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Processes
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Coal Mining and Unconventional Oil Exploration
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Coal Mining and Unconventional Oil Exploration

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Energy Systems".

Deadline for manuscript submissions: closed (20 July 2024) | Viewed by 5226

Special Issue Editors

Dr. Minghui Li

E-Mail Website
Guest Editor
Institute of Deep Earth Sciences and Green Energy, Shenzhen University, Shenzhen 518060, China
Interests: deep rock mechanics and engineering; deep earth resource exploitation; advanced experimental techniques in geomechanics
Dr. Xiting Long

E-Mail Website
Guest Editor
Institute of Deep Earth Sciences and Green Energy, College of Civil and Transportation Engineering, Shenzhen University, Shenzhen 518060, China
Interests: geological exploration; geothermal resource development

Special Issue Information

Dear Colleagues,

This Special Issue, "Coal Mining and Unconventional Oil Exploration", provides an in-depth examination of the latest advancements and research findings in the field of coal mining and unconventional oil extraction. The featured articles delve into a wide array of subjects, including the mechanical behavior of rocks, underlining how this understanding influences the design and implementation of mining and drilling operations, novel extraction technologies, environmental impacts, and the modeling of mining processes. New developments in coal mining techniques are scrutinized, with particular attention paid to their efficiency, safety, and environmental consequences. The exploration of unconventional oil resources, such as shale oil and gas, is also thoroughly investigated. Research in this area discusses innovative exploration technologies and potential environmental effects. Overall, this Special Issue serves as a comprehensive resource for researchers, policymakers, and industry professionals seeking to understand and contribute to the evolving landscape of coal mining and unconventional oil exploration.

Dr. Minghui Li
Dr. Xiting Long
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. Processes is an international peer-reviewed open access monthly 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

  • rock mechanics under in situ geological conditions
  • coal mining
  • unconventional oil and gas
  • deep high geo-stress
  • rock failure
  • deep engineering
  • hydraulic fracturing

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Published Papers (5 papers)

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Research

14 pages, 14355 KiB  
Article
An Automated Quantitative Methodology for Computing Gravel Parameters in Imaging Logging Leveraging Deep Learning: A Case Analysis of the Baikouquan Formation within the Mahu Sag
by Liang Wang, Jing Lu, Yang Luo, Benbing Ren, Angxing Li and Ning Zhao
Processes 2024, 12(7), 1337; https://doi.org/10.3390/pr12071337 - 27 Jun 2024
Cited by 1 | Viewed by 679
Abstract
Gravels are widely distributed in the Baikouquan formation in the Mabei area of the Junggar Basin. However, conventional logging methods cannot quantitatively characterize gravel development, which limits the identification of lithology, structure, and sedimentary facies in this region. This study proposes a new [...] Read more.
Gravels are widely distributed in the Baikouquan formation in the Mabei area of the Junggar Basin. However, conventional logging methods cannot quantitatively characterize gravel development, which limits the identification of lithology, structure, and sedimentary facies in this region. This study proposes a new method for automatically identifying gravels from electric imaging images and calculating gravel parameters utilizing the salient object detection (SOD) network. Firstly, a SOD network model (U2-Net) was constructed and trained using electric imaging data from the Baikouquan formation at the Mahu Sag. The blank strips in the images were filled using the U-Net convolutional neural network model. Sample sets were then prepared, and the gravel areas were labeled in the electric imaging images with the Labelme software in combination with image segmentation and human–machine interaction. These sample sets were used to train the network model, enabling the automatic recognition of gravel areas and the segmentation of adhesive gravel regions in the electric imaging images. Based on the segmented gravel results, quantitative evaluation parameters such as particle size and gravel quantity were accurately calculated. The method’s validity was confirmed through validation sets and actual data. This approach enhances adhesive area segmentation’s accuracy and processing speed while effectively reducing human error. The trained network model demonstrated an average absolute error of 0.0048 on test sets with a recognition accuracy of 83.7%. This method provides algorithmic support for the refined evaluation of glutenite reservoir logging. Full article
(This article belongs to the Special Issue Coal Mining and Unconventional Oil Exploration)
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16 pages, 8051 KiB  
Article
Experimental Analysis of the Mechanical Properties and Failure Behavior of Deep Coalbed Methane Reservoir Rocks
by Haiyang Wang, Shugang Yang, Linpeng Zhang, Yunfeng Xiao, Xu Su, Wenqiang Yu and Desheng Zhou
Processes 2024, 12(6), 1125; https://doi.org/10.3390/pr12061125 - 30 May 2024
Viewed by 668
Abstract
A comprehensive understanding of the mechanical characteristics of deep coalbed methane reservoir rocks (DCMRR) is crucial for the safe and efficient development of deep coalbed gas resources. In this study, the microstructural and mechanical features of the coal seam roof, floor, and the [...] Read more.
A comprehensive understanding of the mechanical characteristics of deep coalbed methane reservoir rocks (DCMRR) is crucial for the safe and efficient development of deep coalbed gas resources. In this study, the microstructural and mechanical features of the coal seam roof, floor, and the coal seam itself were analyzed through laboratory experiments. The impact mechanisms of drilling fluid and fracturing fluid hydration on the mechanical properties and failure behavior of coal seam rocks were investigated. The experimental results indicate that the main minerals in coal seams are clay and amorphous substances, with kaolinite being the predominant clay mineral component in coal seam rocks. The rock of the coal seam roof and floor exhibits strong elasticity and high compressive strength, while the rock in the coal seam section shows a lower compressive capacity with pronounced plastic deformation characteristics. The content of kaolinite shows a good correlation with the mechanical properties of DCMRR. As the kaolinite content increases, the strength of DCMRR gradually decreases, and deformability enhances. After immersion in drilling fluid and slickwater, the strength of coal seam rocks significantly decreases, leading to shear fracture zones and localized strong damage features after rock compression failure. The analysis of the mechanical properties of DCMRR suggests that the horizontal well trajectory should be close to the coal seam roof, and strong sealing agents should be added to drilling fluid to reduce the risk of wellbore collapse. Enhancing the hydration of slickwater is beneficial for the formation of a more complex fracture network in deep coalbed methane reservoir. Full article
(This article belongs to the Special Issue Coal Mining and Unconventional Oil Exploration)
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22 pages, 11485 KiB  
Article
Spatio-Temporal Evolution of Loading and Deformation of Surface Gas Pipelines for High-Intensity Coalbed Mining and Its Integrity Prediction Methodology
by Yingnan Xu, Shun Liang, Xu Liang, Biao Yang, Zhuolin Shi, Chengle Wu, Jinhang Shen, Miao Yang, Yindou Ma and Pei Xu
Processes 2024, 12(1), 213; https://doi.org/10.3390/pr12010213 - 18 Jan 2024
Cited by 2 | Viewed by 1158
Abstract
In recent years, the integrity of the gas pipeline in the coal-gas co-mining subsidence area has become a critical problem, restricting the safe and efficient mining of coal resources. This paper establishes a theoretical model for the safety prediction of gas pipelines in [...] Read more.
In recent years, the integrity of the gas pipeline in the coal-gas co-mining subsidence area has become a critical problem, restricting the safe and efficient mining of coal resources. This paper establishes a theoretical model for the safety prediction of gas pipelines in mining subsidence areas based on elastic free theory, constructs a 3D model of pipe-sand soil by using ABAQUS simulation software (2021), analyzes the characteristics of ground surface and pipeline settlement combined with the measured data on-site, and reveals the temporal and spatial evolution law of the pipeline load and deformation under the condition of diagonal intersections of the pipeline and high-strength mining working face. The results show that during the mining cycle, the pipe and the sandy soil body experienced the stage of cooperative deformation, the stage of increasing non-cooperative deformation, and the stage of weakening non-cooperative deformation; the pipe body is most vulnerable to yield failure in the circumferential direction of 180°, 45°, 225°, and 0°; the relative deformation rate of the pipe experienced a slow and rapid increase in the stage, and tends to flatten out when the advancement length is about 1.5–2 times the distance at the taken cross-section. The study’s results are conducive to accurately predicting the pipe failure orientation under high-intensity mining conditions in coal seams, improving the diagnostic efficiency of pipes, and optimizing the advancement speed of the working face. Full article
(This article belongs to the Special Issue Coal Mining and Unconventional Oil Exploration)
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22 pages, 16835 KiB  
Article
Effect of Non-Uniform Minerals Distribution on Hydraulic Fracture Evolution during Unconventional Geoenergy Exploration
by Ziqi Gao, Ning Li, Jiahui Tu and Liu Yang
Processes 2023, 11(11), 3200; https://doi.org/10.3390/pr11113200 - 9 Nov 2023
Viewed by 997
Abstract
To study the effect of the non-uniform distribution of minerals on the development of microcracks within the rock during hydraulic fracturing, a novel numerical model considering multiple random mineral distributions was designed. The model investigated the impacts of mineral grain size, composition, and [...] Read more.
To study the effect of the non-uniform distribution of minerals on the development of microcracks within the rock during hydraulic fracturing, a novel numerical model considering multiple random mineral distributions was designed. The model investigated the impacts of mineral grain size, composition, and spatial arrangement on fracture initiation and propagation. The results indicate that the presence of the hard-phase mineral quartz can alter the propagation path of fractures, and increase the width of hydraulic fractures. In coarse-grained granite, the range of crack deflection is maximized, while in medium-grained granite, it is more prone to forming convoluted elongated cracks. A higher quartz content in granite further contributes to the formation of complex crack networks. Simultaneously, the evolution of granite fractures and variations in breakdown pressure in heterogeneous granite were investigated, considering the influence of core parameters such as fluid injection rate, fracturing fluid viscosity, and horizontal stress difference. The research reveals that a high injection rate promotes straight-line fracture expansion. Moreover, modest fluctuations in fracturing fluid viscosity have minimal effects on fracture morphology. When the fracture development avoids quartz, under the influence of high horizontal stress differential, it clearly turns toward the direction of the maximum principal stress. This study can offer insights into innovative and optimized deep reservoir fracturing techniques. Full article
(This article belongs to the Special Issue Coal Mining and Unconventional Oil Exploration)
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22 pages, 6039 KiB  
Article
Deep Neural Network Model for Determination of Coal Cutting Resistance and Performance of Bucket-Wheel Excavator Based on the Environmental Properties and Excavation Parameters
by Srđan Kostić, Milan Stojković, Velibor Ilić and Jelena Trivan
Processes 2023, 11(11), 3067; https://doi.org/10.3390/pr11113067 - 26 Oct 2023
Cited by 2 | Viewed by 1156
Abstract
In the present paper, we develop a new model, based on the implementation of deep neural networks, for the estimation of a series of excavation parameters, depending on the main environmental and excavation properties. The developed model, with high statistical accuracy (R > [...] Read more.
In the present paper, we develop a new model, based on the implementation of deep neural networks, for the estimation of a series of excavation parameters, depending on the main environmental and excavation properties. The developed model, with high statistical accuracy (R > 0.79) and small RMSE (<17% of the actual output values), enables the simultaneous assessment of the following excavation parameters: effective capacity Qef, maximum current consumption Imax, maximum power consumption Nmax, maximum force consumption Pmax, maximum energy consumption Emax, and maximum linear and areal cutting resistance, KLmax and KFmax, respectively, based on the impact of the following environmental properties and excavation parameters: coal unit weight, coal compression strength, coal cohesion, friction angle, excavator movement angle in the left and right direction, slice height and thickness, and wheel velocity. The data analyzed in the present paper were previously collected from three neighboring open-pit coal mines in Serbia: Tamnava Western Field, Tamnava Eastern Field, and Field D. These mines have similar geological conditions and coal properties. Additionally, for each output factor, a complex analysis is provided on the impact of the examined input factors, by applying the multiple linear regression method. As far as we are aware, this is the first time such a comprehensive estimation model has been suggested for the operation of a bucket-wheel excavator in the Neogene coal basins. The deep neural network (DNN) model, trained over 300 epochs, shows an MSE range of 6.7–16.5% across various input factors, with distinct evaluations for Imax due to its unique values (4.8–12.5%). Full article
(This article belongs to the Special Issue Coal Mining and Unconventional Oil Exploration)
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