OMAE2020-18154 Continuous Drilling Sensor Data Reconstruction and Prediction via Recurrent Neural Networks

von Tunkiel, Andrzej T.
Veröffentlicht in ASME International Conference on Ocean, Offshore and Arctic Engineering (39. : 2020 : Online) Proceedings of the ASME 39th International Conference on Ocean, Offshore and Arctic Engineering - 2020 ; Volume 1: Offshore technology (2020)

OMAE2021-61927 Automated Iterative Gap Filling Method for Drilling Logs and Other Data Series

von Tunkiel, Andrzej T.
Veröffentlicht in ASME International Conference on Ocean, Offshore and Arctic Engineering (40. : 2021 : Online) Proceedings of ASME 2021 40th International Conference on Ocean, Offshore and Arctic Engineering (OMAE 2021) ; Volume 10: Petroleum technology (2021)

OMAE2020-18151 Drilling Dataset Exploration, Processing and Interpretation Using Volve Field Data

von Tunkiel, Anarzej T.
Veröffentlicht in ASME International Conference on Ocean, Offshore and Arctic Engineering (39. : 2020 : Online) Proceedings of the ASME 39th International Conference on Ocean, Offshore and Arctic Engineering - 2020 ; Volume 11: Petroleum technology (2020)

Exploration of Strategies to Improve Continual Learning From Irregular Sequential Drilling Data

von Pacis, Felix James
Veröffentlicht in ASME International Conference on Ocean, Offshore & Arctic Engineering (42. : 2023 : Melbourne) Proceedings of ASME 2023 42nd International Conference on Ocean, Offshore & Arctic Engineering (OMAE 2023) ; Volume 9: Offshore geotechnics, petroleum technology (2023)

Transfer Learning Approach to Prediction of Rate of Penetration in Drilling

von Pacis, Felix James
Veröffentlicht in ICCS (22. : 2022 : London) Computational science - ICCS 2022 ; Part 2 (2022)

OMAE2022-78427 Well Path Design Using Q-Learning Algorithms and Bezier Curves With Obstacles Avoidance

von Cao, Jie
Veröffentlicht in ASME International Conference on Ocean, Offshore & Arctic Engineering (41. : 2022 : Hamburg) Proceedings of ASME 2022 41st International Conference on Ocean, Offshore & Arctic Engineering (OMAE 2022) ; Volume 10: Petroleum technology (2022)

OMAE2022-79046 Rate of Penetration Prediction Using Quantile Regression Deep Neural Networks

von Ambrus, Adrian
Veröffentlicht in ASME International Conference on Ocean, Offshore & Arctic Engineering (41. : 2022 : Hamburg) Proceedings of ASME 2022 41st International Conference on Ocean, Offshore & Arctic Engineering (OMAE 2022) ; Volume 10: Petroleum technology (2022)