Application of Convolutional Neural Networks for Dynamic Digital Oil Field Models

Author:

Korovin Iakov S.,Sisyukin Sergey,Schaefer Gerald

Publisher

Springer Singapore

Reference15 articles.

1. Ya. S. Korovin, M. G. Tkachenko, S. V. Kononov (2012) Oilfield equipment’s state diagnostics on the basis of data mining technologies (in Russian), Neftyanoe khozyaystvo - Oil Industry, No. 9, pp. 116–118.

2. Ya. S. Korovin, M. G. Tkachenko (2010) Method of calculation of coordinates of height of products in system of contactless definition of raznovysotnosti tvs of the active zone of the reactor (in Russian), Izvestiya Yuzhnogo Federal’nogo Universiteta. Tekhnicheskie nauki - Izvestiya SFedU. Engineering sciences, Vol. 113, No. 12, pp. 172–178.

3. Ya. S. Korovin, M. V. Khisamutdinov, M. G. Tkachenko (2013) Forecasting of oilfield equipment work conditions with the application of evolutionary algorithms and artificial neural networks (in Russian), Neftyanoe khozyaystvo - Oil Industry, no. 12, pp. 128–133.

4. R. Feraud, F. Clerot, J. L. Simon, D. Pallou, C. Labbe, S. Martin (2000): Kalman and neural network approaches for the control of a VP bandwidth in an ATM network, Springer Lecture Notes in Computer Science, Vol. 1815, pp. 655.

5. CIFAR-10 – Object Recognition in Images. https://www.kaggle.com/c/cifar-10

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1. Real-Time Monitoring of Oilfield Operations with Deep Neural Networks;2024 2nd International Conference on Advancement in Computation & Computer Technologies (InCACCT);2024-05-02

2. Development and research of the ANN based model for increasing oil production;Proceedings of the International Conference on Industrial Control Network and System Engineering Research - ICNSER2019;2019

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