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Multi-Sensor Core Logging – The Future of Core Analysis

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Course Credit: 0.15 CEU, 1.5 PDH

With few exceptions, the data from core analysis are no more than a “snapshot,” with samples selected for analysis based on arbitrary criteria or limited knowledge. In addition, those parts of a core that include, for example, vugs and fractures, as well as low-permeability regions, are typically excluded from the analysis.

In this webinar we examine the concept of “smart core analysis”, where automated high-resolution core data logging is combined with data analytics and machine learning. These methods, originally developed external to the upstream core analysis (oil and gas) industries, are rapid, non-destructive, and non-invasive.

Technologies now available ‐ under the umbrella term automated multi‐sensor core logging – mean that our ability to characterise core is now orders of magnitude more effective and by using methods of modern and classical data science the gain of information is significantly richer. Moreover, these methods can be applied to core pre-existing in repositories worldwide, many of which have embraced these technologies and are in the process of creating “digital twins” of the core in storage. This represents a revolution in our ability to understand the sub‐surface to the wider benefit of industry and also society out with hydrocarbon extraction.

These technologies bring core analysis into the 21st century. High resolution data is rapidly acquired and combined to create and define trans-disciplinary distinct facies types or mineral assemblages enabling highly targeted sampling for all subsequent studies. Many specialised studies are performed on cores and historically the sampling strategy has frequently introduced bias due to lack of relevant information to facilitate informed sampling. Sampling was to a large extent based upon visual cues, core lithological logs (subjective) and well logs – the latter generally have insufficient resolution to be fit for purpose. Multi-sensor core logging acquires essentially continuous data. Taking two examples;

Rock strength – a continuous log of rock strength acquired by scratch testing prior to core sampling for rock mechanics analysis allows the sampling to be highly targeted – picking out weak zones for example. Geomechanical modelling is critical for wellbore stability and completion design and requires input from rock mechanics. Biased sampling based upon qualitative methods can profoundly negatively impact well performance.
Mineralogy – typically sampling for petrography and mineralogy is based upon visual clues. Then a limited number of higher cost analyses are conducted. This sampling methodology may completely fail to effectively characterise the core. Continuous logs of mineralogy and elemental composition using methods such X-Ray fluorescence (XRF), hyper-spectral imaging HIS) and Laser‐induced breakdown spectroscopy (LIBS) in themselves create detailed mineral maps but also may require calibration from X-Ray diffraction analysis. Precise sampling is facilitated by these methods. Mineralogy of core has a wide range of important applications and for mining derived core is the key data of interest
Aside from future studies for oil and gas, these technologies can be applied across a range of disciplines where core is acquired. The energy transition towards Net Zero has created a vastly expanding demands for metals including rare earth elements, cobalt, lithium and many more – automated mineralogical core logging is playing a key role in identifying these elements and the mining industry acquires vastly more core annually than for oil and gas. Cores are also acquired for marine geoscience, geotechnical, geochronology, climate change studies and more. Welcome to the future of core analysis.

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Course Chapters

  • 1Multi-Sensor Core Logging – The Future of Core Analysis - Chapter 1
    Media Type: Video

Credits

Earn credits by completing this course0.15 CEU credit1.5 PDH credits

Speakers

Denis Klemin
Stefano Pruno - ModeratorRegional Technical Advisor for Core Analysis, Stratum ReservoirStefano Pruno is Stratum Reservoir Regional
Technical Advisor for Core Analysis. He has a
university background in Geology (MSc), working
in the core analysis domain for approx. 25 years,
with Stratum Reservoir for the last 15 years.
Main fields of expertise are core analysis data
evaluation, interpretation in addition to core
analysis program design, planning and data
management.