New method prediction of rock mechanics parameters in drilling process based on machine learning-Reference-Cited by-同舟云学术

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New method prediction of rock mechanics parameters in drilling process based on machine learning

Published:2024-01-05 Issue: Volume: Page:1-24
ISSN:1091-6466
Container-title:Petroleum Science and Technology
language:en
Short-container-title:Petroleum Science and Technology

Author:

Wei Mingqiang¹^ORCID,Qiu Dongling¹,Yue Tao²,Duan Yonggang¹,Wang Heying³

Affiliation:

1. Petroleum School, Southwest Petroleum University, Chengdu, China

2. National Pipeline Corporation’s West-East Gas Transmission Company Nanjing Metering Research Center, Nanjing, China

3. Chengdu Xiyou Mingke Testing Technology Co., Ltd, Chengdu, China

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/10916466.2023.2298998

Reference27 articles.

1. Application of Neural Networks in the Prediction of Reservoir Hydrocarbon Mixture Composition From Production Data

2. Prediction of Gas Viscosity of Yemeni Gas Fields Using Machine Learning Techniques

3. Gamal H. A. Gowida S. Elkatatny A. Abdulraheem and S. Haidary. 2021. Generating the rock strength profile while drilling complex lithologies in real-time by employing artificial neural networks. Paper presented at the 55th U.S. Rock Mechanics/Geomechanics Symposium Virtual June 2021.ARMA-2021-1914

4. Data Mining and Analytics in the Process Industry: The Role of Machine Learning

5. Identification of Zones of Abnormal Pressures and Determination of the Mechanical Properties of the Rock Through Pseudo-Sonic and Pseudo-Density Logs in Conventional and Unconventional Reservoirs

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