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2024 | OriginalPaper | Buchkapitel

Optimized Drilling Status Recognition for Oil Drilling Using Artificial Intelligence: Empirical Research and Methodology

verfasst von : Xin-yi Yang, Meng Cui, Yan-long Zhang, Ling-zhi Jing, Yong Ji, Xiao-yan Shi

Erschienen in: Proceedings of the International Field Exploration and Development Conference 2023

Verlag: Springer Nature Singapore

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Abstract

This study focuses on the application of modern artificial intelligence (AI) techniques to improve the accuracy of drilling status recognition in the oil drilling industry, with the aim of enhancing safety and efficiency. To address the limitations of existing research in evaluation methods and practical applications, we constructed a unified drilling status dataset, introduced a more scientific evaluation criterion, the F1 score, and conducted a comprehensive evaluation and improvement of existing oil drilling status recognition methods. This paper provides an in-depth analysis of various drilling status characteristics and explores the applicability and limitations of different AI algorithms in drilling status recognition. Based on these findings, we propose optimized general drilling status recognition algorithms and validate the performances in real drilling status. Our research offers valuable insights and guidance for future oil drilling status recognition studies and is expected to promote safer and more efficient development in the oil drilling industry.

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Metadaten
Titel
Optimized Drilling Status Recognition for Oil Drilling Using Artificial Intelligence: Empirical Research and Methodology
verfasst von
Xin-yi Yang
Meng Cui
Yan-long Zhang
Ling-zhi Jing
Yong Ji
Xiao-yan Shi
Copyright-Jahr
2024
Verlag
Springer Nature Singapore
DOI
https://doi.org/10.1007/978-981-97-0272-5_15