georesГеоресурсыGeoresources1608-50431608-5078Georesursy LLC10.18599/grs.2024.4.11geores-429Research ArticleПОИСК, РАЗВЕДКА И РАЗРАБОТКА МЕСТОРОЖДЕНИЙ УГЛЕВОДОРОДОВ, ИССЛЕДОВАНИЕ КОЛЛЕКТОРОВPROSPECTING, EXPLORATION AND DEVELOPMENT OF HYDROCARBON DEPOSITS, RESERVOIR PROPERTIES STUDYПрименение искусственных цифровых моделей в методе рентгеновской томографии керна при решении задачи бинаризации пустотного пространства горных породThe Application of Artificial Digital Models in X-Ray Computed Tomography (CT) of the Core in Solving the Problem of Binarization of the Void Space of Reservoir RocksМелкишевО. А.MelkishevO. A.

Олег Александрович Мелкишев – доцент кафедры Геология нефти и газа, кандидат техн. Наук.

614990, Пермь, Комсомольский пр., д. 29

Oleg A. Melkishev – Associate Professor of the Department of Oil and Gas Geology, Cand. Sci. (Technical Sciences).

29 Komsomolsky Ave., Perm, 614990

melkishev@pstu.ruСавицкийЯ. В.SavitskyY. V.

Ян Владимирович Савицкий – инженер кафедры Геология нефти и газа, кандидат тех. наук.

614990, Пермь, Комсомольский пр., д. 29

Yan V. Savitsky – Engineer of the Department of Oil and Gas Geology, Cand. Sci. (Technical Sciences).

29 Komsomolsky Ave., Perm, 614990

yansavitsky@pstu.ruГалкинС. В.GalkinS. V.

Сергей Владиславович Галкин – доктор геол.-минерал. наук, профессор, декан горно-нефтяного факультета.

614990, Пермь, Комсомольский пр., д. 29

Sergey V. Galkin – Dr. Sci. (Geology and Mineralogy), Professor, Dean of the Faculty of Mining and Petroleum.

29 Komsomolsky Ave., Perm, 614990

gnfd@pstu.ruПермский национальный исследовательский политехнический университетРоссияPerm National Research Polytechnic UniversityRussian Federation202430122024264218228Copyright © Мелкишев О.А., Савицкий Я.В., Галкин С.В., 20242024Мелкишев О.А., Савицкий Я.В., Галкин С.В.Melkishev O.A., Savitsky Y.V., Galkin S.V.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/429

Метод рентгеновской томографии обладает рядом преимуществ, заключающихся в неразрушающем способе воздействия на образец и возможности объемной визуализации скелета породы и емкостного Пространства. При этом проблемой, Ограничивающей Возможности Практического Использования Томографии, является низкая разрешающая способность при исследовании образцов диаметром 30 мм. В образцах таких размеров существенная часть пор имеет размеры меньшие, чем разрешающая способность большинства современных систем рентгеновской томографии, что не позволяет определить граничное значение пора – скелет в томограммах керна и визуализировать весь объем емкостного пространства. Для подтверждения этого проанализированы томограммы реальных образцов коллекторов нефти и газа. Анализ полученных гистограмм условной рентгеновской плотности позволил прийти к выводу, что прямое определение граничного значения условной рентгеновской плотности, характеризующей границу пора – скелет, невозможно.

Для решения проблемы оценки граничного значения в работе предложен подход, предлагающий применение искусственных цифровых моделей – фантомов. Эта методика ранее использовались преимущественно в компьютерном моделировании, в нефтяной геологии такой подход практически не применялся. Главным достоинством метода использования фантомов является полный контроль задаваемых параметров порового пространства и рентгеновской плотности скелета, что принципиально не достижимо на реальных образцах. Проведен вычислительный эксперимент, в ходе которого с помощью численного моделирования созданы 124 фантома керна с заданными характеристиками пористости. Эксперимент позволил установить статистические характеристики для значений условной рентгеновской плотности образца, получаемых на этапе реконструкции.

На основе результатов эксперимента определены граничные значения, пригодные для наиболее достоверного выделения пустотного пространства. При помощи регрессионного и корреляционного анализа предложена модель оценки оптимального граничного значения условной рентгеновской плотности для выделения пустотного пространства. Предложен алгоритм, позволяющий определять и использовать это значение при обработке и анализе данных рентгеновской томографии керна.

Результаты представленной методики использованы для оценки структуры пустотного пространства реальных образцов керна, которые не привлекались для создания модели прогноза. Применение разработанной модели прогноза граничных значений продемонстрировало высокую корреляцию с фактическими данными.

The X-ray tomography method has several advantages, including its non-destructiveness and the ability to visualize the rock skeleton and pore space in three dimensions. However, one of the main challenges of this method is the limited resolution when studying core samples that are 30 millimeters in diameter, which is typical for petrophysical analysis. In these samples, a significant portion of pores have dimensions smaller than the resolution capabilities of most X-ray tomographic systems, making it impossible to accurately determine the boundary between the pore and skeleton structures in tomograms, nor visualize the entire pore volume.

To verify this hypothesis, tomograms from real oil and gas samples were analyzed. The resulting histograms of X-ray densities revealed that it is not possible to directly measure the threshold value of X-ray density that defines the “skeleton-pore” boundary. In order to solve the problem of estimating boundary values, a technique is proposed in this work that suggests using artificial digital models – phantoms. This approach has been previously used mainly in computer modeling, but it has not been used much in petroleum geology. The main advantage of using phantoms is complete control over the set pore space parameters and X-ray density of the skeleton, which cannot be achieved on real samples.

A computational experiment was conducted in the work, where 124 core phantoms with specific porosity characteristics were generated using numerical modeling. These phantoms were then converted into tomograms, allowing us to determine statistical characteristics of the values for X-ray densities of the samples at the reconstruction stage.

Based on the statistical analysis of the X-ray density distribution in the sample, we determined the boundary values that are most suitable for reliable void space detection. Using regression and correlation methods, we developed a model to estimate the optimal boundary value for X-ray density in void space allocation.

We proposed an algorithm for determining and applying this value in the analysis of core X-ray CT data.This model was tested on real samples that were not used in the development of the forecast model. The use of the proposed model for predicting boundary values on obtained tomograms demonstrated a high degree of consistency with actual data.

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The authors declare that there are no conflicts of interest present.