Research on adaptive prediction model of rate of penetration under dynamic formation conditions-Reference-Cited by-同舟云学术

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Research on adaptive prediction model of rate of penetration under dynamic formation conditions

Published:2024-07 Issue: Volume:133 Page:108281
ISSN:0952-1976
Container-title:Engineering Applications of Artificial Intelligence
language:en
Short-container-title:Engineering Applications of Artificial Intelligence

Author:

Yin Hu^ORCID,Zhao Xiuwen,Li Qian

Funder

Sichuan Province Department of Science and Technology

Ministry of Science and Technology of the People's Republic of China

Natural Science Foundation of Sichuan Province

National Key Research and Development Program of China

Publisher

Elsevier BV

Reference29 articles.

1. Prediction of the rate of penetration while drilling horizontal carbonate reservoirs using the self-adaptive artificial neural networks technique;Al-Abduljabbar;Sustainability-Basel,2020

2. Artificial neural network model for real-time prediction of the rate of penetration while horizontally drilling natural gas-bearing sandstone formations;Al-Abduljabbar;Arabian J. Geosci.,2021

3. Hybrid data driven drilling and rate of penetration optimization;Alali;J. Petrol. Sci. Eng.,2021

4. Modeling of drilling rate of penetration using adaptive neuro-fuzzy inference system;Ayoub;Int. J. Appl. Eng. Res.,2017

5. Machine learning methods applied to drilling rate of penetration prediction and optimization-a review;Barbosa;J. Petrol. Sci. Eng.,2019

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Research on adaptive feature optimization and drilling rate prediction based on real-time data;Geoenergy Science and Engineering;2024-11

2. Improvement of Rate of Penetration in Drilling Process Based on TCN-Vibration Recognition;IEEE Transactions on Instrumentation and Measurement;2024

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