Standard

Seismic Inversion for Fracture Model Reconstruction: From 1D Inversion to Machine Learning. / Protasov, Maxim; Kenzhin, Roman; Dmitrachkov, Danil et al.

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. 99-109 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13957 LNCS).

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

Harvard

Protasov, M, Kenzhin, R, Dmitrachkov, D & Pavlovskiy, E 2023, Seismic Inversion for Fracture Model Reconstruction: From 1D Inversion to Machine Learning. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 13957 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 99-109. https://doi.org/10.1007/978-3-031-36808-0_7

APA

Protasov, M., Kenzhin, R., Dmitrachkov, D., & Pavlovskiy, E. (2023). Seismic Inversion for Fracture Model Reconstruction: From 1D Inversion to Machine Learning. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 99-109). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13957 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-36808-0_7

Vancouver

Protasov M, Kenzhin R, Dmitrachkov D, Pavlovskiy E. Seismic Inversion for Fracture Model Reconstruction: From 1D Inversion to 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. 99-109. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-031-36808-0_7

Author

Protasov, Maxim ; Kenzhin, Roman ; Dmitrachkov, Danil et al. / Seismic Inversion for Fracture Model Reconstruction: From 1D Inversion to 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. 99-109 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

BibTeX

@inproceedings{ff82e2f28c9247ec9aa79c9a8bdb574e,
title = "Seismic Inversion for Fracture Model Reconstruction: From 1D Inversion to Machine Learning",
abstract = "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.",
keywords = "convolution model, fractures, machine learning, seismic inversion",
author = "Maxim Protasov and Roman Kenzhin and Danil Dmitrachkov and Evgeniy Pavlovskiy",
note = "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).",
year = "2023",
doi = "10.1007/978-3-031-36808-0_7",
language = "English",
isbn = "9783031368073",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "99--109",
booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
address = "Germany",

}

RIS

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