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Machine Vision Methods in the Application for Core Image SegmentationNormal access

Authors: E. Baraboshkin, L. Ismailova, D. Orlov and D. Koroteev
Event name: ProGREss'19
Session: Pitch Session «Solutions in O&G Exploration» / Питч сессия «Поиск решений в ГРР»
Publication date: 05 August 2019
DOI: 10.3997/2214-4609.201953061
Language: Russian
Info: Extended abstract, PDF ( 501.68Kb )
Price: € 20

Summary:
The computational power of the reservoir modelling is growing nowadays enabling the use of more precise core descriptions. The industry needs high accuracy models for precise reserves estimation. As a way to improve that, different authors proposed semiautomatic image segmentation algorithms based on color spaces approaches. The segmentation algorithms are common in machine vision as most images consist of semantically different parts. This paper focuses on the review and application of different machine vision algorithms for semi-supervised segmentation of full core images based on superpixel approach. Such an approach takes into account pixel groups with their semantic (texture, intensities, etc.) meaning. The reviewed algorithms can contribute to the precise description of rocks at different scales. The automatic way to segment lithotypes and other characteristics of rock introduced. U-Net like convolutional neural network fine-tuned on a small dataset may produce meaningful results.


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