E3S Web Conf.
Volume 146, 2020The 2019 International Symposium of the Society of Core Analysts (SCA 2019)
|Number of page(s)||11|
|Section||Core Analysis in a Digital World|
|Published online||05 February 2020|
Digital core repository coupled with machine learning as a tool to classify and assess petrophysical rock properties
1 Voxaya, Cap Oméga, rond-point Benjamin Franklin, CS 39521, 34960 Montpellier, France
2 University of Konstanz, Universitätsstraße 10, 78464 Konstanz, Germany
3 IFP Energies Nouvelles, 1-4 Avenue du Bois Préau, 92852 Rueil-Malmaison, France
* Corresponding author: firstname.lastname@example.org
To make efficient use of image-based rock physics workflow, it is necessary to optimize different criteria, among which: quantity, representativeness, size and resolution. Advances in artificial intelligence give insights of databases potential. Deep learning methods not only enable to classify rock images, but could also help to estimate their petrophysical properties. In this study we prepare a set of thousands high-resolution 3D images captured in a set of four reservoir rock samples as a base for learning and training. The Voxilon software computes numerical petrophysical analysis. We identify different descriptors directly from 3D images used as inputs. We use convolutional neural network modelling with supervised training using TensorFlow framework. Using approximately fifteen thousand 2D images to drive the classification network, the test on thousand unseen images shows any error of rock type misclassification. The porosity trend provides good fit between digital benchmark datasets and machine learning tests. In a few minutes, database screening classifies carbonates and sandstones images and associates the porosity values and distribution. This work aims at conveying the potential of deep learning method in reservoir characterization to petroleum research, to illustrate how a smart image-based rock physics database at industrial scale can swiftly give access to rock properties.
© The Authors, published by EDP Sciences, 2020
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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