Bayesian deep learning and evolutionary algorithms applied to drilling optimization: an approach combining long short-term memory and genetic algorithms

Author:

De Almeida Rafael ValladaresORCID,Jacinto Marcos Vinícius GomesORCID,De Medeiros Gilvandro César,De Montalvão Leonardo Carvalho,Bassani Gabriel Soares

Publisher

Instituto Brasileiro de Petroleo e Gas

Reference12 articles.

1. Alsubaih, A., Albadran, F., & Alkanaani, N. (2018). Mechanical Specific Energy and Statistical Techniques to Maximizing the Drilling Rates for Production Section of Mishrif Wells in Southern Iraq Fields (pp. 1–16). Presented at the SPE/IADC Middle East Drilling Technology Conference and Exhibition, Abu Dhabi: Society of Petroleum Engineers. Retrieved from https://www.onepetro.org/conference-paper/SPE-189354-MS

2. Amadi, W. K., & Iyalla, I. (2012). Application of Mechanical Specific Energy Techniques in Reducing Drilling Cost in Deepwater Development (pp. 1–10). Presented at the SPE Deepwater Drilling and Completions Conference, Galveston, Texas: Society of Petroleum Engineers. Retrieved from https://www.onepetro.org/conference-paper/SPE-156370-MS

3. Bello, O., Holzmann, J., Yaqoob, T., & Teodoriu, C. (2015). 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. http://dx.doi.org/10.1515/jaiscr-2015-0024

4. Dupriest, F. E., & Koederitz, W. L. (2005). Maximizing Drill Rates with Real-Time Surveillance of Mechanical Specific Energy (pp. 1–10). Presented at the SPE/IADC Drilling Conference, Amsterdam: Society of Petroleum Engineers. Retrieved from https://www.onepetro.org/conference-paper/SPE-92194-MS

5. Ertel, W. (2017). Introduction to Artificial Intelligence. Springer. Retrieved from https://link.springer.com/book/10.1007/978-3-319-58487-4

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