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Deep Learning for Anisotropy Parameters Estimation in Oil/Gas Fractured Reservoirsстатья Тезисы
- Авторы: Sabinin G., Chichinina T., Tulchinsky V.
- Сборник: Conference Proceedings, 82nd EAGE Annual Conference & Exhibition Workshop Programme
- Том: 2020
- Год издания: 2020
- Издательство: European Association of Geoscientists and Engineers
- Местоположение издательства: Netherlands
- Первая страница: 1
- Последняя страница: 5
- Аннотация: We study the applicability of Deep Learning in solving the problem of estimating the fractured medium parameters, represented as anisotropy parameters of a transversely isotropic model (HTI), using synthetic seismic data. Normal and tangential weaknesses of fractures Δ_Ν and Δ_Τ, the Thomsen anisotropy parameters ε, δ, γ, the crack density and the aspect ratio (crack opening) are considered. We develop a neural network model for solving this problem. Trained on synthetic seismograms, it provides quite accurate results. The effectiveness of Deep Learning for the inverse problem is demonstrated. The prospects for the development of this method for more complex rock-physics models are outlined.
- Добавил в систему: Сабинин Григорий Владимирович