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Unsupervised Machine Learning Techniques for Improving Reservoir Interpretation Using Walkaway VSP and Sonic Log Data

Author

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  • Mateusz Zareba

    (Department of Geoinformatics and Applied Computer Science, Faculty of Geology, Geophysics and Environmental Protection, AGH University of Science and Technology, 30-059 Krakow, Poland)

  • Tomasz Danek

    (Department of Geoinformatics and Applied Computer Science, Faculty of Geology, Geophysics and Environmental Protection, AGH University of Science and Technology, 30-059 Krakow, Poland)

  • Michal Stefaniuk

    (Department of Fossil Fuels, Faculty of Geology, Geophysics and Environmental Protection, AGH University of Science and Technology, 30-059 Krakow, Poland)

Abstract

In this paper, we present a detailed analysis of the possibility of using unsupervised machine learning techniques for reservoir interpretation based on the parameters obtained from geophysical measurements that are related to the elastic properties of rocks. Four different clustering algorithms were compared, including balanced iterative reducing and clustering using hierarchies, the Gaussian mixture model, k-means, and spectral clustering. Measurements with different vertical resolutions were used. The first set of input parameters was obtained from the walkaway VSP survey. The second one was acquired in the well using a full-wave sonic tool. Apart from the study of algorithms used for clustering, two data pre-processing paths were analyzed in the context of matching the vertical resolution of both methods. The validation of the final results was carried out using a lithological identification of the medium based on an analysis of the drill core. The measurements were performed in Silurian rocks (claystone, mudstone, marly claystone) lying under an overburdened Zechstein formation (salt and anhydrite). This formation is known for high attenuating seismic signal properties. The presented study shows results from the first and only multilevel walkaway VSP acquisition in Poland.

Suggested Citation

  • Mateusz Zareba & Tomasz Danek & Michal Stefaniuk, 2023. "Unsupervised Machine Learning Techniques for Improving Reservoir Interpretation Using Walkaway VSP and Sonic Log Data," Energies, MDPI, vol. 16(1), pages 1-25, January.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:1:p:493-:d:1022677
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    References listed on IDEAS

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    1. Mateusz Zaręba & Tomasz Danek & Michał Stefaniuk, 2021. "P-Wave-Only Inversion of Challenging Walkaway VSP Data for Detailed Estimation of Local Anisotropy and Reservoir Parameters: A Case Study of Seismic Processing in Northern Poland," Energies, MDPI, vol. 14(8), pages 1-23, April.
    2. Kacper Domagała & Tomasz Maćkowski & Michał Stefaniuk & Beata Reicher, 2021. "Prediction of Reservoir Parameters of Cambrian Sandstones Using Petrophysical Modelling—Geothermal Potential Study of Polish Mainland Part of the Baltic Basin," Energies, MDPI, vol. 14(13), pages 1-28, July.
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