Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.5
2.7
Journals
Applied Sciences
Special Issues
Application of Machine Learning in Drilling Technology
share announcement

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: 30 September 2024 | Viewed by 441

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

Published Papers (1 paper)

Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

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
Viewed by 266
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)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-1
Back to TopTop