1887

Abstract

Summary

In this paper we present machine-learning (ML) technology using convolutional neural networks (CNN) trained from various seismic volumes and their matching appropriate labels from the Gulf of Mexico. These CNNs, the ML brains, are used to predict the probable position of the water bottom, top of salt, and base of salt, all required inputs to create velocity models during Earth model building (EMB). The interpretation process to generate these input horizons has traditionally been time-consuming and labor-intensive, taking days to weeks with the standard available tools. The time savings gained from the interpretation of a single volume compounded over the life span of the project due to the iterative nature of seismic imaging and the continual generation of improved seismic images is considerable. We record a 60–80% turnaround time reduction of the seismic interpretation stages, compared to a manual approach that relied on gridded interpretations and the best available auto-tracking capabilities. In addition to the turnaround time reduction, the horizons are delivered at higher resolution with improved quality, benefiting from the interpreter’s ability to focus on the more geologically complex areas

Loading

Article metrics loading...

/content/papers/10.3997/2214-4609.202310623
2023-06-05
2026-03-12
Loading full text...

Full text loading...

References

  1. Gramstad, O., & Nickel, M. (2018): Automated top salt interpretation using a deep convolutional net: Expanded Abstracts, 80th EAGE Conference & Exhibition, Copenhagen, Denmark
    [Google Scholar]
  2. Kaul, A., C.Li, & Abubakar, A. (2021): Pseudo three-dimensional deep learning approach for top and bottom of salt detection: Expanded Abstracts, In First International Meeting for Applied Geoscience & Energy
    [Google Scholar]
  3. Pilcher, Robin S., BillKilsdonk, & Trude, J. (2011): Primary basins and their boundaries in the deep-water northern Gulf of Mexico: Origin, trap types, and petroleum system implications: AAPG Bulletin95, no. 2, 219–240.
    [Google Scholar]
  4. Reisdorf, A., Fernandez, D.F., Munoz Cuenca, H.E., King, R., Manzano, D., & Menzel-Jones, G., (2022): Predicting horizons for salt body models using machine learning from neighboring seismic surveys: A case study from the northern Gulf of Mexico. Expanded Abstracts, Second International Meeting for Applied Geoscience & Energy.
    [Google Scholar]
  5. Ronneberger, O., Fischer, P., & Brox, T. (2015): U-net: Convolutional networks for biomedical image segmentation, in N.Navab, J.Hornegger, W.Wells, A.Frangi, eds., Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015, 9351, 234–241.
    [Google Scholar]
  6. Sen, S., Kainkaryam, C. Ong, & Sharma, A. (2020): SaltNet: A salt production-scale deep learning pipeline for automated salt model building: Leading Edge, 39, 195–203
    [Google Scholar]
  7. Zhao, T., C.Zhao, A.Kaul, & Abubakar, A. (2021): Automatic salt geometry update using deep learning in iterative FWI-RTM workflows. Expanded Abstracts, italic>First International Meeting for Applied Geoscience and Energy
    [Google Scholar]
  8. Zhou, H., S.Xu, G.Ionescu, M.Laomana, & Weber, N. (2020): Salt interpretation with U-Salt Net. Expanded Abstracts, 90th SEG Annual International Meeting
    [Google Scholar]
/content/papers/10.3997/2214-4609.202310623
Loading
/content/papers/10.3997/2214-4609.202310623
Loading

Data & Media loading...

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