Benefits of using dense OBN for exploration: an example from Utsira using AI and machine learning

Sindre Jansen; Adriana Citlali Ramirez; David Went; Bezhad Alaei

doi:10.3997/1365-2397.fb2021074

ISSN: 0263-5046
E-ISSN: 1365-2397

Benefits of using dense OBN for exploration: an example from Utsira using AI and machine learning
Authors Sindre Jansen¹, Adriana Citlali Ramirez¹, David Went¹, Bezhad Alaei²
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Affiliations: ¹ TGS ² ESA
Source: First Break, Volume 39, Issue 10, Oct 2021, p. 45 - 52
DOI: https://doi.org/10.3997/1365-2397.fb2021074
- Published online: 01 Oct 2021

Abstract

The first example of artificial intelligence (AI) geological interpretation on a large scale, densely sampled ocean bottom node (OBN) exploration dataset, Utsira OBN, is presented here. This entirely new suite of derivative seismic products provides enhanced exploration insights through AI, especially in areas where infrastructure led exploration (ILX) is drawing increased focus from the E&P industry. This work is complemented with a velocity model built with the latest processing technology: ultra-long offset and reflection FWI combined with tomography and anisotropy updates. The toolboxes, processing versus AI/ML, do not exclude or replace each other; a combined analysis can extract added value and reduce uncertainties. Such analysis is presented here.

EAGE Publications BV

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2021-10-01

2024-04-25

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References

Davies, J., Stewart, A., Cartwright, A., Lappin, M., Johnston, R., Fraser, I. and Brown, R. [2004]. “3D Seismic Technology: Are We Realizing Its Full Potential”, Geological Society, London, Memoirs, 1–10.
[Google Scholar]
Lonergan, L. and Cartwright, L. [1999]. Polygonal Faults and Their Influence on Deep-Water Sandstone Reservoir Geometries, Alba Field, United Kingdom Central North Sea, AAPG Bulletin, 83(3), 410–432.
[Google Scholar]
Ramirez, A., Baldock, S., Mondal, D., Gromotka, J. and Hart, M. [2020]. Long offset ocean bottom node full-waveform inversion and multi-azimuth tomography for high-resolution velocity model building: North Sea, Utsira High, SEG Technical Program Expanded Abstracts 2020. https://library.seg.org/doi/10.1190/segam2020-3428806.1.
[Google Scholar]
Sun, J., Innanen, A.K. and Huang, C. [2021]. Physics-guided deep learning for seismic inversion with hybrid training and uncertainty analysis, Geophysics, 86, R303–R317. https://doi.org/10.1190/geo2020-0312.1.
[Google Scholar]
The Norwegian Petroleum Directorate (NPD) 8. Aug 2021, https://factpages.npd.no/nb-no/field/PageView/All.
[Google Scholar]
The Norwegian Petroleum Directorate (NPD) 8. Aug 2021, https://www.npd.no/contentassets/f0bf7cf3f3ee4700b9a361e2e9351369/chapter-4.pdf.
[Google Scholar]
Wu, X., Yan, S., Bi, Z., Zhang, S. and Si, H. [2021]. “Deep learning for multi-dimensional seismic impedance inversion,” Geophysics, 0, 1–44. https://doi.org/10.1190/geo2020-0564.1.
[Google Scholar]
Zoeppritz, Karl [1919]. “VIIb. Über Reflexion und Durchgang seis-mischer Wellen durch Unstetigkeitsflächen.” [VIIb. On reflection and transmission of seismic waves by surfaces of discontinuity], Nachrichten von der Königlichen Gesellschaft der Wissenschaften zu Göttingen, Mathematisch-physikalische Klasse, 66-84.
[Google Scholar]

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Benefits of using dense OBN for exploration: an example from Utsira using AI and machine learning

First Break 39, 45 (2021); https://doi.org/10.3997/1365-2397.fb2021074

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Benefits of using dense OBN for exploration: an example from Utsira using AI and machine learning

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