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

Mateusz Zareba (), Tomasz Danek and Michal Stefaniuk
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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

Energies, 2023, vol. 16, issue 1, 1-25

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.

Keywords: machine learning; oil and gas; exploration; seismic; geophysics; well (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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