Standard

3D Seismic Inversion for Fracture Model Reconstruction Based on Machine Learning. / Протасов, Максим Игоревич; Кенжин, Роман Мугарамович; Павловский, Евгений.

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. p. 105-117 8 (Lecture Notes in Computer Science (LNCS); Vol. 14389).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review

Harvard

Протасов, МИ, Кенжин, РМ & Павловский, Е 2023, 3D Seismic Inversion for Fracture Model Reconstruction Based on Machine Learning. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)., 8, Lecture Notes in Computer Science (LNCS), vol. 14389, Springer Science and Business Media Deutschland GmbH, pp. 105-117, 9th Russian Supercomputing Days International Conference, Москва, Russian Federation, 25.09.2023. https://doi.org/10.1007/978-3-031-49435-2_8

APA

Протасов, М. И., Кенжин, Р. М., & Павловский, Е. (2023). 3D Seismic Inversion for Fracture Model Reconstruction Based on Machine Learning. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 105-117). [8] (Lecture Notes in Computer Science (LNCS); Vol. 14389). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-49435-2_8

Vancouver

Протасов МИ, Кенжин РМ, Павловский Е. 3D Seismic Inversion for Fracture Model Reconstruction Based on Machine Learning. In 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. p. 105-117. 8. (Lecture Notes in Computer Science (LNCS)). doi: 10.1007/978-3-031-49435-2_8

Author

Протасов, Максим Игоревич ; Кенжин, Роман Мугарамович ; Павловский, Евгений. / 3D Seismic Inversion for Fracture Model Reconstruction Based on Machine Learning. 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. pp. 105-117 (Lecture Notes in Computer Science (LNCS)).

BibTeX

@inproceedings{8c633279bebf4f0d9336f73925275787,
title = "3D Seismic Inversion for Fracture Model Reconstruction Based on Machine Learning",
abstract = "The presented paper is devoted to the numerical study of the applicability of 3D inversion for fracture model reconstruction based on machine learning. In practice, geophysicists use seismic inversion for predicting reservoir properties. One-dimensional convolutional model lies in the basis of standard versions of inversion, but geology is more complex. That is why we provide implementation and investigation of the approach for 3D fracture model reconstruction based machine learning, which uses U-net neural network and 3D convolutional model. We provide numerical results for a realistic 3D synthetic fractured model from the North of Russia.",
keywords = "3D Convolutional Model, 3D Fractured Model, Machine Learning",
author = "Протасов, {Максим Игоревич} and Кенжин, {Роман Мугарамович} and Евгений Павловский",
note = "The presented research is supported and done within the scope of investigations of RSF grant 21-71-20002. We use the computational resources of Peter the Great Saint-Petersburg Polytechnic University Supercomputing Center (scc.spbstu.ru) to provide the numerical experiments and to obtain the numerical results.; 9th Russian Supercomputing Days International Conference, RuSCDays 2023 ; Conference date: 25-09-2023 Through 26-09-2023",
year = "2023",
doi = "10.1007/978-3-031-49435-2_8",
language = "English",
isbn = "978-303149434-5",
series = "Lecture Notes in Computer Science (LNCS)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "105--117",
booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
address = "Germany",

}

RIS

TY - GEN

T1 - 3D Seismic Inversion for Fracture Model Reconstruction Based on Machine Learning

AU - Протасов, Максим Игоревич

AU - Кенжин, Роман Мугарамович

AU - Павловский, Евгений

N1 - Conference code: 9

PY - 2023

Y1 - 2023

N2 - The presented paper is devoted to the numerical study of the applicability of 3D inversion for fracture model reconstruction based on machine learning. In practice, geophysicists use seismic inversion for predicting reservoir properties. One-dimensional convolutional model lies in the basis of standard versions of inversion, but geology is more complex. That is why we provide implementation and investigation of the approach for 3D fracture model reconstruction based machine learning, which uses U-net neural network and 3D convolutional model. We provide numerical results for a realistic 3D synthetic fractured model from the North of Russia.

AB - The presented paper is devoted to the numerical study of the applicability of 3D inversion for fracture model reconstruction based on machine learning. In practice, geophysicists use seismic inversion for predicting reservoir properties. One-dimensional convolutional model lies in the basis of standard versions of inversion, but geology is more complex. That is why we provide implementation and investigation of the approach for 3D fracture model reconstruction based machine learning, which uses U-net neural network and 3D convolutional model. We provide numerical results for a realistic 3D synthetic fractured model from the North of Russia.

KW - 3D Convolutional Model

KW - 3D Fractured Model

KW - Machine Learning

UR - https://www.scopus.com/record/display.uri?eid=2-s2.0-85182593509&origin=inward&txGid=919a5e69e2330e8f0265991250350114

UR - https://www.mendeley.com/catalogue/00ef7390-c335-3adb-95e1-040b80a9f219/

U2 - 10.1007/978-3-031-49435-2_8

DO - 10.1007/978-3-031-49435-2_8

M3 - Conference contribution

SN - 978-303149434-5

T3 - Lecture Notes in Computer Science (LNCS)

SP - 105

EP - 117

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

T2 - 9th Russian Supercomputing Days International Conference

Y2 - 25 September 2023 through 26 September 2023

ER -

ID: 59681178