Abstract
In technological advancement, there are several techniques have discovered for exact identification of hydrocarbons which is being used by oil industries to detect the oil reservoirs. In this study, we have investigated and proposed system of detection and prediction of hydrocarbons under earth subsurface through microscopic rock image modality. This system presents a robust watershed segmentation approach for determining porosity where convolutional neural networks are used for classification of sandstone and carbonate rock samples. The system is tested on microscopic images of sandstone and carbonate rock samples, and detection observed in rocks is based upon estimation of total porosity. Experimental comparison of proposed system shows outperform over state-of-the-art methods.
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Acknowledgement
The work presented here is being conducted in the Computer Vision Laboratory of Computer Science and Engineering Department of Tripura University (A Central University), Tripura, Suryamaninagar-799022, and Faculty of Engineering and Technology, Sri Ramachandra Institute of Higher Education and Research, Porur, Chennai-600116.
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Singha, A., Saha, P., Bhowmik, M.K. (2022). Automatic Classification of Sedimentary Rocks Towards Oil Reservoirs Detection. In: Raman, B., Murala, S., Chowdhury, A., Dhall, A., Goyal, P. (eds) Computer Vision and Image Processing. CVIP 2021. Communications in Computer and Information Science, vol 1567. Springer, Cham. https://doi.org/10.1007/978-3-031-11346-8_11
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