well as the Fe-rich cemented beds would fit with this interpretation due to the precipitation of diagenetic minerals such as Fe-sulfides, Fe-carbonates, etc. However, alignment of clay mineral distribution with laminae and the identification of high porosity sand-rich beds within the Mid Ness Formation indicates that the lagoon was periodically breached, creating an influx of sand-rich material.

Prior to multi-sensor core scanning the only measurements available were from an existing core plug sample and sedimentological description. In this well a core plug was drilled every 1 ft resulting in three discrete data points per 3 ft core section. The MSCL downcore data were acquired at between 1 cm and 10 cm measurement spacings per data point resulting in 3–30 times more data compared to the original work. The increased data density has enabled a more detailed characterization of the sedimentology for this well supporting and enhancing the understanding of the depositional environment, concluding that the Mid Ness Formation is far more heterogeneous than previous documented.

In order to realize a data-driven lithological interpretation of these multi-sensor core scanner data we build upon the work published by Shreeve et al. (2021) who used Data Mosaic (Hill et al. 2015) to create unsupervised hierarchical domains from boundary and classification information using multiple variables from the data. The input variables were chosen based on four criteria:

1. Geology: selection of parameters that can separate clay-rich, sand-rich and carbonate-rich rock types.

2. Application: the petroleum focus of the study means that parameters should be chosen that help in the identification of lithologies that affect porosity or permeability.

3. Core quality: parameters chosen that have good quality results from the quality of core scanned.

4. Reasoning: a data review of the stratigraphy and core scanning data show elements, minerals or physical properties that separate lithologies and show heterogeneity. Parameter selection was therefore guided by reasoned observation.

1. sandstone;

2. shale and interlayered sandstone;

3. siltstone;

4. Fe-rich beds.

Data Mosaic is an unsupervised classification, and therefore selection of the input parameters can affect the output and can change the interpretation (Shreeve et al. 2021). However, the results and application of the method show that there are a range of opportunities available to users with access to these multi-sensor core scanner data. Multi-sensor core scanner data and data science techniques do not replace the original data but rather they augment this existing dataset and build a consistent depth and data framework from which analytical studies can be performed.

A core repository holds the physical record of a nation's or company's subsurface wealth and history by storing and preserving geological samples. Multi-sensor core scanners are core analysis equipment that use a range of petrophysical, geochemical and imaging technology to create a digital record of geological samples. This has the potential to significantly improve how core repositories provide knowledge to their scientific or industrial users.

This study presents a workflow to guide a repository through the process of turning physical core samples into digital data using multi-sensor core scanners. The workflow comprises four steps: (1) manage and rationalize; (2) analyse; (3) archive and visualize; and (4) discover.

In order to illustrate the workflow, 100 ft of archived core from Well 211-23-8Z from the Osprey Field in Block 211/23 of the UKCS was digitized using Geotek's MSCL and imaging technology. The sensors utilized and the amount of data acquired were rationalized using two acquisition scopes of work: exploratory and detailed level. The exploratory level scans used geochemistry and physical properties with visible and X-ray images to create a new digital stratigraphy that is stored in a cloud-based database and visualization software. Exploratory scanning identified boundaries between the Upper, Mid and Lower Ness formations of the Brent Group and showed that the open delta plain sediments of the Upper Ness and Heather formations are rich in K from detrital K-feldspar when compared to the Lower Ness Formation. Furthermore, the data identify the heterogeneous Mid Ness Formation, which is a regional reservoir barrier separating the Upper and Lower Jurassic Brent reservoirs. Detailed level analysis using hyperspectral infrared imaging spectroscopy and high-resolution density and P-wave velocity scanning were then used to help quantify this heterogeneity. These data provided the foundation for a multivariate data-driven lithological interpretation using an unsupervised machine learning model, Data Mosaic. The output of this model reclassified much of the originally interpreted sandstone of the Mid Ness Formation into under-represented shale/sandstone, which suggests that the lagoon depositional setting of the Mid Ness Formation was occasionally breached allowing for the variation of sand, silt and clay laminae and bedding within the Mid Ness Formation. Furthermore, the data and resultant model identified a new Fe-rich rock type that had never previously been described.

The data acquired from multi-sensor core scanners provide a unique visualization of core and geological samples. The datasets are depth co-registered and ready for new data science techniques to deliver new insights into the geological stratigraphy. The workflow establishes a framework upon which a core repository can turn its physical samples into a digital archive.

The re-use of legacy core will be important as geological applications of the subsurface change from, for example, extraction of oil and gas to injection of carbon dioxide for carbon capture storage, or the exploration of battery metals from historical mine sites. Archived core and the data that are trapped within has never been more valuable, and multi-sensor core scanners combined with geoscientific information systems that database and visualize help to realize that value.

I would like to thank my colleagues Briony Shreeve, Georgia Poulton and Melanie Holland at Geotek Limited who have worked with these data over the last couple of years. In addition, I would like thank Fairfield Energy who kindly donated the core used in this study to Geotek Limited and provided insight into what digital data were available for this well.

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

JWS: writing – original draft (lead), writing – review & editing (lead).

This research received no specific grant from any funding agency in the public, commercial or not-for-profit sectors.

Data used in this study are available on the UK National Data Repository, which contains information provided by the Oil and Gas Authority and/or other third parties. Multi-Sensor Core Scanner and image data from Well 211/23-8Z were acquired by Geotek Limited and are available upon request.