1887
Volume 40, Issue 11
  • ISSN: 0263-5046
  • E-ISSN: 1365-2397

Abstract

Abstract

The interpretation of faults and horizons from seismic data forms a critical part of the geoscience workflow, enhancing our understanding of the subsurface and ultimately the chance of success in extracting hydrocarbons. The depiction of these vital seismic interpretations has long been restricted to conventional manual and semi-automatic techniques, which require geoscientists to work line by line over ever-expanding volumes of seismic data.

The advent of machine learning (ML) and cloud computing technology has revolutionised tasks across multiple industries, enabling the identification of patterns in large multi-factor datasets. In this case study, we predict fault locations using 2D U-Net architecture convolutional neural network (CNNs) and predict horizons by employing Radial Basis Functions (RBFs) and Neural Networks (NNs). We aim to demonstrate the gains in efficiency and geological insight found using ML technology as the bedrock of the seismic interpretation workflow, through the interpretation of a broadband seismic dataset from the Loppa High area, Barents Sea.

Loading

Article metrics loading...

/content/journals/10.3997/1365-2397.fb2022091
2022-11-01
2022-11-28
Loading full text...

Full text loading...

References

  1. Indrevær, K., Gabrielsen, R.H. and Faleide, J.I. [2017]. Early Cretaceous synrift uplift and tectonic inversion in the Loppa High area, southwestern Barents Sea, Norwegian shelf.Journal of the Geological Society, 174(2), 242–254.
    [Google Scholar]
  2. Indrevær, K., Gabrielsen, R.H. and Faleide, J.I. [2018]. Crustal-scale subsidence and uplift caused by metamorphic phase changes in the lower crust: a model for the evolution of the Loppa High area, SW Barents Sea from late Paleozoic to Present.Journal of the Geological Society, 175(3), 497–508.
    [Google Scholar]
  3. Kairanov, B., Marín, D., Escalona, A. and Cardozo, N. [2019]. Growth and linkage of a basin-bounding fault system: Insights from the Early Cretaceous evolution of the northern Polhem Subplatform, SW Barents Sea.Journal of Structural Geology, 124, 182–196.
    [Google Scholar]
  4. Lie, J., Danielsen, V., Dhelie, P.E., Sablon, R., Siliqi, R., Grubb, C., Vinje, V., Nilsen, C.I. and Soubaras, R. [2018]. A Novel Source-Over-Cable Solution to Address the Barents Sea Imaging Challenges, European Association of Geoscientists and Engineers.
    [Google Scholar]
  5. ManralS. [2020]. Enhancing Fault Interpretation Efficiency and Accuracy with Deep Convolutional Neural Network and Elastic Cloud Compute.First EAGE Digitalization Conference and Exhibition, 2020. 1–5.
    [Google Scholar]
  6. MemariN., RamliA.R., SaripanM.I.B., MashohorS. and MoghbelM. [2017]. Supervised retinal vessel segmentationfrom color fundus images based on matched filtering and AdaBoost classifier.Computerized Medical Imaging and Graphics: The Official Journal of the Computerized Medical Imaging Society, 55, 68–77.
    [Google Scholar]
  7. Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A. and Fei-Fei, L. [2015]. ImageNet Large Scale Visual Recognition Challenge.International Journal of Computer Vision, 115, 211–252.
    [Google Scholar]
  8. Smith, S., Zimina, S., Manral, S. and Nickel, M. [2022]. Machine-learning assisted interpretation: Integrated fault prediction and extraction case study from the Groningen gas field, Netherlands.Interpretation, 10(2), 17–30.
    [Google Scholar]
  9. Tschannen, V., Delescluse, M., Ettrich, N. and Keuper, J. [2020]. Extracting horizon surfaces from 3D seismic data using deep learning.Geophysics, 85(3), 17–26.
    [Google Scholar]
  10. Waldeland, A.U., Jensen, A. C., Gelius, L.J. and Solberg, A.H.S. [2018]. Convolutional neural networks for automated seismic interpretation.The Leading Edge, 37, 529–537.
    [Google Scholar]
  11. Zimina, O., Hellem Boe, T., Manral, S. and Aare, V. [2022]. Machine Learning Assisted Seismic Horizon Interpretation Applied on the Groningen Gas Field, Netherlands.Second EAGE Digitalization Conference and Exhibition, Volume March 2022, 1–6.
    [Google Scholar]
http://instance.metastore.ingenta.com/content/journals/10.3997/1365-2397.fb2022091
Loading
/content/journals/10.3997/1365-2397.fb2022091
Loading

Data & Media loading...

  • Article Type: Research Article
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error