Deep learning network optimization and hyperparameter tuning for seismic lithofacies classification

Autor: Robert Smith, Mingliang Liu, Michael Jervis

Publikováno v: The Leading Edge. 40:514-523

Deep learning is increasingly being applied in many aspects of seismic processing and interpretation. Here, we look at a deep convolutional neural network approach to multiclass seismic lithofacies characterization using well logs and seismic data. I

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::7979c4d1d1581a520c10b1bd28a7d932
https://doi.org/10.1190/tle40070514.1

A deep learning framework for seismic facies classification

Autor: Harpreet Kaur, Nam Pham, Sergey Fomel, Zhicheng Geng, Luke Decker, Ben Gremillion, Michael Jervis, Ray Abma, Shuang Gao

Publikováno v: First International Meeting for Applied Geoscience & Energy Expanded Abstracts.

We have proposed a deep neural network-based framework for seismic facies classification. We implement two different neural networks based on the architectures of DeepLabv3+ and generative adversarial network for segmentation and compare the mapping

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1618069c0ecd6ceeb25c297599e71d23
https://doi.org/10.1190/segam2021-3583072.1

List of contributors

Autor: Ghunaim T. Alanezi, Khalid Aldamegh, Anatoly S. Alekseev, Hassan Ali AlJanobi, Fahad Almalki, Hussain AlNasser, Abdullah Alramadhan, Khaled AlYousef, Andrey Bakulin, Andrey S. Belonosov, Noel Black, Lyudmila P. Braginskaya, Vladimir Burtman, Eva Caspari, Julia Correa, Leif Cox, Martin uma, Tom Daley, Tess Dance, S.A. Elagin, A.F. Emanov, Alexey G. Fatyanov, Barry Freifeld, Naoyuki Fujii, Boris M. Glinsky, Stanislav Glubokovskikh, Andrew Greenwood, Andrey P. Grigoryuk, Boris Gurevich, Muran Han, Yoko Hasada, Jan Henninges, Ryoya Ikuta, Tomohiko Ise, Kiyoshi Ito, Shinji Ito, Monika Ivandic, Michael Jervis, Christopher Juhlin, Shiori Kamei, Aya Kamimura, Dmitriy A. Karavaev, Junzo Kasahara, V.N. Kashun, Akio Katsumata, Shozo Kawamura, Thomas Kempka, Anton Kepic, Marat S. Khairetdinov, Valery V. Kovalevsky, Takahiro Kunitomo, Omar Lafouza, A.V. Liseikin, Stefan Lüth, Alberto Marsala, Masami Matsubara, Jun Matsushima, Hitoshi Mikada, Kei Murase, Hiromichi Nagao, Takahiro Nakajima, Osamu Nishizawa, Ben Norden, Mitsuyoshi Osaki, V.P. Panchenko, Roman Pevzner, Dmitry Popik, Sofya Popik, Matthias Raab, Dennis Rippe, Michelle Robertson, Cornelia Schmidt-Hattenberger, Galina F. Sedukhina, V.S. Seleznev, Naotaka Shigeta, Gulnara M. Shimanskaya, Gyulnara M. Shimanskaya, Valeriya Shulakova, Rajindar Singh, Robert Smith, V.M. Solovyev, David Sunwall, Junichi Takekawa, Konstantin Tertyshnikov, Gennady M. Tsibulchik, Kayoko Tsuruga, Tsyren A. Tubanov, Takumi Ueda, Takemichi Ueki, Milovan Urosevic, E.P. Velikhov, Le Wan, Shuming Wang, Toshiki Watanabe, Max Watson, Ziqiu Xue, Alexander A. Yakimenko, Koshun Yamaoka, Sinem Yavuz, Yasuhiro Yoshida, V.I. Yushin, Michael S. Zhdanov, Sasha Ziramov

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::bd4ef2465c02f163047cac775a77ccb1
https://doi.org/10.1016/b978-0-08-102684-7.00036-4

Making time-lapse seismic work in a complex desert environment for CO2 EOR monitoring — Design and acquisition

Autor: Michael Jervis, Andrey Bakulin, Robert Smith

Publikováno v: The Leading Edge. 37:598-606

Onshore seismic monitoring for CO2 injection in carbonate reservoirs in the Middle East is a major challenge for many reasons. The 4D signal is generally much smaller than that of clastic or chalk reservoirs due to the high bulk moduli of the rocks a

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::9df631a7adda36304cdaa5e31018ec72
https://doi.org/10.1190/tle37080598.1

3D seismic facies classification using convolutional neural network and semi-supervised generative adversarial network

Autor: Michael Jervis, Philippe Nivlet, Mingliang Liu, Weichang Li

Publikováno v: SEG Technical Program Expanded Abstracts 2019.

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::89b17a6efb1ef123bc8b752cdc03b9ac
https://doi.org/10.1190/segam2019-3216797.1