georesГеоресурсыGeoresources1608-50431608-5078Georesursy LLC10.18599/grs.2025.4.16geores-531Research ArticleСТАТЬИRESEARCH ARTICLESПрогнозирование пористости стандартных образцов керна на основе микротомографии, разметки с учителем и трансформера со сдвигаемыми окнамиCore Plug Porosity Prediction Using Microtomography, Supervised Labeling, and a Shifted Window TransformerКадыровР. И.KadyrovR. I.

Раиль Илгизарович Кадыров - кандидат геол.-минерал. наук, старший научный сотрудник, Институт геологии и нефтегазовых технологий

420008, Казань, ул. Кремлевская, д. 4/5

Rail I. Kadyrov – Cand. Sci. (Geology and Mineralogy), Senior Researcher, Institute of Geology and Petroleum Technologies

4/5 Kremlevskaya st., Kazan, 420008

rail7777@gmail.comСтаценкоЕ. О.StatsenkoE. O.

Евгений Олегович Стаценко - младший научный со­трудник, Институт геологии и нефтегазовых технологий,

420008, Казань, ул. Кремлевская, д. 4/5

Evgeny O. Statsenko – Junior Research Fellow, Institute of Geology and Petroleum Technologies

4/5 Kremlevskaya st., Kazan, 420008

НгуенТ. Х.NguyenT. H.

Тхань Хынг Нгуен - младший научный сотрудник, Институт геологии и нефтегазовых технологий

420008, Казань, ул. Кремлевская, д. 4/5

Thanh Hung Nguyen – Junior Research Fellow, Institute of Geology and Petroleum Technologies

4/5 Kremlevskaya st., Kazan, 420008

СкоробогатоваМ. А.SkorobogatovaM. A.

Марта Андреевна Скоробогатова - инженер, Институт геологии и нефтегазовых технологий

420008, Казань, ул. Кремлевская, д. 4/5

Marta A. Skorobogatova – Engineer, Institute of Geology and Petroleum Technologies

4/5 Kremlevskaya st., Kazan, 420008

Казанский федеральный университетРоссияKazan Federal UniversityRussian Federation2025301220252746782Copyright © Кадыров Р.И., Стаценко Е.О., Нгуен Т.Х., Скоробогатова М.А., 20252025Кадыров Р.И., Стаценко Е.О., Нгуен Т.Х., Скоробогатова М.А.Kadyrov R.I., Statsenko E.O., Nguyen T.H., Skorobogatova M.A.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://www.geors.ru/jour/article/view/531

Недавние достижения в области машинного обуче­ния позволили автоматически анализировать изображе­ния микротомографии (микро-КТ), способствуя более эффективной идентификации свойств горных пород. Целью данного исследования является прогнозирование экспериментально измеренной открытой пористости пород-коллекторов с использованием изображений микро-КТ стандартных образцов керна. Был собран на­бор данных из 136 образцов керна, включая 49 образцов песчаника и 87 образцов карбоната. Открытая пористость была экспериментально определена с использованием газового волюметра. Образцы керна (30 ± 1 мм в высо­ту и диаметр) были отсканированы с помощью микро­КТ с разрешением 34,6-38,0 мкм, что дало 16-битные стеки изображений. Набор данных состоял из 100 232 изображений (64 119 карбоната и 36 113 песчаника). Для маркировки изображений мы ввели контролируе­мый метод под названием «Сегментация неразрешенных пор с помощью экспериментального эталона» (SUPER), который сегментирует темные воксели для соответствия экспериментально измеренной открытой пористости, адаптируясь к характеристикам каждого образца. Были обучены три модели трансформера со сдвигаемыми окнами (Swin): универсальная модель и специализи­рованные модели для песчаника и карбоната. Модели использовали трансферное обучение с весами ImageNet, за которым последовала тонкая настройка. Тестирование подтвердило, что специализированные модели превзошли универсальную модель. Это подчеркивает, что обучение ансамбля моделей, адаптированных к определенным типам пород, приводит к лучшей производительности, чем одна общая модель для прогнозирования пористости. Основная проблема возникла с песчаниками, особенно мелкозернистыми типами, где мелкие поры сливались из- за ограничений разрешения. Последующая работа должна быть направлена на улучшение разрешения изображений и непосредственное введение детализированных изо­бражений в модель. Метод имеет потенциал применения для полноразмерного керна и ранней оценки пористости в неэкстрагированных стандартных образцах, включая хрупкие коллекторы с нефтью или битумами.

Recent advances in machine learning have enabled the automatic analysis of microtomography (µCT) images, facilitating more efficient rock property identification. This study aims to predict the experimentally measured open porosity of reservoir rocks using µCT images of standard core plugs. A dataset of 136 core plugs was collected, including 49 sandstone and 87 carbonate samples. Open porosity was experimentally determined using gas volumetry. The core plugs (30 ± 1 mm in height and diameter) were scanned using µCT with a resolution of 34.6–38.0 µm, producing 16-bit image stacks. The dataset consisted of 100,232 images (64,119 carbonate and 36,113 sandstone). To label the images, we introduced a supervised method called Segmentation of Unresolved Pores via Experimental Reference (SUPER), which segments dark voxels to match the experimentally measured open porosity, adapting to each sample’s characteristics. Three shifted window (Swin) transformer models were trained: a universal model and specialized models for sandstone and carbonate. The models used transfer learning with ImageNet weights, followed by fine-tuning. Testing confirmed that specialized models outperformed the universal model. This highlights that training an ensemble of models adapted to specific rock types leads to better performance than a single general model for porosity prediction. A key challenge arose with sandstones, especially fine-grained types, where small pores merged due to resolution limitations. Future work should improve image resolution and feed detailed images into the model. The method has potential for full-scale core scans and early porosity assessment in raw core plugs, including fragile reservoirs with oil or bitumens.

микро-КТ (микротомография)открытая пористостьколлекторстандартный образец кернамашинное обучениетрансферное обучениеSwin (трансформер со сдвигаемыми окнами)прогнозирование пористостиµCT (microtomography)open porosityreservoir rockcore plugmachine learningtransfer learningSwin (shifted window) transformerporosity predictionРабота выполнена за счет гранта Академии наук Республики Татарстан, предоставленного молодым кандидатам наук (постдокторантам) с целью защиты докторской диссертации, выполнения научно-исследовательских работ, а также выполнения трудовых функций в научных и образовательных организациях Республики Татарстан в рамках Государственной программы Республики Татарстан «Научно-технологическое развитие Республики Татарстан» (соглашение № 20/2024-ПД)This paper is performed as part of the grant of the Tatarstan Academy of Sciences, provided to young candidates of science (postdoctoral fellows) for the purpose of defending their doctoral dissertation, conducting research, as well as performing their work duties in scientific and educational organizations of the Republic of Tatarstan within the framework of the State Program of the Republic of Tatarstan «Scientific and Technological Development of the Republic of Tatarstan» (Agreement No.20/2024-PD)ReferencesAlqahtani N., Alzubaidi F., Armstrong R.T., Swietojanski P. & Mostaghimi P. 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The authors declare that there are no conflicts of interest present.