Elastic impedance based facies classification using support vector machine and deep learning

Elastic impedance based facies classification using support vector machine and deep learning

Nishitsuji, Y. (TU Delft Applied Geophysics and Petrophysics; Petro Summit E&P Corporation)
Exley, Russell (Summit Exploration and Production Limited)

2018-08-27

Machine learning methods including support-vector-machine and deep learning are applied to facies classification problems using elastic impedances acquired from a Paleocene oil discovery in the UK Central North Sea. Both of the supervised learning approaches showed similar accuracy when predicting facies after the optimization of hyperparameters derived from well data. However, the results obtained by deep learning provided better correlation with available wells and more precise decision boundaries in cross-plot space when compared to the support-vector-machine approach. Results from the support-vector-machine and deep learning classifications are compared against a simplified linear projection based classification and a Bayes-based approach. Differences between the various facies classification methods are connected by not only their methodological differences but also human interactions connected to the selection of machine learning parameters. Despite the observed differences, machine learning applications, such as deep learning, have the potential to become standardized in the industry for the interpretation of amplitude versus offset cross-plot problems, thus providing an automated facies classification approach.

Elastics
Interpretation
Inversion
Reservoir geophysics
Rock physics

http://resolver.tudelft.nl/uuid:9be25d41-8234-47c6-bc21-82309e344255

https://doi.org/10.1111/1365-2478.12682

0016-8025

Geophysical Prospecting, 67 (2019) (4), 1040-1054

Institutional Repository

journal article

© 2018 Y. Nishitsuji, Russell Exley