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Deep learning for lithological classification of carbonate rock micro-CT images

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Abstract

In addition to the ongoing development, pre-salt carbonate reservoir characterization remains a challenge, primarily due to inherent geological particularities. These challenges stimulate the use of well-established technologies, such as artificial intelligence algorithms, for image classification tasks. Therefore, this work intends to present an application of deep learning techniques to identify lithological patterns in Brazilian pre-salt carbonate rocks using microtomographic images. Four convolutional neural network models were proposed. The first model includes three convolutional layers, followed by a fully connected layer. This model is used as a base model for the following proposals. In the next two models, we replace the max pooling layer with a spatial pyramid pooling and a global average pooling layer. The last model uses a combination of spatial pyramid pooling followed by global average pooling in place of the final pooling layer. All models are compared using original images, when possible, as well as resized images. The dataset consists of 6,000 images from three different classes. The model performances were evaluated by each image individually, as well as by the most frequently predicted class for each sample. According to accuracy, Model 2 trained on resized images achieved the best results, reaching an average of 75.54% for the first evaluation approach and an average of 81.33% for the second. We developed a workflow to automate and accelerate the lithology classification of Brazilian pre-salt carbonate samples by categorizing microtomographic images using deep learning algorithms in a non-destructive way.

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Acknowledgements

The authors would like to thank Petrobras for providing the data and financial support and NVIDIA Corporation for the GPU provided by the NVIDIA Grant Program. The authors also thank the Brazilian Research Council (CNPq) for the scholarships for students and researchers.

Funding

This work was funded by Petrobras, which provided the data and financial support.

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Authors and Affiliations

  1. Department of Civil Engineering, COPPE, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil

    Carlos E. M. dos Anjos, Manuel R. V. Avila, Adna G. P. Vasconcelos, Aurea M. Pereira Neta, Lizianne C. Medeiros, Alexandre G. Evsukoff & Luiz Landau

  2. CENPES, Petrobras, Rio de Janeiro, Brazil

    Rodrigo Surmas

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  1. Carlos E. M. dos Anjos
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  2. Manuel R. V. Avila
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  3. Adna G. P. Vasconcelos
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Correspondence to Carlos E. M. dos Anjos.

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dos Anjos, C.E.M., Avila, M.R.V., Vasconcelos, A.G.P. et al. Deep learning for lithological classification of carbonate rock micro-CT images. Comput Geosci 25, 971–983 (2021). https://doi.org/10.1007/s10596-021-10033-6

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