Machine learning as a service and MLOps technologies applied in a cloud computing system: an innovative infrastructure for data-driven mud logging applications-Reference-Cited by-同舟云学术

Machine learning as a service and MLOps technologies applied in a cloud computing system: an innovative infrastructure for data-driven mud logging applications

Published:2022-09-26 Issue:2022 Volume:22 Page:297-298
ISSN:2525-7579
Container-title:Rio Oil and Gas Expo and Conference
language:
Short-container-title:ROG

Author:

Jacinto Marcos Vinícius Gomes^ORCID,De Medeiros Gilvandro César^ORCID,De Montalvão Leonardo Carvalho,Bassani Gabriel Soares^ORCID,De Oliveira Luiz Henrique Lira^ORCID,Silva Mateus de Assis,Medeiros Diego Rodrigues

Publisher

Instituto Brasileiro de Petroleo e Gas

Subject

Metals and Alloys,Mechanical Engineering,Mechanics of Materials

Reference15 articles.

1. Bello, O., Holzmann, J., Yaqoob, T., & Teodoriu, C. (2020). BELLO, Opeyemi et al. Application of artificial intelligence methods in drilling system design and operations: a review of the state of the art. Journal of Artificial Intelligence and Soft Computing Research, 5(2), 121–139. https://doi.org/10.1515/jaiscr-2015-0024

2. Blue, D., Blakey, T., & Rowe, M. (2019). Advanced Mud Logging: Key to Safe and Efficient Well Delivery. Offshore Technology Conference, 12. https://doi.org/10.4043/29469-MS

3. Google. (n.d.). Google Cloud Solutions. Retrieved March 1, 2022, from https://cloud.google.com/solutions#industry-solutions

4. Google. (2018). Google Cloud Security Whitepapers. https://services.google.com/fh/files/misc/security_whitepapers_march2018.pdf

5. Jacinto, M. V. G., Medeiros, G. C., Montalvão, L. C., Bassani, G. S., & Almeida, R. V. (2020). Bayesian deep learning and evolutionary algorithms applied to drilling optimization: an approach combining long short-term memory and genetic algorithms. 20, 415. https://doi.org/10.48072/2525-7579.rog.2020.415