Machine learning algorithms for real-time prediction of the sonic logs based on drilling parameters and downhole accelerometers
Author:
Affiliation:
1. Curtin University
2. Saudi Aramco
Publisher
Society of Exploration Geophysicists
Link
https://library.seg.org/doi/pdf/10.1190/segam2020-3427085.1
Reference10 articles.
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3. Gan, T., A. Kumar, M. Ehiwario, B. Zhang, C. Sembroski, O. de Jesus, O. Hoffmann, and Y. Metwally, 2019, Artificial intelligent logs for formation evaluation using case studies in Gulf of Mexico and trinidad and Tobago: Annual Technical Conference and Exhibition, SPE.
4. Hareland, G., and R. Nygaard, 2007, Calculating unconfined rock strength from drilling data: Proceedings of the 1st Canada-US Rock Mechanics Symposium – Rock Mechanics Meeting Society’s Challenges and Demands, 1717–1723, doi: 10.1201/NOE0415444019-c216.
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