Multi-mineral segmentation of SEM images using deep learning techniques

Vladislav Vasilevich Alekseev, Denis Mihaylovich Orlov, Dmitry Anatolevich Koroteev

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The approaches of building and methods of using the digital core are currently developing rapidly. The use of these methods makes it possible to obtain petrophysical information by non-destructive methods quickly. Digital rock physics includes two main stages: constructing models and modeling various physical processes on the obtained models. Our work proposes using deep learning methods for mineral and pore space segmentation instead of classical methods such as threshold image processing. Deep neural networks have long been able to show their advantages in many areas of computer vision. This paper proposes and tests methods that help identify different minerals in images from a scanning electron microscope. We used images of rocks of the Achimov formation, which are arkoses, as samples. We tested various deep neural networks such as LinkNet, U-Net, ResUNet, and pix2pix and identified those that performed best in segmentation.

Original languageEnglish
Title of host publicationSociety of Petroleum Engineers - SPE Russian Petroleum Technology Conference 2021, RPTC 2021
PublisherSociety of Petroleum Engineers
ISBN (Electronic)9781613998304
DOIs
Publication statusPublished - 2021
EventSPE Russian Petroleum Technology Conference 2021, RPTC 2021 - Virtual, Online
Duration: 12 Oct 202115 Oct 2021

Publication series

NameSociety of Petroleum Engineers - SPE Russian Petroleum Technology Conference 2021, RPTC 2021

Conference

ConferenceSPE Russian Petroleum Technology Conference 2021, RPTC 2021
CityVirtual, Online
Period12/10/2115/10/21

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