georesГеоресурсыGeoresources1608-50431608-5078Georesursy LLC10.18599/grs.2025.1.3geores-296Research ArticleГЕОЛОГО-ГЕОХИМИЧЕСКИЕ ИССЛЕДОВАНИЯ, ПОИСК, РАЗВЕДКА И РАЗРАБОТКА МЕСТОРОЖДЕНИЙ УГЛЕВОДОРОДОВGEOLOGICAL-GEOCHEMICAL RESEARCH, PROSPECTING, EXPLORATION AND DEVELOPMENT OF HYDROCARBON FIELDSПрименение статистических методов оценки трещиноватых коллекторов по промысловым даннымApplying Statistical Methods to Assess Fractured Reservoirs Relying on Field Datahttps://orcid.org/0000-0003-4004-2160ЩекинА. И.ShchekinA. I.

Александр Иванович Щекин, кандидат тех. наук, доцент

кафедра разработки и эксплуатации нефтяных и газовых месторождений

355035; пр. Кулакова, д. 16/1; Ставрополь

Alexander I. Shchekin, Cand. Sci. (Engineering), Associate Professor

Department of Oil and Gas Field Development and Operation

355035; 16/1 Kulakov av.; Stavropol

ashchekin@ncfu.ruСеверо-Кавказский федеральный университетРоссияNorth-Caucasus Federal UniversityRussian Federation202530032025271275283Copyright © Щекин А.И., 20252025Щекин А.И.Shchekin A.I.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/296

  В трещиноватых коллекторах наличие трещин может оказывать как негативное, так и положительное влияние на технико-экономические показатели разработки месторождения, поэтому важным условием является ранняя классификация трещин. Идентификация типа трещиноватого коллектора на начальных стадиях разработки является ключом к подбору оптимального типа модели и системы разработки месторождения. В статье проведена оценка применимости показателя накопленной добычи и коэффициента продуктивности по скважинам для определения типа трещиноватого коллектора с использованием статистического метода анализа кривой Лоренца и коэффициента Джини (в применении к определению влияния трещин) при небольших выборках данных и на начальных стадиях разработки. Для исследования коэффициента влияния трещин при небольшом фонде скважин в статье использован метод математической статистики – бутстреп-метод. Этот метод основан на многократной генерации множества случайных выборок из исходных данных и на их последующем статистическом анализе. Моделирование выборок проводилось при помощи генератора случайных чисел в электронных таблицах. По результатам исследований установлено, что применение для идентификации трещиноватых коллекторов при небольшом количестве скважин таких показателей, как накопленная добыча и коэффициент продуктивности, показало сопоставимые результаты. Для повышения достоверности классификации при небольшом количестве скважин требуется выборка данных, которая будет наиболее полно описывать месторождение. Получить представительную выборку данных для объективного анализа распределения и влияния систем трещин возможно размещением скважин с охватом всей площади месторождения. На ранних стадиях разработки из-за незначительных объемов добываемой продукции и небольших сроков работы скважин для анализа рекомендовано использовать коэффициент продуктивности.

   The presence of fractures in reservoirs can have both a negative and a positive impact on the technical and economic indicators of field development and thus an early classification of fractures is an important requisite. Identifying the type of a fractured reservoir at the initial stages of development is a clue to selecting an optimal type of model and field development system. The paper evaluates the applicability of the cumulative production indicator and the productivity index of wells to determine the type of fractured reservoir with the help of a statistical method for analyzing the Lorenz curve and the Gini coefficient (as applied to determine the impact of fractures) with small data samples and at the initial stages of development. A method of mathematical statistics namely the bootstrap method is used in this paper in order to study the fracture impact coefficient for a small number of wells. This method is based on the repeated generation of random samples multitude from the original data set and their subsequent statistical analysis. Modeling of samples was carried out by means of a random number generator available in spreadsheets. The results of a research proved that the use of indicators such as cumulative production and productivity index to identify fractured reservoirs with a small number of wells produced the comparable results. To increase the reliability of classification for a small number of wells, a data sample is required that will most fully describe the field. It is possible to obtain a representative sample of data for an objective analysis of the distribution and influence of fracture systems by placing wells covering the entire area of the field. In the early stages of development, due to the low production volumes and short periods of well operation, it is recommended to use the productivity index for the analysis.

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