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Applied Sciences
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Application of Machine Learning in Drilling Technology
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Application of Machine Learning in Drilling Technology

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Industrial Technologies".

Deadline for manuscript submissions: closed (20 May 2025) | Viewed by 3308

Special Issue Editor

Prof. Dr. Dan Sui

E-Mail Website
Guest Editor
Faculty of Science and Technology, Department of Energy and Petroleum Engineering, University of Stavanger, 4021 Stavanger, Norway
Interests: drilling automation; digitalization; AI; machine learning; data processing and analytics; modeling, optimization and simulation; control system design (model predictive control, PID, moving horizon estimation, Kalman filter); advanced drilling technologies; drilling event detection; geothermal drilling and energy

Special Issue Information

Dear Colleagues,

We are excited to announce a forthcoming Special Issue of the journal Applied Sciences dedicated to the "Application of Machine Learning in Drilling Technology." The aim of this Special Issue is to explore the intersection of cutting-edge machine learning techniques and the drilling industry, highlighting their potential to revolutionize drilling processes and enhance efficiency, safety, and sustainability.

Machine learning has emerged as a powerful tool with the potential to address complex challenges in drilling technology. From real-time data analytics, performance monitoring, anomaly detection and predictive maintenance to automation and decision support systems, the integration of machine learning algorithms promises to unlock new horizons in drilling technology.

We invite researchers, engineers, and experts in the field of drilling technology to contribute innovative research, case studies, and reviews that shed light on the application of machine learning in various aspects of drilling, including but not limited to the following:

  • Predictive maintenance;
  • Formation evaluation;
  • Drill bit design and optimization;
  • Drilling fluid management;
  • Wellbore stability;
  • Real-time monitoring, control and optimization;
  • Risk assessment and mitigation;
  • Health and safety in drilling operations;
  • Anomaly detection;
  • Decision making;
  • Intelligent drilling control systems;
  • Physics-informed ML systems;
  • Drilling data analytics and management;
  • Explainable artificially intelligent drilling systems.

By disseminating cutting-edge research in this Special Issue, we aim to foster collaboration, share best practices, and advance the adoption of machine learning techniques within the drilling industry. Submissions are now open, and we look forward to receiving your contributions.

Prof. Dr. Dan Sui
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • machine learning
  • drilling technology
  • automation
  • real-time monitoring, decision support
  • efficiency
  • safety

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Published Papers (2 papers)

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Research

14 pages, 4131 KiB  
Article
Research on the Prediction of Drilling Rate in Geological Core Drilling Based on the BP Neural Network
by Da Gong, Yutong Zu, Zheng Zhou, Mingrang Jia, Jiachen Liu and Yuanbiao Hu
Appl. Sci. 2024, 14(21), 9959; https://doi.org/10.3390/app14219959 - 31 Oct 2024
Viewed by 1066
Abstract
The development of automation and intelligence in geological core drilling is not yet mature. The selection and improvement of drilling parameters rely mainly on experience, and adjustments are often made after drilling by evaluating the core, which introduces a lag and reduces the [...] Read more.
The development of automation and intelligence in geological core drilling is not yet mature. The selection and improvement of drilling parameters rely mainly on experience, and adjustments are often made after drilling by evaluating the core, which introduces a lag and reduces the drilling efficiency. Therefore, this study first establishes a geological core drilling experiment platform to collect drilling data. Through the constructed geological core drilling experimental platform, the practical data at the drill bit can be directly obtained, solving the problem of the data from the surface equipment in the practical drilling differing from the practical data. Second, the back propagation (BP) algorithm is used to perform the ROP prediction, with weight on bit (WOB), torque (TOR), flow rate (Q), and rotation speed (RPM) as input parameters, and rate of penetration (ROP) as the output. Subsequently, correlation analysis is used to perform the feature parameter optimization, and the effects of bit wear and bit cutting depth on the experiment are considered. Finally, comparison with algorithms such as ridge regression, SVM and KNN shows that the ROP prediction model using the BP neural network has the highest prediction accuracy of 94.1%. The results provide a reference for ROP prediction and the automation of geological core drilling rigs. Full article
(This article belongs to the Special Issue Application of Machine Learning in Drilling Technology)
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14 pages, 3968 KiB  
Article
Enhancing Interpretability in Drill Bit Wear Analysis through Explainable Artificial Intelligence: A Grad-CAM Approach
by Lesego Senjoba, Hajime Ikeda, Hisatoshi Toriya, Tsuyoshi Adachi and Youhei Kawamura
Appl. Sci. 2024, 14(9), 3621; https://doi.org/10.3390/app14093621 - 25 Apr 2024
Cited by 4 | Viewed by 1699
Abstract
This study introduces a novel method for analyzing vibration data related to drill bit failure. Our approach combines explainable artificial intelligence (XAI) with convolutional neural networks (CNNs). Conventional signal analysis methods, such as fast Fourier transform (FFT) and wavelet transform (WT), require extensive [...] Read more.
This study introduces a novel method for analyzing vibration data related to drill bit failure. Our approach combines explainable artificial intelligence (XAI) with convolutional neural networks (CNNs). Conventional signal analysis methods, such as fast Fourier transform (FFT) and wavelet transform (WT), require extensive knowledge of drilling equipment specifications, which limits their adaptability to different conditions. In contrast, our method leverages XAI algorithms applied to CNNs to directly identify fault signatures from vibration signals. The signals are transformed into their frequency components and then employed as inputs to a CNN model, which is trained to detect patterns indicative of drill bit failure. XAI algorithms are then employed to generate attention maps, highlighting regions of interest in the CNN. By scrutinizing these maps, engineers can identify critical frequencies associated with drill bit failure, providing valuable insights for maintenance and optimization. This method offers a transparent and interpretable framework for analyzing vibration data, enabling informed decision-making and proactive maintenance strategies to enhance drilling efficiency and minimize downtime. The integration of XAI with CNNs facilitates a deeper understanding of the root causes of drill bit failure and improves overall drilling performance. Full article
(This article belongs to the Special Issue Application of Machine Learning in Drilling Technology)
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