Peer-reviewed articles 17,970 +


Title: CORE-SCALE ROCK TYPING USING CONVOLUTIONAL NEURAL NETWORKS FOR RESERVOIR CHARACTERIZATION IN THE PETROLEUM INDUSTRY
CORE-SCALE ROCK TYPING USING CONVOLUTIONAL NEURAL NETWORKS FOR RESERVOIR CHARACTERIZATION IN THE PETROLEUM INDUSTRY

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Muhammad Sarmad; Johan Phan; Leonardo Ruspini; Gabriel Kiss; Frank Lindseth
10.5593/sgem2023/1.1/s06.78
10.5593/sgem2023/1.1
1314-2704
978-619-7603-56-9
English
23
1.1
•    Prof. DSc. Oleksandr Trofymchuk, UKRAINE 
•    Prof. Dr. hab. oec. Baiba Rivza, LATVIA
Oil and Gas Exploration
Rock typing is an essential tool for reservoir characterization and management in the petroleum industry. It is the process of grouping portions of a rock sample based on their physical and chemical properties. This process is currently done by experts in the industry, which consumes valuable industry resources. Precise and efficient rock typing can build accurate geological models, optimize exploration and production strategies, and reduce exploration and production risks. This work proposes a deep learning method to identify and classify rocks based on their pore geometry, mineralogy, and other characteristics. The proposed technique segments a micro-CT image into different rock types using a neural network for automated rock typing. We suggest using a UNet architecture for the neural network for this task. The network has been trained in a supervised manner on expert-labelled images. The method's performance has been evaluated using qualitative and quantitative metrics. The neural network takes less than 200 milliseconds to provide the rock types, which is much faster than a human expert. We perform an explainability analysis of the neural network using class activation heatmaps approach to get insight into the learned weights. Rock typing using deep learning can improve the petroleum industry's workflow.
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This work was partially supported by the Norwegian Research Council (grant number 296093) and the members of the SmartRocks joint industry project (ENI AS, Repsol AS, and Chevron Corporation). (Ismail, 2013)
conference
https://doi.org/10.5593/sgem2023/1.1/s06.78
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Proceedings of 23rd International Multidisciplinary Scientific GeoConference SGEM 2023
23rd International Multidisciplinary Scientific GeoConference SGEM 2023, 03 - 09 July, 2023
Proceedings Paper
STEF92 Technology
International Multidisciplinary Scientific GeoConference-SGEM
SWS Scholarly Society; Acad Sci Czech Republ; Latvian Acad Sci; Polish Acad Sci; Russian Acad Sci; Serbian Acad Sci and Arts; Natl Acad Sci Ukraine; Natl Acad Sci Armenia; Sci Council Japan; European Acad Sci, Arts and Letters; Acad Fine Arts Zagreb Croatia; Croatian Acad Sci and Arts; Acad Sci Moldova; Montenegrin Acad Sci and Arts; Georgian Acad Sci; Acad Fine Arts and Design Bratislava; Russian Acad Arts; Turkish Acad Sci.
653-664
03 - 09 July, 2023
website
9067
rock typing, digital rock analysis, deep learning, segmentation

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