Том 337 № 2 (2026)
DOI https://doi.org/10.18799/24131830/2026/2/5002
Машинное обучение в седиментологических и фациальных реконструкциях нефтегазовых резервуаров: критический обзор внедрённых и перспективных методов
Актуальность. Предсказательная сила геологической модели напрямую определяет эффективность и экономическую целесообразность разработки нефтегазовых месторождений. Однако восстановление точной пространственной структуры резервуаров затруднено из-за дефицита керновых данных, неравномерной плотности скважинной информации и разнородности геолого-геофизических данных. В условиях цифровой трансформации нефтегазовой отрасли методы машинного обучения всё активнее интегрируются в геологическое моделирование, дополняя и усиливая классические геостатистические подходы, что открывает новые возможности для повышения точности фациальных и седиментологических реконструкций. Цель. Систематизировать мировой опыт применения методов машинного обучения в седиментологических и фациальных реконструкциях нефтегазовых резервуаров, выделив уже внедрённые решения и альтернативные технологии, находящиеся на ранней стадии освоения. Методы. Проведён анализ научно-исследовательских работ, охватывающий байесовские сети, вариационные автоэнкодеры, генеративно-состязательные модели, гибридные и классические геостатистические алгоритмы; зрелость технологий оценивалась по типу исходных данных, роли эксперта и метрикам валидации. Результаты и выводы. Выявлено, что методы машинного обучения выходят за рамки пространственной интерполяции: они выявляют сложные зависимости и восстанавливают сложные геологические зависимости и сценарии осадконакопления. На практике увеличивается применение генеративных и вероятностных графовых моделей, что подчёркивает важность разнотипных данных и геологического контекста. Несмотря на автоматизацию, участие эксперта остаётся критически важным, особенно в байесовских подходах. Основной проблемой остаётся недостаток размеченных обучающих выборок, однако байесовские методы демонстрируют устойчивость даже при ограниченных данных. Отмечена тенденция к использованию гибридных подходов, сочетающих преимущества машинного обучения и геостатистики, что подчёркивает сохраняющуюся важность доменных специалистов в процессе моделирования.
Ключевые слова:
геологическое моделирование, машинное обучение, фациальное моделирование, седиментология, байесовские сети, генеративные модели, альтернативные концепции резервуара
Библиографические ссылки:
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