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ISC2018, Québec City
CORE PHOTOS LITHOLOGICAL INTERPRETATION USING NEURAL
NETWORKS
E.E.Baraboshkin1*, A.V.Ivchenko2**, L.S.Ismailova1, D.M.Orlov1, E.Yu.Baraboshkin3, D.A.Koroteev1
1Petroleum Engineering department, Skolkovo Institute of Science and Technology, Nobelya Ulitsa 3, Moscow,
Russia.
2 Radio Engineering and Cybernetics department, Moscow Institute of Physics and Technology, 9 Institutskiy per.,
Dolgoprudny, Moscow Region, Russia.
3 Regional Geology and Earth History department, Lomonosov Moscow State University, Leninskie Gory 1, Moscow,
Russia.
*e-mail: evgenii.baraboshkin@skoltech.ru
** e-mail: ivchenko.a.v@phystech.edu
With a rapid increase in digital data, oil and gas companies are constantly looking for an
automatic and accurate method to extract the geological information from the core. However,
methods for automatic core analysis are very limited2. At the moment, this is still done by an expert-
geologist, which takes a lot of time and effort. Recently, Chatterje (2012) applied the multi-class
support vector machine (SVM) algorithm to classify rocks1. Hasanov et al. 2016 used color
distribution analysis on core photos3. Still, no comprehensive study was performed on real
geological samples with complex structural and textural features.
In this study, we propose a non-destructive method for rock-type classification of core
images. An algorithm for core images processing and rock-type classification is based on
convolutional neural network analysis. We trained our network using 10 cm samples of core
images, which uniformly sized to 128x128 px images. The training samples consisted of four
lithotypes (laminated sandstone, limestone, sandstone, shale). We also added to the analysis two
non-conditional core types (non-cutted and crushed core). The computer successfully distinguished
4 classes with different false rates. The best results achieved for limestones and shales (recall=83
and 91%, on 54 and 138 test images), the worst are for laminated sandstones and sandstones (recall
are 53 and 73% on 15 test images each). Other two groups discern with poor scores (crushed core
recall 7%, non-cutted– 32% on 30 and 57 images). In most cases these two groups got right class
as a real rock type and wrong one as image predicted type (compared to true label of image). As it
seen from experimental results, the rates highly depend on each class number and quality of images.
The performance of low-quality images with a small number of them in class gives the poor
precision.
Our results show that the convolutional neural network detects the right class of lithology
with low boundary recall 85% (average, in grayscale images), even on small amount of data.
Additionally, this algorithm allows studying core photos in much faster and accurate way. To
improve further the accuracy of the method, more core-photos of different quality, texture
characteristic and geological settings should be analyzed.
References
1 S. Chatterjee. Applied Intelligence. 39, 2012.
2 L. Lepistö, I. Kunttu, J. Autio, A. Visa. WSCG SHORT PAPERS proceedings, WSCG'2003, 2003.
3 I.I. Hasanov, I.A. Ponomarev, A.V. Postnikov, N.A. Osintseva. Geomodel-2016 (in Russian), 2016.
Acknowledgements
This work was supported by RFBR (grants 16-05-00207a, 13-05-00745a).