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Annual International Conference on Information Management and Big Data

SIMBig 2020: Information Management and Big Data pp 211–222Cite as

Towards a Benchmark for Sedimentary Facies Classification: Applied to the Netherlands F3 Block

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1410))

Abstract

In this paper, we attempt to provide a new benchmark for image seismic interpretation tasks in a public seismic dataset (Netherlands F3 Block). For this, techniques such as data augmentation together with five different deep network architectures were used, as well as the application of focal loss function. Our experiments achieved an improvement in all evaluation metrics cited at the current benchmark. For instance, we managed to improve in \(3.7\%\) the pixel accuracy metric and \(5.4\%\) on mean class accuracy for a modified U-Net that uses dilated convolution layers in its bottleneck. In addition to this, the confusion matrices of each model are shown for a better inspection in the classes (sedimentary facies) where the greatest amount of misclassification occurred. The training process of almost all networks took less than one hour to converge. Finally, we applied Conditional Random Fields (CRF) as post-processing in order to obtained smother results. The inferences performed with the best topology, in an inline or section of the test set, is closer to achieving an interpretation at a human level.

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Notes

  1. 1.

    A Machine Learning Benchmark for Facies Classification: https://github.com/yalaudah/facies_classification_benchmark.

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Acknowledgments

The authors would like to thank at the Applied Computational Intelligence Laboratory (ICA) and Cenpes/Petrobras, partners for 20 years in the research and development of artificial intelligence projects for oil and gas sector.

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Authors and Affiliations

  1. National University of Engineering, Lima, Peru

    Maykol J. Campos Trinidad & Smith W. Arauco Canchumuni

  2. Pontifical Catholic University of Rio de Janeiro, Rio de Janeiro, Brazil

    Maykol J. Campos Trinidad, Smith W. Arauco Canchumuni & Marco Aurelio Cavalcanti Pacheco

Authors
  1. Maykol J. Campos Trinidad
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  2. Smith W. Arauco Canchumuni
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  3. Marco Aurelio Cavalcanti Pacheco
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Corresponding author

Correspondence to Maykol J. Campos Trinidad .

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Editors and Affiliations

  1. Stanford University, Stanford, CA, USA

    Juan Antonio Lossio-Ventura

  2. Visibilia, São Paulo, Brazil

    Jorge Carlos Valverde-Rebaza

  3. University of Valencia, Valencia, Spain

    Eduardo Díaz

  4. Universidad del Pacífico, Lima, Peru

    Hugo Alatrista-Salas

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Campos Trinidad, M.J., Arauco Canchumuni, S.W., Cavalcanti Pacheco, M.A. (2021). Towards a Benchmark for Sedimentary Facies Classification: Applied to the Netherlands F3 Block. In: Lossio-Ventura, J.A., Valverde-Rebaza, J.C., Díaz, E., Alatrista-Salas, H. (eds) Information Management and Big Data. SIMBig 2020. Communications in Computer and Information Science, vol 1410. Springer, Cham. https://doi.org/10.1007/978-3-030-76228-5_15

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  • DOI: https://doi.org/10.1007/978-3-030-76228-5_15

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