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Applied Sciences
Special Issues
Trends and Prospects in Intelligent Drilling Technology
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Trends and Prospects in Intelligent Drilling Technology

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

Deadline for manuscript submissions: closed (21 November 2022) | Viewed by 5529

Special Issue Editors

Dr. Baochang Liu

E-Mail Website
Guest Editor
Department of Exploration and Drilling Engineering, Construction Engineering College, Jilin University, Changchun 130026, China
Interests: drilling technology
Dr. Qilong Xue

E-Mail Website
Guest Editor
School of Engineering and Technology, China University of Geosciences, Beijing 100083, China
Interests: dynamics of drillstring, measurement and control technology of drilling engineering

Special Issue Information

Dear Colleagues,

Even though the rapid progress in drilling technology has reduced drilling costs significantly in the past ten years, the cost of drilling is still more than that of other operations in the exploration and development of oil and gas. With the application of high-tech solutions for exploration and development, petroleum science and technology will gain informative, intelligent, and visual approaches in the upcoming years. Future drilling technology will be more precise, efficient, and intelligent; less costly; and more environmentally friendly. With the further development of new materials, detection controls, microelectronics, telecommunications, computers, robots, and ultramicrotechniques, new intelligent drilling technologies will emerge and become the main development direction of drilling technologies in the 21st century. Along with the continual development and integrative utilization of these technologies, new intelligent drilling technology will be realized and applied in the future.

This Special Issue is devoted to trends and prospects in intelligent drilling technology. Potential topics include but are not limited to:

  • New understandings of intelligent drilling technology;
  • The prospective intelligent drilling technology of tomorrow;
  • New trends in vibration and well trajectory control;
  • Measurement and data processing;
  • Measurement while drilling;
  • Information management systems;
  • Drilling fluid management and disposal.

Dr. Baochang Liu
Dr. Qilong Xue
Guest Editors

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

  • new understanding of intelligent drilling technology
  • a prospective intelligent drilling technology of tomorrow
  • new trends in vibration and well trajectory control
  • measurement and data processing
  • measurement while drilling
  • information management and systems
  • drilling fluid management & disposal

Published Papers (2 papers)

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Research

16 pages, 4070 KiB  
Article
Prediction of the Deformation of Aluminum Alloy Drill Pipes in Thermal Assembly Based on a BP Neural Network
by Xiaofeng Wang, Baochang Liu, Jiaqi Yun, Xueqi Wang and Haoliang Bai
Appl. Sci. 2022, 12(2), 757; https://doi.org/10.3390/app12020757 - 12 Jan 2022
Cited by 1 | Viewed by 1089
Abstract
The connection between the steel joint and aluminum alloy pipe is the weak part of the aluminum alloy drill pipe. Practically, the interference connection between the aluminum alloy rod and the steel joint is usually realized by thermal assembly. In this paper, the [...] Read more.
The connection between the steel joint and aluminum alloy pipe is the weak part of the aluminum alloy drill pipe. Practically, the interference connection between the aluminum alloy rod and the steel joint is usually realized by thermal assembly. In this paper, the relationship between the cooling water flow rate, initial heating temperature and the thermal deformation of the steel joint in interference thermal assembly was studied and predicted. Firstly, the temperature data of each measuring point of the steel joint were obtained by a thermal assembly experiment. Based on the theory of thermoelasticity, the analytical solution of the thermal deformation of the steel joint was studied. The temperature function was fitted by the least square method, and the calculated value of radial thermal deformation of the section was finally obtained. Based on the BP neural network algorithm, the thermal deformation of steel joint section was predicted. Besides, a prediction model was established, which was about the relationship between cooling water flow rate, initial heating temperature and interference. The magnitude of interference fit of steel joint was predicted. The magnitude of the interference fit of the steel joint was predicted. A polynomial model, exponential model and Gaussian model were adopted to predict the sectional deformation so as to compare and analyze the predictive performance of a BP neural network, among which the polynomial model was used to predict the magnitude of the interference fit. Through a comparative analysis of the fitting residual (RE) and sum of squares of the error (SSE), it can be known that a BP neural network has good prediction accuracy. The predicted results showed that the error of the prediction model increases with the increase of the heating temperature in the prediction model of the steel node interference and related factors. When the cooling water velocity hit 0.038 m/s, the prediction accuracy was the highest. The prediction error increases with the increase or decrease of the velocity. Especially when the velocity increases, the trend of error increasing became more obvious. The analysis shows that this method has better prediction accuracy. Full article
(This article belongs to the Special Issue Trends and Prospects in Intelligent Drilling Technology)
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20 pages, 8008 KiB  
Article
Measurement and Analysis of Downhole Drill String Vibration Signal
by Yafeng Li, Jin Wang, Yonggang Shan, Chong Wang and Yuanbiao Hu
Appl. Sci. 2021, 11(23), 11484; https://doi.org/10.3390/app112311484 - 3 Dec 2021
Cited by 8 | Viewed by 3412
Abstract
Downhole drill string vibration data can provide an effective reference for research drill string vibration during drilling. In this paper, the research group used a storage-type downhole vibration measurement equipment equipped with an orthogonal, three-axis accelerometer to measure and collect drill string vibration [...] Read more.
Downhole drill string vibration data can provide an effective reference for research drill string vibration during drilling. In this paper, the research group used a storage-type downhole vibration measurement equipment equipped with an orthogonal, three-axis accelerometer to measure and collect drill string vibration signals during drilling in an oil well. Based on the installation characteristics of the sensor, the relationship between the acceleration measurement value of the sensor and the center acceleration value of the drill string is obtained. Then the time-domain signals representative of the vibration in igneous rock drilling is analyzed. It can be found that the occurrence of stick-slip vibration can be judged by the regular wave packets in the time-domain signal, while the time-domain signal of whirl is disorderly. The main frequency of stick-slip vibration in the low-frequency band is 0.1221 Hz and the period of stick-slip vibration is very close to 10 s through Fast Fourier (FFT) and Short-time Fourier transform (DTFT) methods. In the process of whirling, two frequencies, respectively, 0.05341 Hz and 155.5 Hz, play a major role. The frequency 0.05341 Hz is very close to the reciprocal of the period of 20 s when the peak energy spectrum density appears, indicating that the occurrence of whirl is very likely to be related to the natural frequency of the drilling tool. Through further time-frequency analysis, it also can be found that the occurrence of whirl and stick-slip is greatly related to the use of torsional impactors and jars. Full article
(This article belongs to the Special Issue Trends and Prospects in Intelligent Drilling Technology)
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