Automation of Carbonate Rock Thin Section Description Using Cognitive Image Recognition

Shebl, Hesham; Tamimi, Mohamed; Boyd, Douglas A.; Nehaid, Hani

doi:10.2118/208149-ms

Cited by 2 publications

References 6 publications

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Applying Machine Learning NLP Algorithm for Reconciliation Geology and Petrophysics in Rock Typing

Tveritnev,

Khanji,

Abdullah

et al. 2023

Day 2 Tue, October 03, 2023

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Oil- and gas-bearing rock deposits have distinct properties that significantly influence fluid distribution in pore spaces and the rock's ability to facilitate fluid flow. Rock typing involves analyzing various subsurface data to understand property relationships, enabling predictions even in data-limited areas. Central to this is understanding porosity, permeability, and saturation, which are crucial for identifying fluid types, volumes, flow rates, and estimating fluid recovery potential. These fundamental properties form the basis for informed decision-making in hydrocarbon reservoir development. While extensive descriptions with significant information exist, the data is frozen in text format and needs integration into analytical solutions like rock typing algorithms. Applying text analysis, a crucial area in natural language processing, aims to extract meaningful insights and valuable information from unstructured textual data. With the vast amount of text generated every day, automated and efficient text analysis methods are becoming increasingly essential. Machine learning techniques have revolutionized the analysis and understanding of text data. In this paper, we present a comprehensive summary of the available methods for text analysis using machine learning, covering various stages of the process, from data preprocessing to advanced text modeling approaches. The overview explores the strengths and limitations of each method, providing researchers and practitioners with valuable insights for their text analysis endeavors. A data-driven rock typing scheme is necessary for decision-making and optimization to achieve the best ultimate recovery of hydrocarbons in the most efficient way.

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Applying Machine Learning NLP Algorithm for Reconciliation Geology and Petrophysics in Rock Typing

Tveritnev,

Khanji,

Abdullah

et al. 2023

Day 2 Tue, October 03, 2023

View full text Add to dashboard Cite

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Intelligent Classification and Segmentation of Sandstone Thin Section Image Using a Semi-Supervised Framework and GL-SLIC

Han,

Liu

2024

Minerals

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This study presents the development and validation of a robust semi-supervised learning framework specifically designed for the automated segmentation and classification of sandstone thin section images from the Yanchang Formation in the Ordos Basin. Traditional geological image analysis methods encounter significant challenges due to the labor-intensive and error-prone nature of manual labeling, compounded by the diversity and complexity of rock thin sections. Our approach addresses these challenges by integrating the GL-SLIC algorithm, which combines Gabor filters and Local Binary Patterns for effective superpixel segmentation, laying the groundwork for advanced component identification. The primary innovation of this research is the semi-supervised learning model that utilizes a limited set of manually labeled samples to generate high-confidence pseudo labels, thereby significantly expanding the training dataset. This methodology effectively tackles the critical challenge of insufficient labeled data in geological image analysis, enhancing the model’s generalization capability from minimal initial input. Our framework improves segmentation accuracy by closely aligning superpixels with the intricate boundaries of mineral grains and pores. Additionally, it achieves substantial improvements in classification accuracy across various rock types, reaching up to 96.3% in testing scenarios. This semi-supervised approach represents a significant advancement in computational geology, providing a scalable and efficient solution for detailed petrographic analysis. It not only enhances the accuracy and efficiency of geological interpretations but also supports broader hydrocarbon exploration efforts.

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Automation of Carbonate Rock Thin Section Description Using Cognitive Image Recognition

Cited by 2 publications

References 6 publications

Applying Machine Learning NLP Algorithm for Reconciliation Geology and Petrophysics in Rock Typing

Applying Machine Learning NLP Algorithm for Reconciliation Geology and Petrophysics in Rock Typing

Intelligent Classification and Segmentation of Sandstone Thin Section Image Using a Semi-Supervised Framework and GL-SLIC

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