1887

Abstract

Summary

Faults are essential subsurface features affecting the mechanical and hydraulic properties of rock masses. However, interpreting faults from seismic images may lead to various scenarios reflecting the uncertainty due to the seismic image quality and fault zone definition. Actually, some details of the fault network may be invisible because of the seismic image resolution. For instance, one large fault can be seen as a single feature or as a collection of smaller ones and the uncertainty on the connectivity of such small features may be under-estimated. The goal of this work is to quantify the uncertainty related to the number and connectivity of faults honoring a likelihood image, built from seismic image. A marked point process framework is adopted to capture the geometry and the topology of fault network while using a data conditioning strategy to account for fault localization. This modeling strategy enables a construction of a Gibbs probability distribution to characterize fault networks. The output realizations are sampled from this distribution using the Metropolis-Hastings algorithm. To characterize these realizations, the visit map is constructed to visualize regions of highest posterior probabilities of fault presence. The approach is applied on the Volve data, acquired in the Central North Sea.

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2023-11-27
2025-11-11
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References

  1. Aydin, O., Caers, J. K. [2017] Quantifying structural uncertainty on fault networks using a marked point process within a Bayesian framework.Tectonophysics712–713:101–124.
    [Google Scholar]
  2. Bonneau, F., Caumon, G., Renard, P. [2016] Impact of a stochastic sequential initiation of fractures on the spatial correlations and connectivity of discrete fracture networks.Journal of Geophysical Research: Solid Earth121(8):5641–5658.
    [Google Scholar]
  3. Botter, C., Cardozo, N., Lecomte, I., Rotevatn, A., Paton, G. [2017] The impact of faults and fluid flow on seismic images of a relay ramp over production time.Petroleum Geoscience23(1):17–28.
    [Google Scholar]
  4. Cherpeau, N., Caumon, G. [2015] Stochastic structural modelling in sparse data situations.Petroleum Geoscience21(4):233–247.
    [Google Scholar]
  5. Childs, C., Manzocchi, T., Walsh, J. J., Bonson, C. G., Nicol, A., Schöpfer, M. P. [2009] A geometric model of fault zone and fault rock thickness variations.Journal of Structural Geology31(2):117–127.
    [Google Scholar]
  6. Chiu, S. N., Stoyan, D., Kendall, W. S., Mecke, J. [2013] Stochastic geometry and its applications. Wiley series in probability and statistics, Chichester, West Sussex, United Kingdom: John Wiley & Sons Inc, third edition.
    [Google Scholar]
  7. Geode-Solutions [2022] OpenGeode framework.Zenodo. Retrieved 2022-06-01, from https://zenodo.org/record/3610370doi:10.5281/ZENODO.3610370.
    [Google Scholar]
  8. Goodwin, H., Aker, E., Røe, P. [2022] Stochastic Modeling of Subseismic Faults Conditioned on Displacement and Orientation Maps.Mathematical Geosciences54(1):207–224.
    [Google Scholar]
  9. Kruuse, M., Tempel, E., Kipper, R., Stoica, R. S. [2019] Photometric redshift galaxies as tracers of the filamentary network.Astronomy & Astrophysics625:A130.
    [Google Scholar]
  10. Ravasi, M., Vasconcelos, I., Curtis, A., Kritski, A. [2015] Vector-acoustic reverse time migration of Volve ocean-bottom cable data set without up/down decomposed wavefields.GEOPHYSICS80(4):S137–S150.
    [Google Scholar]
  11. Soleng, H., Rivenæs, J., Gjerde, J., Hollund, K., Holden, L. [2004] Structural Uncertainty Modelling and the Representation of Faults as Staircases. In ECMOR IX - 9th European Conference on the Mathematics of Oil Recovery, Cannes, France,. European Association of Geoscientists & Engineers.
    [Google Scholar]
  12. Stoica, R. S., Gay, E., Kretzschmar, A. [2007] Cluster Pattern Detection in Spatial Data Based on Monte Carlo Inference.Biometrical Journal49(4):505–519.
    [Google Scholar]
  13. Stoica, R. S., Martínez, V. J., Mateu, J., Saar, E. [2005] Detection of cosmic filaments using the Candy model.Astronomy & Astrophysics434(2):423–432.
    [Google Scholar]
  14. Szydlik, T., Way, S., Smith, P., Aamodt, L., Friedrich, C. [2006] 3d pp/ps prestack depth migration on the volve field. In 68th EAGE Conference and Exhibition incorporating SPE EUROPEC 2006, pages cp–2. EAGE Publications BV.
    [Google Scholar]
  15. Tempel, E., Stoica, R. S., Martínez, V. J., Liivamägi, L. J., Castellan, G., Saar, E. [2014] Detecting filamentary pattern in the cosmic web: a catalogue of filaments for the SDSS.Monthly Notices of the Royal Astronomical Society438(4):3465–3482.
    [Google Scholar]
  16. Torabi, A., Berg, S. S. [2011] Scaling of fault attributes: A review.Marine and Petroleum Geology28(8):1444–1460.
    [Google Scholar]
  17. Van Lieshout, M. N. M., Stoica, R. S. [2003] The Candy model: properties and inference.Statistica Neerlandica57(2):177–206.
    [Google Scholar]
  18. Wu, X., Shi, Y., Fomel, S., Liang, L., Zhang, Q., Yusifov, A. Z. [2019] FaultNet3D: Predicting Fault Probabilities, Strikes, and Dips With a Single Convolutional Neural Network.IEEE Transactions on Geoscience and Remote Sensing57(11):9138–9155.
    [Google Scholar]
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