Результаты исследований: Публикации в книгах, отчётах, сборниках, трудах конференций › статья в сборнике материалов конференции › научная › Рецензирование
Seismic Inversion for Fracture Model Reconstruction: From 1D Inversion to Machine Learning. / Protasov, Maxim; Kenzhin, Roman; Dmitrachkov, Danil и др.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer Science and Business Media Deutschland GmbH, 2023. стр. 99-109 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Том 13957 LNCS).Результаты исследований: Публикации в книгах, отчётах, сборниках, трудах конференций › статья в сборнике материалов конференции › научная › Рецензирование
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TY - GEN
T1 - Seismic Inversion for Fracture Model Reconstruction: From 1D Inversion to Machine Learning
AU - Protasov, Maxim
AU - Kenzhin, Roman
AU - Dmitrachkov, Danil
AU - Pavlovskiy, Evgeniy
N1 - The work is supported by RSF grant 21-71-20002. The numerical results were obtained using the computational resources of Peter the Great Saint-Petersburg Polytechnic University Supercomputing Center (scc.spbstu.ru).
PY - 2023
Y1 - 2023
N2 - The presented paper is devoted to the numerical study of the applicability of 1D seismic inversion and 2D machine learning based inversion for fracture model reconstruction. Seismic inversion is used to predict reservoir properties. Standard version is based on a one-dimensional convolutional model, but real geological media are more complex, and therefore it is necessary to determine conditions where seismic inversion gives acceptable results. For this purpose, the work carries out a comparative analysis of one-dimensional and two-dimensional convolutional modeling. Also, machine learning methods have been adopted for 2D fracture model reconstruction. We use UNet architecture and 2D convolutional model to create a training dataset. We perform numerical experiments for a realistic synthetic model from Eastern Siberia and Sigsbee model.
AB - The presented paper is devoted to the numerical study of the applicability of 1D seismic inversion and 2D machine learning based inversion for fracture model reconstruction. Seismic inversion is used to predict reservoir properties. Standard version is based on a one-dimensional convolutional model, but real geological media are more complex, and therefore it is necessary to determine conditions where seismic inversion gives acceptable results. For this purpose, the work carries out a comparative analysis of one-dimensional and two-dimensional convolutional modeling. Also, machine learning methods have been adopted for 2D fracture model reconstruction. We use UNet architecture and 2D convolutional model to create a training dataset. We perform numerical experiments for a realistic synthetic model from Eastern Siberia and Sigsbee model.
KW - convolution model
KW - fractures
KW - machine learning
KW - seismic inversion
UR - https://www.scopus.com/record/display.uri?eid=2-s2.0-85165073290&origin=inward&txGid=1e7d10f5e0993b567b973ddb471c93e1
UR - https://www.mendeley.com/catalogue/60499ee5-6ce7-3158-8da0-a706957a485e/
U2 - 10.1007/978-3-031-36808-0_7
DO - 10.1007/978-3-031-36808-0_7
M3 - Conference contribution
SN - 9783031368073
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 99
EP - 109
BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PB - Springer Science and Business Media Deutschland GmbH
ER -
ID: 59126690