Journal
COMPUTERS & GEOSCIENCES
Volume 148, Issue -, Pages -Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.cageo.2020.104684
Keywords
Semi-supervised clustering; Oil prospectivity; Graph Laplacian
Funding
- Mitacs
- Chevron Energy Technology Company
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A new graph-Laplacian based semi-supervised clustering method is proposed in this research, which is capable of handling large datasets and yielding satisfactory results in oil prospectivity analysis.
We present a new graph-Laplacian based semi-supervised clustering method. This new approach can be viewed as an extension/improvement of previously published work, both in terms of areas of applicability and computational speed. Our clustering method is capable of handling very large datasets with millions of data points using very limited amounts of labelled data. In this work, we apply our clustering method to 3D oil prospectivity, based on amplitude-versus-angle inversion parameters and borehole information. We cluster the synthetic Life of Field dataset, which has a fault-block constrained central oil reservoir, where we also perform a cross-validation check of the predictive power of our method. Furthermore, we cluster a field dataset, which is characterized by a stratigraphic trapped channelling system. In both cases we find appealing results.
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