1887
Volume 30, Issue 4
  • ISSN: 1354-0793
  • E-ISSN:
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Abstract

This contribution is an introduction to the thematic collection ‘Digitally enabled geoscience workflows: Unlocking the power of our data’. The goal of the collection is to show how advances in data-science are transforming the process of scientific research and fueling a new generation of energy geoscience workflows. These workflows are providing game-changing advances in terms of time saving on complex tasks, improved consistency and repeatability of interpretation, and utilization of scarce experienced geoscientists. Eight articles have been accepted for publication as part of this thematic collection, five in and three in . We provide a short summary of each of these contributions and hope that this collection will provide inspiration and examples of the breadth of workflows that can be transformed by embracing the coming wave of digital technologies.

This thematic collection resulted from an open call for papers on the theme of ‘Digitally enabled geoscience workflows: Unlocking the power of our data’. Eight contributions have been accepted for publication, five in and three in . Although the energy geoscience industry typically employs statistical workflows that are highly data intensive, it has been relatively slow to adopt modern data-science technologies. This is a result of historical reliance on established methods, the cost and complexity of adopting new technologies, and cultural and organizational challenges. However, with improved computing power and growing interest in data sciences, this is now changing rapidly, with the development and application of data-driven workflows an active area of research in energy geoscience. It is expected that the publication of research contributions in this area will continue to accelerate, with this collection providing a useful summary of the current key emerging themes.

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2024-11-15
2025-12-05
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References

  1. Fathi, E., Takribi-Borujeni, A., Belyadi, F. and Faiq Adenan, M. 2024. Simultaneous well spacing and completion optimization using an automated machine learning approach. A case study of the Marcellus Shale reservoir, northeastern United States. Petroleum Geoscience, 30, petgeo2023-077, https://doi.org/10.1144/petgeo2023-077
    [Google Scholar]
  2. Osah, U. and Howell, J. 2023. Predicting oil field performance using machine learning programming: a comparative study from the UK continental shelf. Petroleum Geoscience, 29, petgeo2022-071, https://doi.org/10.1144/petgeo2022-071
    [Google Scholar]
  3. Pantaleo, G., Molossi, A. and Pipan, M. 2024. Estimation of CO2 saturation maps from synthetic seismic data using a deep-learning method with a multi-scale approach. Geoenergy, 2, https://doi.org/10.1144/geoenergy2023-057
    [Google Scholar]
  4. Sahu, A.K. and Roy, A. 2023. Characterizing fractured reservoirs by integrating outcrop analogue studies with flow simulations. Petroleum Geoscience, 29, petgeo2023-032, https://doi.org/10.1144/petgeo2023-032
    [Google Scholar]
  5. Sahu, A.K. and Roy, A. 2024. Predicting fluid flow in reservoirs: analysis of fracture clustering in outcrop analogues. Petroleum Geoscience, 30, petgeo2023-091, https://doi.org/10.1144/petgeo2023-091
    [Google Scholar]
  6. Sahu, P., Gonzalez, A., Heidari, Z. and Lopez, O. 2024. Propagating image-based rock classes from cored to non-cored depth intervals using supervised machine learning. Petroleum Geoscience, 30, petgeo2023-147, https://doi.org/10.1144/petgeo2023-147
    [Google Scholar]
  7. Siler, D.L. 2023. Structural discontinuities and their control on hydrothermal systems in the Great Basin, USA. Geoenergy, 1, geoenergy2023-009, https://doi.org/10.1144/geoenergy2023-009
    [Google Scholar]
  8. Stewart, S.A. 2024. Mapping hydrodynamic structure with sparse or no well data. Geoenergy, 2, geoenergy2023-028, https://doi.org/10.1144/geoenergy2023-028
    [Google Scholar]
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  • Article Type: Introduction

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