Natural Gas Industry B最新文献

{"title":"Integrated wellbore-surface pressure control production optimization for shale gas wells","authors":"Xingyu Zhou ,&nbsp;Liming Zhang ,&nbsp;Ji Qi ,&nbsp;Yanxing Wang ,&nbsp;Kai Zhang ,&nbsp;Ruijia Zhang ,&nbsp;Yaqi Sun","doi":"10.1016/j.ngib.2025.03.011","DOIUrl":"10.1016/j.ngib.2025.03.011","url":null,"abstract":"<div><div>Shale gas wells often face challenges in maintaining continuous and stable production due to their coexistence with high- and low-pressure wells within the same development block, which leads to issues involving mixed-pressure flows. Traditional pipeline optimization methods used in conventional gas well blocks fail to address the unique needs of shale gas wells, such as the precise planning of airflow paths, pressure distribution, and compression. This study proposes a pressure-controlled production optimization strategy specifically designed for shale gas wells operating under mixed-pressure flow conditions. The strategy aims to improve production stability and optimize system efficiency. The decline in production and pressure for individual wells over time is forecasted using a predictive model that accounts for key factors of system optimization, such as reservoir depletion, wellbore conditions, and equipment performance. Additionally, the model predicts the timing and impact of liquid loading, which can significantly affect production. The optimization process involves analyzing the existing gathering pipeline network to determine the most efficient flow directions and compression strategies based on these predictions, while the strategy involves adjusting compressor settings, optimizing flow rates, and planning pressure distribution across the network to maximize productivity while maintaining system stability. By implementing these strategies, this study significantly improves gas well productivity and enhances the adaptability and efficiency of the gathering and transportation system. The proposed approach provides systematic technical solutions and practical guidance for the efficient development and stable production of shale gas fields, ensuring more robust and sustainable pipeline operations.</div></div>","PeriodicalId":37116,"journal":{"name":"Natural Gas Industry B","volume":"12 2","pages":"Pages 123-134"},"PeriodicalIF":4.2,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143886561","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}

{"title":"Hybrid genetic algorithm for parametric optimization of surface pipeline networks in underground natural gas storage harmonized injection and production conditions","authors":"Jun Zhou ,&nbsp;Zichen Li ,&nbsp;Shitao Liu ,&nbsp;Chengyu Li ,&nbsp;Yunxiang Zhao ,&nbsp;Zonghang Zhou ,&nbsp;Guangchuan Liang","doi":"10.1016/j.ngib.2025.03.009","DOIUrl":"10.1016/j.ngib.2025.03.009","url":null,"abstract":"<div><div>The surface injection and production system (SIPS) is a critical component for effective injection and production processes in underground natural gas storage. As a vital channel, the rational design of the surface injection and production (SIP) pipeline significantly impacts efficiency. This paper focuses on the SIP pipeline and aims to minimize the investment costs of surface projects. An optimization model under harmonized injection and production conditions was constructed to transform the optimization problem of the SIP pipeline design parameters into a detailed analysis of the injection condition model and the production condition model. This paper proposes a hybrid genetic algorithm generalized reduced gradient (HGA-GRG) method, and compares it with the traditional genetic algorithm (GA) in a practical case study. The HGA-GRG demonstrated significant advantages in optimization outcomes, reducing the initial cost by 345.371 × 10⁴ CNY compared to the GA, validating the effectiveness of the model. By adjusting algorithm parameters, the optimal iterative results of the HGA-GRG were obtained, providing new research insights for the optimal design of a SIPS.</div></div>","PeriodicalId":37116,"journal":{"name":"Natural Gas Industry B","volume":"12 2","pages":"Pages 234-250"},"PeriodicalIF":4.2,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143886570","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}

{"title":"Mineral identification in thin sections using a lightweight and attention mechanism","authors":"Xin Zhang ,&nbsp;Wei Dang ,&nbsp;Jun Liu ,&nbsp;Zijuan Yin ,&nbsp;Guichao Du ,&nbsp;Yawen He ,&nbsp;Yankai Xue","doi":"10.1016/j.ngib.2025.03.001","DOIUrl":"10.1016/j.ngib.2025.03.001","url":null,"abstract":"<div><div>Mineral identification is foundational to geological survey research, mineral resource exploration, and mining engineering. Considering the diversity of mineral types and the challenge of achieving high recognition accuracy for similar features, this study introduces a mineral detection method based on YOLOv8-SBI. This work enhances feature extraction by integrating spatial pyramid pooling-fast (SPPF) with the simplified self-attention module (SimAM), significantly improving the precision of mineral feature detection. In the feature fusion network, a weighted bidirectional feature pyramid network is employed for advanced cross-channel feature integration, effectively reducing feature redundancy. Additionally, Inner-Intersection Over Union (InnerIOU) is used as the loss function to improve the average quality localization performance of anchor boxes. Experimental results show that the YOLOv8-SBI model achieves an accuracy of 67.9 %, a recall of 74.3 %, a [email protected] of 75.8 %, and a [email protected]:0.95 of 56.7 %, with a real-time detection speed of 244.2 frames per second. Compared to YOLOv8, YOLOv8-SBI demonstrates a significant improvement with 15.4 % increase in accuracy, 28.5 % increase in recall, and increases of 28.1 % and 20.9 % in [email protected] and [email protected]:0.95, respectively. Furthermore, relative to other models, such as YOLOv3, YOLOv5, YOLOv6, YOLOv8, YOLOv9, and YOLOv10, YOLOv8-SBI has a smaller parameter size of only 3.01 × 10⁶. This highlights the optimal balance between detection accuracy and speed, thereby offering robust technical support for intelligent mineral classification.</div></div>","PeriodicalId":37116,"journal":{"name":"Natural Gas Industry B","volume":"12 2","pages":"Pages 135-146"},"PeriodicalIF":4.2,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143886563","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}

{"title":"Stratified allocation method for water injection based on machine learning: A case study of the Bohai A oil and gas field","authors":"Changlong Liu ,&nbsp;Pingli Liu ,&nbsp;Qiang Wang ,&nbsp;Lu Zhang ,&nbsp;Zechao Huang ,&nbsp;Yuande Xu ,&nbsp;Shaojiu Jiang ,&nbsp;Le Zhang ,&nbsp;Changxiao Cao","doi":"10.1016/j.ngib.2025.03.005","DOIUrl":"10.1016/j.ngib.2025.03.005","url":null,"abstract":"<div><div>The Bohai A oil and gas field is a natural gas and oil coproduction reservoir in the southern Bohai Sea, with an average gas–oil ratio of approximately 65 m³/m³. The oil and gas field has now entered the high water-cut stage, and in it, ineffective water circulation has intensified. Meanwhile, the process of adjusting the injection volume of water injection wells is overly complicated and relies on the experience of reservoir engineers. This paper established an automatic allocation method aimed at optimizing injection strategies based on the reservoir injection allocation scheme and utilizing real-time online data from intelligent layered injection wells by combining numerical simulation with artificial intelligence and machine learning algorithms. First, according to the basic parameters of block B in the Bohai A oil and gas field, a reservoir numerical simulation model was established, and historical fitting was carried out. The calculation found that the natural gas production of the A oil field would increase over time, although its oil production showed a decreasing trend. Using this model, finite group calculations were performed to establish an effective dataset. Second, the training and prediction effects of three machine learning prediction models—support vector machine, BP neural network, and random forest—were compared, and the BP neural network was selected as the machine learning mathematical model for injection allocation optimization. Third, 300 neurons and three hidden layers were used in the optimized neural network. The number of training set samples used was 185, and the number of test set samples was 19. Fourth, the optimized BP neural network model exhibits enhanced prediction accuracy, improved generalization capabilities, and superior dynamic relationship–capturing abilities. It effectively establishes a relatively accurate complex nonlinear relationship between the injected water volume and the production of natural gas and oil, providing valuable guidance for layered allocation in injection wells. The relative error of the calculation results of the optimized neural network prediction model is approximately ±2.3 %. This model can be utilized to simulate the injection allocation of injection wells, potentially increasing natural gas and oil production by over 4 %.</div></div>","PeriodicalId":37116,"journal":{"name":"Natural Gas Industry B","volume":"12 2","pages":"Pages 207-218"},"PeriodicalIF":4.2,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143886571","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}

{"title":"A novel surrogate model with deep learning for predicting spacial-temporal pressure in coalbed methane reservoirs","authors":"Yukun Dong ,&nbsp;Xiaodong Zhang ,&nbsp;Jiyuan Zhang ,&nbsp;Kuankuan Wu ,&nbsp;Shuaiwei Liu","doi":"10.1016/j.ngib.2025.03.008","DOIUrl":"10.1016/j.ngib.2025.03.008","url":null,"abstract":"<div><div>Coalbed methane (CBM) is a vital unconventional energy resource, and predicting its spatiotemporal pressure dynamics is crucial for efficient development strategies. This paper proposes a novel deep learning–based data-driven surrogate model, AxialViT-ConvLSTM, which integrates AxialAttention Vision Transformer, ConvLSTM, and an enhanced loss function to predict pressure dynamics in CBM reservoirs. The results showed that the model achieves a mean square error of 0.003, a learned perceptual image patch similarity of 0.037, a structural similarity of 0.979, and an R² of 0.982 between predictions and actual pressures, indicating excellent performance. The model also demonstrates strong robustness and accuracy in capturing spatial–temporal pressure features.</div></div>","PeriodicalId":37116,"journal":{"name":"Natural Gas Industry B","volume":"12 2","pages":"Pages 219-233"},"PeriodicalIF":4.2,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143886572","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}

{"title":"From data to decisions: AI-Augmented geoscience and engineering in natural gas industry","authors":"Huiwen Pang,&nbsp;Shaoqun Dong,&nbsp;Peng Tan,&nbsp;Hanqing Wang,&nbsp;Jiabao Li","doi":"10.1016/j.ngib.2025.04.001","DOIUrl":"10.1016/j.ngib.2025.04.001","url":null,"abstract":"","PeriodicalId":37116,"journal":{"name":"Natural Gas Industry B","volume":"12 2","pages":"Pages 101-109"},"PeriodicalIF":4.2,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143886669","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}

{"title":"Automatic detection and classification of drill bit damage using deep learning and computer vision algorithms","authors":"Xiongwen Yang ,&nbsp;Xiao Feng ,&nbsp;Chris Cheng ,&nbsp;Jiaqing Yu ,&nbsp;Qing Zhang ,&nbsp;Zilong Gao ,&nbsp;Yang Liu ,&nbsp;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}

{"title":"An intelligent algorithm for identifying dropped blocks in wellbores","authors":"Qian Wang ,&nbsp;Zixuan Yang ,&nbsp;Chenxi Ye ,&nbsp;Wenbao Zhai ,&nbsp;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}

{"title":"Developing a large language model for oil- and gas-related rock mechanics: Progress and challenges","authors":"Botao Lin ,&nbsp;Yan Jin ,&nbsp;Qianwen Cao ,&nbsp;Han Meng ,&nbsp;Huiwen Pang ,&nbsp;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}

{"title":"EILnet: An intelligent model for the segmentation of multiple fracture types in karst carbonate reservoirs using electrical image logs","authors":"Zhuolin Li ,&nbsp;Guoyin Zhang ,&nbsp;Xiangbo Zhang ,&nbsp;Xin Zhang ,&nbsp;Yuchen Long ,&nbsp;Yanan Sun ,&nbsp;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}