1887
Volume 43, Issue 2
  • ISSN: 0263-5046
  • E-ISSN: 1365-2397

Abstract

Abstract

The effective exploration and development of subsurface resources require detailed, continuous rock characterisation to understand reservoir heterogeneity and optimise resource recovery. Traditional core analysis techniques often rely on destructive, fragmented testing, which leaves significant gaps in the spatial resolution of rock properties and can lead to biased interpretations of subsurface conditions. This article introduces CoreDNA™, an innovative non-destructive core digitalisation platform that transforms conventional core analysis by creating high-resolution, multidisciplinary digital logs along the entire length of a core.

By integrating advanced imaging, geochemical, petrophysical, and geomechanical data, CoreDNA™ generates a ‘digital twin’ of the core. This approach bridges the gap between broadscale wireline logging and detailed core subsample analyses, enabling precise lithofacies identification, optimised subsample selection, and robust data upscaling. A case study from Well 15/12-20 S in the Norwegian Central North Sea demonstrates how the solution accelerates rock property characterisation, identifies reservoir heterogeneity, and enhances the resolution and reliability of reservoir quality assessments.

This cutting-edge workflow reduces uncertainties in reservoir modelling by providing a scalable, objective, and cost-effective methodology for subsurface evaluation. By complementing existing analytical techniques, the tool establishes a new paradigm in core analysis, paving the way for safer and more efficient resource exploration.

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2025-02-01
2026-02-15
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References

  1. Germay, C., Lhomme, T. and Bisset, P. [2021]. Combining high-resolution core data with unsupervised machine learning schemes for the identification of rock types and the prediction of reservoir quality. The 34th International Symposium of the Society of Core Analysts.
    [Google Scholar]
  2. Germay, C., Lhomme, T. and Perneder, L. [2023]. Core digitalization programme and Artificial intelligence for the automatic recognition of sedimentological features. The 36th International Symposium of the Society of Core Analysts. Abu Dhabi.
    [Google Scholar]
  3. Germay, C., Lhomme, T. and Perneder, L. [2023]. High-resolution core data and machine learning schemes applied to rock facies classification. (A. Neal, M. Ashton, L. Williams, S. Dee, T. Dodd, & J. Marshall, Eds.) Special Publications of the London Geological Society, 527(Core Values: the Role of Core in Twenty-first Century Reservoir Characterization), 121–135. doi:https://doi.org/10.1144/SP527–2021-19.
    [Google Scholar]
  4. Germay, C., Lhomme, T., Perneder, L. and Cummings, J. [2022]. Combining high-resolution core data and machine learning schemes to develop sustainable core analysis practices. The 35th International Symposium of the Society of Core Analysts. Austin.
    [Google Scholar]
  5. McCormick, C. A., Corlett, H., Stacey, J., Hollis, C., Feng, J., Rivard, B. and Omma, J. [2021]. Shortwave infrared hyperspectral imaging as a novel method to elucidate multi-phase dolomitization, recrystallization, and cementation in carbonate sedimentary rocks. Sci Rep, 11(21732). doi:https://doi.org/10.1038/s41598-021-01118-4.
    [Google Scholar]
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  • Article Type: Research Article
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