Pub Date : 2025-04-01DOI: 10.1016/j.ngib.2025.03.004
Xiongwen Yang , Xiao Feng , Chris Cheng , Jiaqing Yu , Qing Zhang , Zilong Gao , Yang Liu , Bo Chen
This study aims to eliminate the subjectivity and inconsistency inherent in the traditional International Association of Drilling Contractors (IADC) bit wear rating process, which heavily depends on the experience of drilling engineers and often leads to unreliable results. Leveraging advancements in computer vision and deep learning algorithms, this research proposes an automated detection and classification method for polycrystalline diamond compact (PDC) bit damage. YOLOv10 was employed to locate the PDC bit cutters, followed by two SqueezeNet models to perform wear rating and wear type classifications. A comprehensive dataset was created based on the IADC dull bit evaluation standards. Additionally, this study discusses the necessity of data augmentation and finds that certain methods, such as cropping, splicing, and mixing, may reduce the accuracy of cutter detection. The experimental results demonstrate that the proposed method significantly enhances the accuracy of bit damage detection and classification while also providing substantial improvements in processing speed and computational efficiency, offering a valuable tool for optimizing drilling operations and reducing costs.
{"title":"Automatic detection and classification of drill bit damage using deep learning and computer vision algorithms","authors":"Xiongwen Yang , Xiao Feng , Chris Cheng , Jiaqing Yu , Qing Zhang , Zilong Gao , Yang Liu , Bo Chen","doi":"10.1016/j.ngib.2025.03.004","DOIUrl":"10.1016/j.ngib.2025.03.004","url":null,"abstract":"<div><div>This study aims to eliminate the subjectivity and inconsistency inherent in the traditional International Association of Drilling Contractors (IADC) bit wear rating process, which heavily depends on the experience of drilling engineers and often leads to unreliable results. Leveraging advancements in computer vision and deep learning algorithms, this research proposes an automated detection and classification method for polycrystalline diamond compact (PDC) bit damage. YOLOv10 was employed to locate the PDC bit cutters, followed by two SqueezeNet models to perform wear rating and wear type classifications. A comprehensive dataset was created based on the IADC dull bit evaluation standards. Additionally, this study discusses the necessity of data augmentation and finds that certain methods, such as cropping, splicing, and mixing, may reduce the accuracy of cutter detection. The experimental results demonstrate that the proposed method significantly enhances the accuracy of bit damage detection and classification while also providing substantial improvements in processing speed and computational efficiency, offering a valuable tool for optimizing drilling operations and reducing costs.</div></div>","PeriodicalId":37116,"journal":{"name":"Natural Gas Industry B","volume":"12 2","pages":"Pages 195-206"},"PeriodicalIF":4.2,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143886569","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-04-01DOI: 10.1016/j.ngib.2025.03.003
Qian Wang , Zixuan Yang , Chenxi Ye , Wenbao Zhai , Xiao Feng
Real-time monitoring of wellbore stability during drilling is crucial for the early detection of instability and timely interventions. The cause and type of wellbore instability can be identified by analyzing the dropped blocks brought to the surface by the drilling fluid, enabling preventive measures to be taken. In this study, an image capture system with fully automated sorting and 3D scanning was developed to obtain the complete 3D point cloud data of dropping blocks. The raw data obtained were preprocessed using methods such as format conversion, down sampling, coordinate transformation, statistical filtering, and clustering. Feature extraction algorithms, including the principal component analysis bounding box method, triangular meshing method, triaxial projection method, local curvature method, and model segmentation projection method, were employed, which resulted in the extraction of 32 feature parameters from the point cloud data. An optimal machine learning algorithm was developed by training it with 10 machine learning algorithms and the block data collected in the field. The XGBoost algorithm was then used to optimize the feature parameters and improve the classification model. An intelligent, fully automated feature parameter extraction and classification system was developed and applied to classify the types of falling blocks in 12 sets of drilling field and laboratory experiments and to identify the causes of wellbore instability. An average accuracy of 93.9 % was achieved. This system can thus enable the timely diagnosis and implementation of preventive and control measures for wellbore instability in the field.
{"title":"An intelligent algorithm for identifying dropped blocks in wellbores","authors":"Qian Wang , Zixuan Yang , Chenxi Ye , Wenbao Zhai , Xiao Feng","doi":"10.1016/j.ngib.2025.03.003","DOIUrl":"10.1016/j.ngib.2025.03.003","url":null,"abstract":"<div><div>Real-time monitoring of wellbore stability during drilling is crucial for the early detection of instability and timely interventions. The cause and type of wellbore instability can be identified by analyzing the dropped blocks brought to the surface by the drilling fluid, enabling preventive measures to be taken. In this study, an image capture system with fully automated sorting and 3D scanning was developed to obtain the complete 3D point cloud data of dropping blocks. The raw data obtained were preprocessed using methods such as format conversion, down sampling, coordinate transformation, statistical filtering, and clustering. Feature extraction algorithms, including the principal component analysis bounding box method, triangular meshing method, triaxial projection method, local curvature method, and model segmentation projection method, were employed, which resulted in the extraction of 32 feature parameters from the point cloud data. An optimal machine learning algorithm was developed by training it with 10 machine learning algorithms and the block data collected in the field. The XGBoost algorithm was then used to optimize the feature parameters and improve the classification model. An intelligent, fully automated feature parameter extraction and classification system was developed and applied to classify the types of falling blocks in 12 sets of drilling field and laboratory experiments and to identify the causes of wellbore instability. An average accuracy of 93.9 % was achieved. This system can thus enable the timely diagnosis and implementation of preventive and control measures for wellbore instability in the field.</div></div>","PeriodicalId":37116,"journal":{"name":"Natural Gas Industry B","volume":"12 2","pages":"Pages 186-194"},"PeriodicalIF":4.2,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143886565","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-04-01DOI: 10.1016/j.ngib.2025.03.007
Botao Lin , Yan Jin , Qianwen Cao , Han Meng , Huiwen Pang , Shiming Wei
In recent years, large language models (LLMs) have demonstrated immense potential in practical applications to enhance work efficiency and decision-making capabilities. However, specialized LLMs in the oil and gas engineering area are rarely developed. To aid in exploring and developing deep and ultra-deep unconventional reservoirs, there is a call for a personalized LLM on oil- and gas-related rock mechanics, which may handle complex professional data and make intelligent predictions and decisions. To that end, herein, we overview general and industry-specific LLMs. Then, a systematic workflow is proposed for building this domain-specific LLM for oil and gas engineering, including data collection and processing, model construction and training, model validation, and implementation in the specific domain. Moreover, three application scenarios are investigated: knowledge extraction from textural resources, field operation with multidisciplinary integration, and intelligent decision assistance. Finally, several challenges in developing this domain-specific LLM are highlighted. Our key findings are that geological surveys, laboratory experiments, field tests, and numerical simulations form the four original sources of rock mechanics data. Those data must flow through collection, storage, processing, and governance before being fed into LLM training. This domain-specific LLM can be trained by fine-tuning a general open-source LLM with professional data and constraints such as rock mechanics datasets and principles. The LLM can then follow the commonly used training and validation processes before being implemented in the oil and gas field. However, there are three primary challenges in building this domain-specific LLM: data standardization, data security and access, and striking a compromise between physics and data when building the model structure. Some of these challenges are administrative rather than technical, and overcoming those requires close collaboration between the different interested parties and various professional practitioners.
{"title":"Developing a large language model for oil- and gas-related rock mechanics: Progress and challenges","authors":"Botao Lin , Yan Jin , Qianwen Cao , Han Meng , Huiwen Pang , Shiming Wei","doi":"10.1016/j.ngib.2025.03.007","DOIUrl":"10.1016/j.ngib.2025.03.007","url":null,"abstract":"<div><div>In recent years, large language models (LLMs) have demonstrated immense potential in practical applications to enhance work efficiency and decision-making capabilities. However, specialized LLMs in the oil and gas engineering area are rarely developed. To aid in exploring and developing deep and ultra-deep unconventional reservoirs, there is a call for a personalized LLM on oil- and gas-related rock mechanics, which may handle complex professional data and make intelligent predictions and decisions. To that end, herein, we overview general and industry-specific LLMs. Then, a systematic workflow is proposed for building this domain-specific LLM for oil and gas engineering, including data collection and processing, model construction and training, model validation, and implementation in the specific domain. Moreover, three application scenarios are investigated: knowledge extraction from textural resources, field operation with multidisciplinary integration, and intelligent decision assistance. Finally, several challenges in developing this domain-specific LLM are highlighted. Our key findings are that geological surveys, laboratory experiments, field tests, and numerical simulations form the four original sources of rock mechanics data. Those data must flow through collection, storage, processing, and governance before being fed into LLM training. This domain-specific LLM can be trained by fine-tuning a general open-source LLM with professional data and constraints such as rock mechanics datasets and principles. The LLM can then follow the commonly used training and validation processes before being implemented in the oil and gas field. However, there are three primary challenges in building this domain-specific LLM: data standardization, data security and access, and striking a compromise between physics and data when building the model structure. Some of these challenges are administrative rather than technical, and overcoming those requires close collaboration between the different interested parties and various professional practitioners.</div></div>","PeriodicalId":37116,"journal":{"name":"Natural Gas Industry B","volume":"12 2","pages":"Pages 110-122"},"PeriodicalIF":4.2,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143886670","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-04-01DOI: 10.1016/j.ngib.2025.03.002
Zhuolin Li , Guoyin Zhang , Xiangbo Zhang , Xin Zhang , Yuchen Long , Yanan Sun , Chengyan Lin
Karst fractures serve as crucial seepage channels and storage spaces for carbonate natural gas reservoirs, and electrical image logs are vital data for visualizing and characterizing such fractures. However, the conventional approach of identifying fractures using electrical image logs predominantly relies on manual processes that are not only time-consuming but also highly subjective. In addition, the heterogeneity and strong dissolution tendency of karst carbonate reservoirs lead to complexity and variety in fracture geometry, which makes it difficult to accurately identify fractures. In this paper, the electrical image logs network (EILnet)—a deep-learning-based intelligent semantic segmentation model with a selective attention mechanism and selective feature fusion module—was created to enable the intelligent identification and segmentation of different types of fractures through electrical logging images. Data from electrical image logs representing structural and induced fractures were first selected using the sliding window technique before image inpainting and data augmentation were implemented for these images to improve the generalizability of the model. Various image-processing tools, including the bilateral filter, Laplace operator, and Gaussian low-pass filter, were also applied to the electrical logging images to generate a multi-attribute dataset to help the model learn the semantic features of the fractures. The results demonstrated that the EILnet model outperforms mainstream deep-learning semantic segmentation models, such as Fully Convolutional Networks (FCN-8s), U-Net, and SegNet, for both the single-channel dataset and the multi-attribute dataset. The EILnet provided significant advantages for the single-channel dataset, and its mean intersection over union (MIoU) and pixel accuracy (PA) were 81.32 % and 89.37 %, respectively. In the case of the multi-attribute dataset, the identification capability of all models improved to varying degrees, with the EILnet achieving the highest MIoU and PA of 83.43 % and 91.11 %, respectively. Further, applying the EILnet model to various blind wells demonstrated its ability to provide reliable fracture identification, thereby indicating its promising potential applications.
{"title":"EILnet: An intelligent model for the segmentation of multiple fracture types in karst carbonate reservoirs using electrical image logs","authors":"Zhuolin Li , Guoyin Zhang , Xiangbo Zhang , Xin Zhang , Yuchen Long , Yanan Sun , Chengyan Lin","doi":"10.1016/j.ngib.2025.03.002","DOIUrl":"10.1016/j.ngib.2025.03.002","url":null,"abstract":"<div><div>Karst fractures serve as crucial seepage channels and storage spaces for carbonate natural gas reservoirs, and electrical image logs are vital data for visualizing and characterizing such fractures. However, the conventional approach of identifying fractures using electrical image logs predominantly relies on manual processes that are not only time-consuming but also highly subjective. In addition, the heterogeneity and strong dissolution tendency of karst carbonate reservoirs lead to complexity and variety in fracture geometry, which makes it difficult to accurately identify fractures. In this paper, the electrical image logs network (EILnet)—a deep-learning-based intelligent semantic segmentation model with a selective attention mechanism and selective feature fusion module—was created to enable the intelligent identification and segmentation of different types of fractures through electrical logging images. Data from electrical image logs representing structural and induced fractures were first selected using the sliding window technique before image inpainting and data augmentation were implemented for these images to improve the generalizability of the model. Various image-processing tools, including the bilateral filter, Laplace operator, and Gaussian low-pass filter, were also applied to the electrical logging images to generate a multi-attribute dataset to help the model learn the semantic features of the fractures. The results demonstrated that the EILnet model outperforms mainstream deep-learning semantic segmentation models, such as Fully Convolutional Networks (FCN-8s), U-Net, and SegNet, for both the single-channel dataset and the multi-attribute dataset. The EILnet provided significant advantages for the single-channel dataset, and its mean intersection over union (MIoU) and pixel accuracy (PA) were 81.32 % and 89.37 %, respectively. In the case of the multi-attribute dataset, the identification capability of all models improved to varying degrees, with the EILnet achieving the highest MIoU and PA of 83.43 % and 91.11 %, respectively. Further, applying the EILnet model to various blind wells demonstrated its ability to provide reliable fracture identification, thereby indicating its promising potential applications.</div></div>","PeriodicalId":37116,"journal":{"name":"Natural Gas Industry B","volume":"12 2","pages":"Pages 158-173"},"PeriodicalIF":4.2,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143886567","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-04-01DOI: 10.1016/j.ngib.2025.03.006
Hanqing Wang , Han Wang , Kunyan Liu , Jin Meng , Yitian Xiao , Yanghua Wang
Seismic fault identification is a critical step in structural interpretation, reservoir characterization, and well-drilling planning. However, fault identification in deep fault-karst carbonate formations is particularly challenging due to their deep burial depth and the complex effects of dissolution. Traditional manual interpretation methods are often labor intensive and prone to high uncertainty due to their subjective nature. To address these limitations, this study proposes a transfer learning–based strategy for fault identification in deep fault-karst carbonate formations. The proposed methodology began with the generation of a large volume of synthetic seismic samples based on statistical fault distribution patterns observed in the study area. These synthetic samples were used to pretrain an improved U-Net network architecture, enhanced with an attention mechanism, to create a robust pretrained model. Subsequently, real-world fault labels were manually annotated based on verified fault interpretations and integrated into the training dataset. This combination of synthetic and real-world data was used to fine-tune the pretrained model, significantly improving its fault interpretation accuracy. The experimental results demonstrate that the integration of synthetic and real-world samples effectively enhances the quality of the training dataset. Furthermore, the proposed transfer learning strategy significantly improves fault recognition accuracy. By replacing the traditional weighted cross-entropy loss function with the Dice loss function, the model successfully addresses the issue of extreme class imbalance between positive and negative samples. Practical applications confirm that the proposed transfer learning strategy can accurately identify fault structures in deep fault-karst carbonate formations, providing a novel and effective technical approach for fault interpretation in such complex geological settings.
{"title":"Seismic fault identification of deep fault-karst carbonate reservoir using transfer learning","authors":"Hanqing Wang , Han Wang , Kunyan Liu , Jin Meng , Yitian Xiao , Yanghua Wang","doi":"10.1016/j.ngib.2025.03.006","DOIUrl":"10.1016/j.ngib.2025.03.006","url":null,"abstract":"<div><div>Seismic fault identification is a critical step in structural interpretation, reservoir characterization, and well-drilling planning. However, fault identification in deep fault-karst carbonate formations is particularly challenging due to their deep burial depth and the complex effects of dissolution. Traditional manual interpretation methods are often labor intensive and prone to high uncertainty due to their subjective nature. To address these limitations, this study proposes a transfer learning–based strategy for fault identification in deep fault-karst carbonate formations. The proposed methodology began with the generation of a large volume of synthetic seismic samples based on statistical fault distribution patterns observed in the study area. These synthetic samples were used to pretrain an improved U-Net network architecture, enhanced with an attention mechanism, to create a robust pretrained model. Subsequently, real-world fault labels were manually annotated based on verified fault interpretations and integrated into the training dataset. This combination of synthetic and real-world data was used to fine-tune the pretrained model, significantly improving its fault interpretation accuracy. The experimental results demonstrate that the integration of synthetic and real-world samples effectively enhances the quality of the training dataset. Furthermore, the proposed transfer learning strategy significantly improves fault recognition accuracy. By replacing the traditional weighted cross-entropy loss function with the Dice loss function, the model successfully addresses the issue of extreme class imbalance between positive and negative samples. Practical applications confirm that the proposed transfer learning strategy can accurately identify fault structures in deep fault-karst carbonate formations, providing a novel and effective technical approach for fault interpretation in such complex geological settings.</div></div>","PeriodicalId":37116,"journal":{"name":"Natural Gas Industry B","volume":"12 2","pages":"Pages 174-185"},"PeriodicalIF":4.2,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143886568","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The evaluation of adsorption states and shale gas content in shale fractures and pores relies on the analysis of these fractures and pores. Scanning electron microscopy images are commonly used for shale analysis; however, their low resolution, particularly the loss of high-frequency information at pore edges, presents challenges in analyzing fractures and pores in shale gas reservoirs. This study introduced a novel neural network called the spatial-spectral domain attention network (SSDAN), which employed spatial and spectral domain attention mechanisms to extract features and restore information in parallel. The network generated super-resolution images through a fusion module that included CNN-based spatial blocks for pixel-level image information recovery, spectral blocks to process Fourier transform information of images and enhance high-frequency recovery, and an adaptive vision transformer to process Fourier transform block information, eliminating the need for a preset image size. The SSDAN model demonstrated exceptional performance in comparative experiments on marine shale and marine continental shale datasets, achieving optimal performance on key indicators such as peak signal-to-noise ratio, structural similarity, learned perceptual image patch similarity, and Frechet inception distance while also exhibiting superior visual performance in pore recovery. Ablation experiments further confirmed the effectiveness of the spatial blocks, channel attention, spectral blocks, and frequency loss function in the model. The SSDAN model showed remarkable capability in enhancing the resolution of shale gas reservoir images and restoring high-frequency information at pore edges, thereby validating its effectiveness in unconventional natural gas reservoir analyses.
{"title":"Super-resolution for electron microscope scanning images of shale via spatial-spectral domain attention network","authors":"Junqi Chen , Lijuan Jia , Jinchuan Zhang , Yilong Feng","doi":"10.1016/j.ngib.2025.03.010","DOIUrl":"10.1016/j.ngib.2025.03.010","url":null,"abstract":"<div><div>The evaluation of adsorption states and shale gas content in shale fractures and pores relies on the analysis of these fractures and pores. Scanning electron microscopy images are commonly used for shale analysis; however, their low resolution, particularly the loss of high-frequency information at pore edges, presents challenges in analyzing fractures and pores in shale gas reservoirs. This study introduced a novel neural network called the spatial-spectral domain attention network (SSDAN), which employed spatial and spectral domain attention mechanisms to extract features and restore information in parallel. The network generated super-resolution images through a fusion module that included CNN-based spatial blocks for pixel-level image information recovery, spectral blocks to process Fourier transform information of images and enhance high-frequency recovery, and an adaptive vision transformer to process Fourier transform block information, eliminating the need for a preset image size. The SSDAN model demonstrated exceptional performance in comparative experiments on marine shale and marine continental shale datasets, achieving optimal performance on key indicators such as peak signal-to-noise ratio, structural similarity, learned perceptual image patch similarity, and Frechet inception distance while also exhibiting superior visual performance in pore recovery. Ablation experiments further confirmed the effectiveness of the spatial blocks, channel attention, spectral blocks, and frequency loss function in the model. The SSDAN model showed remarkable capability in enhancing the resolution of shale gas reservoir images and restoring high-frequency information at pore edges, thereby validating its effectiveness in unconventional natural gas reservoir analyses.</div></div>","PeriodicalId":37116,"journal":{"name":"Natural Gas Industry B","volume":"12 2","pages":"Pages 147-157"},"PeriodicalIF":4.2,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143886566","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-01DOI: 10.1016/j.ngib.2025.01.002
Juntao Fu , Jiahao Tang , Jianlu Zhu , Guocong Wang , Yuxing Li , Hui Han
Liquid hydrogen has attracted much attention due to its high energy storage density and suitability for long-distance transportation. An efficient hydrogen liquefaction process is the key to obtaining liquid hydrogen. In an effort to determine the parameter optimization of the hydrogen liquefaction process, this paper employed process simulation software Aspen HYSYS to simulate the hydrogen liquefaction process. By establishing a dynamic model of the unit module, this study carried out dynamic simulation optimization based on the steady-state process and process parameters of the hydrogen liquefaction process and analyzed the dynamic characteristics of the process. Based on the pressure drop characteristic experiment, an equation for the pressure drop in the heat exchanger was proposed. The heat transfer of hydrogen conversion was simulated and analyzed, and its accuracy was verified by comparison with the literature. The dynamic simulation of a plate-fin heat exchanger was carried out by coupling heat transfer simulation and the pressure drop experiment. The results show that the increase in inlet temperature (5 °C and 10 °C) leads to an increase in specific energy consumption (0.65 % and 1.29 %, respectively) and a decrease in hydrogen liquefaction rate (0.63 % and 2.88 %, respectively). When the inlet pressure decreases by 28.57 %, the hydrogen temperature of the whole liquefaction process decreases and the specific energy consumption increases by 52.94 %. The research results are of great significance for improving the operating efficiency of the refrigeration cycle and guiding the actual liquid hydrogen production.
{"title":"Dynamic simulation optimization of the hydrogen liquefaction process","authors":"Juntao Fu , Jiahao Tang , Jianlu Zhu , Guocong Wang , Yuxing Li , Hui Han","doi":"10.1016/j.ngib.2025.01.002","DOIUrl":"10.1016/j.ngib.2025.01.002","url":null,"abstract":"<div><div>Liquid hydrogen has attracted much attention due to its high energy storage density and suitability for long-distance transportation. An efficient hydrogen liquefaction process is the key to obtaining liquid hydrogen. In an effort to determine the parameter optimization of the hydrogen liquefaction process, this paper employed process simulation software Aspen HYSYS to simulate the hydrogen liquefaction process. By establishing a dynamic model of the unit module, this study carried out dynamic simulation optimization based on the steady-state process and process parameters of the hydrogen liquefaction process and analyzed the dynamic characteristics of the process. Based on the pressure drop characteristic experiment, an equation for the pressure drop in the heat exchanger was proposed. The heat transfer of hydrogen conversion was simulated and analyzed, and its accuracy was verified by comparison with the literature. The dynamic simulation of a plate-fin heat exchanger was carried out by coupling heat transfer simulation and the pressure drop experiment. The results show that the increase in inlet temperature (5 °C and 10 °C) leads to an increase in specific energy consumption (0.65 % and 1.29 %, respectively) and a decrease in hydrogen liquefaction rate (0.63 % and 2.88 %, respectively). When the inlet pressure decreases by 28.57 %, the hydrogen temperature of the whole liquefaction process decreases and the specific energy consumption increases by 52.94 %. The research results are of great significance for improving the operating efficiency of the refrigeration cycle and guiding the actual liquid hydrogen production.</div></div>","PeriodicalId":37116,"journal":{"name":"Natural Gas Industry B","volume":"12 1","pages":"Pages 16-25"},"PeriodicalIF":4.2,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143510318","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-01DOI: 10.1016/j.ngib.2025.01.006
Tong Lin , Kangle Wang , Haidong Wang , Runze Yang , Pan Li , Long Su
The coal-bearing source rocks in the Jurassic Shuixigou Group have received widespread attention as the primary source rocks in the Turpan-Hami Basin of China, but the hydrocarbon generation potential and process of the mudstone in the Shuixigou Group, especially the mudstone at the top of the Sangonghe Formation, are unclear. Taking the source rocks of the Xishanyao Formation and the Sangonghe Formation as objectives, this study conducted rock pyrolysis and gold tube simulation experiment to investigate their hydrocarbon generation characteristics and differences. Our results indicate that the source rocks of the Xishanyao Formation include mudstone, carbonaceous mudstone and coal, and the quality of the source rocks is highly heterogeneous; the source rocks of the Sangonghe Formation are mainly composed of mudstone, and it is a good gas source rock. Simulation experiments found that the activation energy required for the generation of gaseous hydrocarbons by the mudstone of the Sangonghe Formation is lower than that by the mudstone of the Xishanyao Formation. The hydrocarbon generation process can be divided into three stages for both formations, but the gas generation potential of the Xishanyao Formation mudstone is higher than that of the Sangonghe Formation mudstone. A large amount of hydrocarbon was generated by the mudstone of the Xishanyao Formation when entering late thermal evolution, of which methane is dominant, mainly from the demethylation reaction of mature kerogen. On the other hand, a large amount of hydrocarbon was generated by the mudstone of the Sangonghe Formation in the early stage of thermal evolution, of which light hydrocarbon and wet gas are dominant, mainly from the early cracking stage of kerogen. This difference may be attributed to the structure of kerogen. The mudstone of the Xishanyao Formation is conducive to the formation of highly mature dry gas reservoirs, while the mudstone of the Sangonghe Formation is conducive to the formation of low maturity condensate gas and volatile oil reservoirs. The research result provides a scientific basis for the comparison of oil and gas sources and the evaluation of oil and gas resources in the Turpan-Hami Basin.
{"title":"The hydrocarbon generation potential of the mudstone source rock in the Jurassic Shuixigou Group, the Turpan-Hami Basin, and indicative significance for oil and gas exploration","authors":"Tong Lin , Kangle Wang , Haidong Wang , Runze Yang , Pan Li , Long Su","doi":"10.1016/j.ngib.2025.01.006","DOIUrl":"10.1016/j.ngib.2025.01.006","url":null,"abstract":"<div><div>The coal-bearing source rocks in the Jurassic Shuixigou Group have received widespread attention as the primary source rocks in the Turpan-Hami Basin of China, but the hydrocarbon generation potential and process of the mudstone in the Shuixigou Group, especially the mudstone at the top of the Sangonghe Formation, are unclear. Taking the source rocks of the Xishanyao Formation and the Sangonghe Formation as objectives, this study conducted rock pyrolysis and gold tube simulation experiment to investigate their hydrocarbon generation characteristics and differences. Our results indicate that the source rocks of the Xishanyao Formation include mudstone, carbonaceous mudstone and coal, and the quality of the source rocks is highly heterogeneous; the source rocks of the Sangonghe Formation are mainly composed of mudstone, and it is a good gas source rock. Simulation experiments found that the activation energy required for the generation of gaseous hydrocarbons by the mudstone of the Sangonghe Formation is lower than that by the mudstone of the Xishanyao Formation. The hydrocarbon generation process can be divided into three stages for both formations, but the gas generation potential of the Xishanyao Formation mudstone is higher than that of the Sangonghe Formation mudstone. A large amount of hydrocarbon was generated by the mudstone of the Xishanyao Formation when entering late thermal evolution, of which methane is dominant, mainly from the demethylation reaction of mature kerogen. On the other hand, a large amount of hydrocarbon was generated by the mudstone of the Sangonghe Formation in the early stage of thermal evolution, of which light hydrocarbon and wet gas are dominant, mainly from the early cracking stage of kerogen. This difference may be attributed to the structure of kerogen. The mudstone of the Xishanyao Formation is conducive to the formation of highly mature dry gas reservoirs, while the mudstone of the Sangonghe Formation is conducive to the formation of low maturity condensate gas and volatile oil reservoirs. The research result provides a scientific basis for the comparison of oil and gas sources and the evaluation of oil and gas resources in the Turpan-Hami Basin.</div></div>","PeriodicalId":37116,"journal":{"name":"Natural Gas Industry B","volume":"12 1","pages":"Pages 50-63"},"PeriodicalIF":4.2,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143510325","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-01DOI: 10.1016/j.ngib.2025.02.002
Xiangfeng Wei , Qingqiu Huang , Jingyu Hao , Zhujiang Liu , Qiang Wang , Qingbo Wang , Daojun Wang , Jilin Xiao
Dongyuemiao Member shale in the Sichuan Basin, China, is characterized by organic-rich shale intervals with different types of interbeds and accumulation modes. The aim of this study is to elucidate the impact of paleoenvironmental indicators on interbed development. With this aim in mind, we established an interbed classification scheme and quantified the development of different types of interbeds and their frequencies. We categorized the shale interbeds into three types based on interbed type: silt interbeds (SIs), shell fragment interbeds (SFIs), and shell skeleton interbeds (SSIs). The SIs, SFIs, and SSIs are respectively the products of extrabasinal low-density turbidity currents, intrabasinal debris flow, and intrabasinal low-density turbidity currents. We propose that variations in paleoenvironmental conditions primarily influenced the types of interbeds that developed but had minimal impact on the frequency of their development. Models depicting the interbed development within the 1st Submember of Dongyuemiao Member indicate that during the early Dongyuemiao depositional period, under conditions of relatively aridity, weak weathering, high terrigenous input, and strong hydrodynamic activity, SSIs were well developed. In the middle depositional period, as the climate gradually transitioned to more humid conditions, and the weathering intensity and amount of terrestrial input increased, the development of SIs and SFIs significantly increased. During the late depositional period, with a continuous decrease in terrestrial inputs and sedimentation rates, the development of SIs decreased while that of SSIs increased.
{"title":"Paleoenvironmental factors controlling the development of the lacustrine shale interbed in the Jurassic Dongyuemiao Member of the Sichuan Basin, China","authors":"Xiangfeng Wei , Qingqiu Huang , Jingyu Hao , Zhujiang Liu , Qiang Wang , Qingbo Wang , Daojun Wang , Jilin Xiao","doi":"10.1016/j.ngib.2025.02.002","DOIUrl":"10.1016/j.ngib.2025.02.002","url":null,"abstract":"<div><div>Dongyuemiao Member shale in the Sichuan Basin, China, is characterized by organic-rich shale intervals with different types of interbeds and accumulation modes. The aim of this study is to elucidate the impact of paleoenvironmental indicators on interbed development. With this aim in mind, we established an interbed classification scheme and quantified the development of different types of interbeds and their frequencies. We categorized the shale interbeds into three types based on interbed type: silt interbeds (SIs), shell fragment interbeds (SFIs), and shell skeleton interbeds (SSIs). The SIs, SFIs, and SSIs are respectively the products of extrabasinal low-density turbidity currents, intrabasinal debris flow, and intrabasinal low-density turbidity currents. We propose that variations in paleoenvironmental conditions primarily influenced the types of interbeds that developed but had minimal impact on the frequency of their development. Models depicting the interbed development within the 1st Submember of Dongyuemiao Member indicate that during the early Dongyuemiao depositional period, under conditions of relatively aridity, weak weathering, high terrigenous input, and strong hydrodynamic activity, SSIs were well developed. In the middle depositional period, as the climate gradually transitioned to more humid conditions, and the weathering intensity and amount of terrestrial input increased, the development of SIs and SFIs significantly increased. During the late depositional period, with a continuous decrease in terrestrial inputs and sedimentation rates, the development of SIs decreased while that of SSIs increased.</div></div>","PeriodicalId":37116,"journal":{"name":"Natural Gas Industry B","volume":"12 1","pages":"Pages 88-100"},"PeriodicalIF":4.2,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143510375","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-01DOI: 10.1016/j.ngib.2025.01.001
Wan Cheng , Zuncha Wang , Gang Lei , Qinghai Hu , Yuzhao Shi , Siyu Yang
Horizontal well intensive fracturing is a critical technology used to stimulate unconventional oil and gas reservoirs. Accurate prediction of wellbore breakdown pressure is conducive to optimal fracturing design and improvement of the reservoir stimulation effect. In this work, the three-dimensional displacement discontinuity method (DDM) is used to characterize fracture deformation and fracture closure after the pumping pressure relief. The influences of key parameters such as the minimum horizontal principal stress, fracture spacing, the Young's modulus, the Poisson's ratio and pumping pressure on the breakdown pressure are analyzed. The results show that, assuming that the fracture half-length is a, the breakdown pressure outside the fracture surface area increases significantly within 2a in the direction of the minimum horizontal principal stress and a in the directions of the vertical stress and maximum horizontal principal stress before pressure relief. The breakdown pressure of the modified zipper-type fracturing in the later stage is lower. When the fracture spacing is small, the fracture breakdown pressure decreases after the modified zipper-type fracturing of two horizontal wells. The fracture breakdown pressure of the first fractured well reaches a maximum when the fracture spacing is a – 1.5a, and the breakdown pressure decreases with increasing well spacing.
{"title":"3D mechanical modeling and analysis of influencing factors on fracture breakdown pressure in dual horizontal well intensive hydraulic fracturing","authors":"Wan Cheng , Zuncha Wang , Gang Lei , Qinghai Hu , Yuzhao Shi , Siyu Yang","doi":"10.1016/j.ngib.2025.01.001","DOIUrl":"10.1016/j.ngib.2025.01.001","url":null,"abstract":"<div><div>Horizontal well intensive fracturing is a critical technology used to stimulate unconventional oil and gas reservoirs. Accurate prediction of wellbore breakdown pressure is conducive to optimal fracturing design and improvement of the reservoir stimulation effect. In this work, the three-dimensional displacement discontinuity method (DDM) is used to characterize fracture deformation and fracture closure after the pumping pressure relief. The influences of key parameters such as the minimum horizontal principal stress, fracture spacing, the Young's modulus, the Poisson's ratio and pumping pressure on the breakdown pressure are analyzed. The results show that, assuming that the fracture half-length is <em>a</em>, the breakdown pressure outside the fracture surface area increases significantly within 2<em>a</em> in the direction of the minimum horizontal principal stress and <em>a</em> in the directions of the vertical stress and maximum horizontal principal stress before pressure relief. The breakdown pressure of the modified zipper-type fracturing in the later stage is lower. When the fracture spacing is small, the fracture breakdown pressure decreases after the modified zipper-type fracturing of two horizontal wells. The fracture breakdown pressure of the first fractured well reaches a maximum when the fracture spacing is a – 1.5a, and the breakdown pressure decreases with increasing well spacing.</div></div>","PeriodicalId":37116,"journal":{"name":"Natural Gas Industry B","volume":"12 1","pages":"Pages 1-15"},"PeriodicalIF":4.2,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143510584","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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