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Applied Sciences
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Development and Application of Intelligent Drilling Technology
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Development and Application of Intelligent Drilling Technology

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Energy Science and Technology".

Deadline for manuscript submissions: 20 May 2025 | Viewed by 6155

Special Issue Editors

Dr. Jingbin Li

E-Mail Website
Guest Editor
State Key Laboratory of Petroleum Resources and Prospecting, China University of Petroleum-Beijing, Beijing 102249, China
Interests: fluid mechanics and engineering of oil and gas wells
Dr. Mengmeng Zhou

E-Mail Website
Guest Editor
College of Carbon Neutral Energy, China University of Petroleum-Beijing, Beijing 102249, China
Interests: liquid-solid two-phase flow; particle settlement characteristics; cuttings transport; proppant transport; CFD-DEM coupling

Special Issue Information

Dear Colleagues,

With the rapid development of artificial intelligence, the application of machine learning in oil and gas drilling engineering is increasing day by day. Intelligent drilling technology based on machine learning is a revolutionary drilling technology that integrates big data, artificial intelligence, information engineering, underground control engineering, and other theories and technologies. Through the application of automated surface drilling rigs, intelligent downhole executive agency, intelligent monitoring, and decision-making technology, drilling operations can achieve advanced detection, closed-loop control, precision guidance, and intelligent decision-making. This can greatly improve the drilling efficiency and reservoir drilling rate, reduce the drilling cost, and significantly improve the production and recovery rate of complex oil and gas reservoirs.

This Special Issue will delve into the latest research, application cases, and future trends in machine learning technology in the field of drilling. We will invite professional researchers, engineers, and industry leaders from around the world to share their unique insights and experiences in the field.

Dr. Jingbin Li
Dr. Mengmeng Zhou
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

  • machine learning
  • drilling technology
  • intelligent well trajectory optimization
  • intelligent optimization of the drilling rate
  • intelligent guided drilling
  • downhole closed-loop control
  • intelligent monitoring and decision-making
  • intelligent rig
  • intelligent drilling pipe
  • intelligent bit
  • intelligent controlled pressure drilling
  • intelligent drilling fluid

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Published Papers (5 papers)

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Research

18 pages, 7440 KiB  
Article
Energy Consumption Prediction for Drilling Pumps Based on a Long Short-Term Memory Attention Method
by Chengcheng Wang, Zhi Yan, Qifeng Li, Zhaopeng Zhu and Chengkai Zhang
Appl. Sci. 2024, 14(22), 10750; https://doi.org/10.3390/app142210750 - 20 Nov 2024
Viewed by 276
Abstract
In the context of carbon neutrality and emission reduction goals, energy consumption optimization in the oil and gas industry is crucial for reducing carbon emissions and improving energy efficiency. As a key component in drilling operations, optimizing the energy consumption of drilling pumps [...] Read more.
In the context of carbon neutrality and emission reduction goals, energy consumption optimization in the oil and gas industry is crucial for reducing carbon emissions and improving energy efficiency. As a key component in drilling operations, optimizing the energy consumption of drilling pumps has significant potential for energy savings. However, due to the complex and variable geological conditions, diverse operational parameters, and inherent nonlinear relationships in the drilling process, accurately predicting energy consumption presents considerable challenges. This study proposes a novel Long Short-Term Memory Attention model for precise prediction of drilling pump energy consumption. By integrating Long Short-Term Memory (LSTM) networks with the Attention mechanism, the model effectively captures complex nonlinear relationships and long-term dependencies in energy consumption data. Comparative experiments with traditional LSTM and Convolutional Neural Network (CNN) models demonstrate that the LSTM-Attention model outperforms these models across multiple evaluation metrics, significantly reducing prediction errors and enhancing robustness and adaptability. The proposed model achieved Mean Absolute Error (MAE) values ranging from 5.19 to 10.20 and R2 values close to one (0.95 to 0.98) in four test scenarios, demonstrating excellent predictive performance under complex conditions. The high-precision prediction of drilling pump energy consumption based on this method can support energy optimization and provide guidance for field operations. Full article
(This article belongs to the Special Issue Development and Application of Intelligent Drilling Technology)
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16 pages, 9977 KiB  
Article
Study on Evaluation and Prediction for Shale Gas PDC Bit in Luzhou Block Sichuan Based on BP Neural Network and Bit Structure
by Ye Chen, Yu Sang, Xudong Wang, Xiaoke Ye, Huaizhong Shi, Pengcheng Wu, Xinlong Li and Chao Xiong
Appl. Sci. 2024, 14(11), 4370; https://doi.org/10.3390/app14114370 - 22 May 2024
Cited by 2 | Viewed by 770
Abstract
Deep and ultra-deep shale gas resources have great potential, but well drilling faces many challenges. The Polycrystalline Diamond Compact (PDC) bit has become the primary rock-breaking instrument for oil and gas drilling. Reasonable bit structure designs can promote rock-breaking efficiency and extend service [...] Read more.
Deep and ultra-deep shale gas resources have great potential, but well drilling faces many challenges. The Polycrystalline Diamond Compact (PDC) bit has become the primary rock-breaking instrument for oil and gas drilling. Reasonable bit structure designs can promote rock-breaking efficiency and extend service life. In this study, reverse modeling technology is used to analyze the structural characteristics of PDC bits collected in the field, and the influence of the structural characteristics of the bit at a specific interval on the rate of penetration (ROP) and drill footage is investigated using the Spearman rank correlation coefficient method. The number of blades, cutting angle of the cutters, crown rotation radius, internal cone angle, and diameter of the cutters are discovered to be the main structural characteristics that affect the ROP and footage of the bits, and the degree of influence varies depending on the formation conditions. The number of blades, crown rotation radius, inner cone angle, and cutting angle of the cutters have a significant impact on the ROP, whereas blade thickness, gauge length, gauge width, nozzle equivalent diameter have a significant impact on the bit footage. In addition, a back propagation (BP) neural network is utilized to build a prediction model of bit footage and ROP over a certain interval based on the structural characteristics of the bit. The goodness of fit of the model is greater than 85%, and its accuracy is high. Based on the usage of the bit, the evaluation and prediction of the bit can provide a reference for the structural design and optimization of the bit in a specific interval, guide the bit selection work, rationally plan the drilling operation, and reduce the drilling cost. Full article
(This article belongs to the Special Issue Development and Application of Intelligent Drilling Technology)
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16 pages, 3232 KiB  
Article
Analysis and Multi-Objective Optimization of the Rate of Penetration and Mechanical Specific Energy: A Case Study Applied to a Carbonate Hard Rock Reservoir Based on a Drill Rate Test Using Play-Back Methodology
by Diunay Zuliani Mantegazini, Andreas Nascimento, Vitória Felicio Dornelas and Mauro Hugo Mathias
Appl. Sci. 2024, 14(6), 2234; https://doi.org/10.3390/app14062234 - 7 Mar 2024
Cited by 1 | Viewed by 1218
Abstract
Until early 2006, in Brazil, the focus used to be on oil and gas exploration/exploitation of post-salt carbonates. This changed when the industry announced the existence of large fields in pre-salt layers across the South Atlantic Ocean from nearshore zones up to almost [...] Read more.
Until early 2006, in Brazil, the focus used to be on oil and gas exploration/exploitation of post-salt carbonates. This changed when the industry announced the existence of large fields in pre-salt layers across the South Atlantic Ocean from nearshore zones up to almost 350 [km] from the shore. With the discovery of pre-salt hydrocarbons reservoirs, new challenges appeared. One of the main challenges is the necessity to optimize the drilling processes due to their high operational costs. Drilling costs are considerably high, which leads the oil and gas industry to search for innovative and entrepreneurial methods. The coupling of the mechanical specific energy (MSE) and the rate of penetration (ROP) is a method that allows for the identification of ideal conditions to efficiently enhance the drilling process. In addition, the performance of the drilling process can be estimated through pre-operational tests, which consist in continuously testing the applied drilling mechanic parameters, such as the weight-on-bit (WOB) and drill string rotary speed (RPM), looking for optimum sets that would ultimately provide the most desirable ROP. Thus, the goal of this research was to analyze field data from pre-salt layer operations, using a multi-objective optimization based on the play-back methodology for pre-operational drilling tests, through the ideal combination of the highest ROP and the lowest MSE. The results showed that the new concept of pre-operational tests based on the MSE proved to be effective in the drilling process optimization. The combination of the highest ROP and the lowest MSE allows for a high-performance drilling process. For WOB intervals of 5 and 7 [klb], a good fit of the parameters was obtained. Through the parameters obtained from pre-operational tests, the eventual cost-saving and time-saving values could be estimated, respectively, ranging from USD 1,056,180 to 1,151,898 and 19.50 to 21.27 [h], respectively. In addition, the results of this research can be applied to the exploration of other natural resources, such as natural hydrogen and geothermal sources. Full article
(This article belongs to the Special Issue Development and Application of Intelligent Drilling Technology)
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16 pages, 1924 KiB  
Article
Prediction of Particle Settling Velocity in Newtonian and Power-Law Fluids Using Artificial Neural Network Model
by Weiping Lv, Zhengming Xu, Xia Jia, Shiming Duan, Jiawei Liu and Xianzhi Song
Appl. Sci. 2024, 14(2), 826; https://doi.org/10.3390/app14020826 - 18 Jan 2024
Cited by 1 | Viewed by 1164
Abstract
In petroleum engineering, accurately predicting particle settling velocity during various stages of a well’s life cycle is vital. This study focuses on settling velocities of both spherical and non-spherical particles in Newtonian and non-Newtonian fluids. Utilizing a dataset of 931 experimental observations, an [...] Read more.
In petroleum engineering, accurately predicting particle settling velocity during various stages of a well’s life cycle is vital. This study focuses on settling velocities of both spherical and non-spherical particles in Newtonian and non-Newtonian fluids. Utilizing a dataset of 931 experimental observations, an artificial neural network (ANN) model with a 7-42-1 architecture is developed (one input layer, one hidden layer with 42 neurons, and one output layer). This model effectively incorporates particle settling orientation and the inclusion of the settling area ratio, enhancing its predictive accuracy. Achieving an average absolute relative error (AARE) of 8.51%, the ANN model surpasses traditional empirical correlations for settling velocities in both Newtonian and power-law fluids. Key influencing factors, such as the consistency index and particle equivalent diameter, were identified. This approach in ANN model construction and data analysis represents a significant advancement in understanding particle dynamics. Full article
(This article belongs to the Special Issue Development and Application of Intelligent Drilling Technology)
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14 pages, 7553 KiB  
Article
Drilling Parameters Multi-Objective Optimization Method Based on PSO-Bi-LSTM
by Jianhua Wang, Zhi Yan, Tao Pan, Zhaopeng Zhu, Xianzhi Song and Donghan Yang
Appl. Sci. 2023, 13(21), 11666; https://doi.org/10.3390/app132111666 - 25 Oct 2023
Cited by 1 | Viewed by 1781
Abstract
The increasing exploration and development of complex oil and gas fields pose challenges to drilling efficiency and safety due to the presence of formations with varying hardness, abrasiveness, and rigidity. Consequently, there is a growing demand for drilling parameter optimization and speed-up technologies. [...] Read more.
The increasing exploration and development of complex oil and gas fields pose challenges to drilling efficiency and safety due to the presence of formations with varying hardness, abrasiveness, and rigidity. Consequently, there is a growing demand for drilling parameter optimization and speed-up technologies. However, existing models based on expert experience can only achieve single-objective optimization with limited accuracy, making real-time adaptation to changing drilling conditions and formation environments challenging. The emergence of artificial intelligence provides a new approach for optimizing drilling parameters. In this study, we introduce the Bi-directional Long Short-Term Memory (Bi-LSTM) deep learning algorithm with the attention mechanism to predict the rate of penetration (ROP). This algorithm improves the ROP prediction accuracy to 98.33%, ensuring reliable subsequent optimization results. Additionally, we propose a coupling optimization algorithm that combines Bi-LSTM with the particle swarm optimization algorithm (PSO) to enhance drilling efficiency through parameter optimization. Our approach aims to maximize drilling footage while maintaining the highest ROP. The optimal solutions obtained are verified through multi-parameter cloud image analysis, yielding consistent results. The application of our approach demonstrates an 81% increase in drilling speed and a 28% reduction in drill bit energy losses. Moreover, the real-time optimization results effectively guide field operations. Full article
(This article belongs to the Special Issue Development and Application of Intelligent Drilling Technology)
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