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An interpretable attention-guided generative adversarial network framework with dual-domain learning for multi-condition constrained sedimentary facies modeling 认领 引用 被引量：1
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作者 Lei Liu Wei Li +7 位作者 Jian Gao Da-Li Yue De-Gang Wu Wu-Rong Wang Jin Lin Zhi-Bo Li Qian Zhong Jia-Gen Hou 《Petroleum Science》 SCIE EI CAS CSCD 2026年第4期1754-1772,共19页
Sedimentary facies modeling is a critical approach for understanding geological phenomena,yet the strong heterogeneity of reservoir systems poses a serious challenge for their refined characterization.In this study,we... Sedimentary facies modeling is a critical approach for understanding geological phenomena,yet the strong heterogeneity of reservoir systems poses a serious challenge for their refined characterization.In this study,we innovatively propose an interpretable attention-guided generative adversarial network framework with dual-domain learning,which achieves precise sedimentary facies modeling under the constraints of well facies and soft probability data.Specifically,we first effectively extract and preserve prior information of sedimentary facies models from both spatial and frequency domain perspectives.Then,during simulation,to enhance the capability of the network model for finely characterizing complex heterogeneous models,cross-spatial attention mechanisms are designed to effectively capture short-range and long-range dependencies between multi-scale pattern features.Additionally,through systematic feature map visualization analysis,we elucidate the processes of conditional fitting and complex sedimentary facies model reconstruction,intuitively demonstrating the functional mechanisms of each module.Finally,systematic experiments are conducted on multiple datasets to validate the effectiveness of the proposed method.The results demonstrate that the generated sedimentary facies models exhibit high consistency with training datasets in terms of visual realism and statistical indicators.Quantitative comparisons reveal remarkable performance of the method,achieving low Wasserstein distance(0.09),Kernel Inception Distance(0.0017)and Kernel Maximum Mean Discrepancy(0.21).These findings further confirm the high realism of the generated realizations regarding pattern features.This study offers a reliable and practical method for geological reservoir modeling,thereby advancing quantitative,precise geological research with broad application prospects. 展开更多
关键词 Sedimentary facies models Attention-guided generative adversarial network Interpretable framework Sedimentary patterns Multi-condition modeling
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Genesis of lamina combinations and intelligent well-logging interpretation in the upper Xiaganchaigou Formation,Yingxi area,Qaidam Basin,China 认领 引用
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作者 Jia-Lin Fu Da-Li Yue +6 位作者 Wu-Rong Wang Kun-Yu Wu Han Wang Ying-Hai Jiang Shu-Qi Zhang Zi-Mo Xu Wei Li 《Petroleum Science》 SCIE EI CAS CSCD 2026年第6期3091-3109,共19页
The laminar sedimentary structures of saline lacustrine mixed rocks affect both organic matter enrichment and reservoir storage performance.However,due to the small-scale nature of laminae,large-scale identification u... The laminar sedimentary structures of saline lacustrine mixed rocks affect both organic matter enrichment and reservoir storage performance.However,due to the small-scale nature of laminae,large-scale identification using well-logging data during reservoir exploration and development remains challenging.It is necessary to introduce a research method to identify and characterize the development of different types of laminae.Based on analyses of typical cores,XRD data,and welllogging curves from the upper member of the Xiaganchaigou Formation in the Yingxi area,five main types of laminae and six lamina combinations were classified.A Transformer-based intelligent recognition method was then applied to identify these lamina combinations from well-log data,with the Random Forest algorithm used as a comparative benchmark.Verification results show that the Transformer model achieves a higher total accuracy of 84%in lamina combination recognition.This study proposes a new approach for the conventional well-log characterization of laminae,in which the classification is established from the perspective of laminae genesis.It reflects the development patterns of lamina combinations driven by paleoenvironmental changes,and selects an appropriate intelligent recognition method to address the challenges in well-log characterization of such reservoirs.In terms of engineering applications,this study can accurately indicate the positions of high-quality reservoirs within sedimentary cycles during field development.It provides a sedimentary facies-controlled basis for the three-dimensional characterization of reservoir quality,thereby offering a valuable reference for the exploration and development of reservoirs formed under similar sedimentary conditions. 展开更多
关键词 Lamina combination identification Transformer intelligent recognition Sedimentary origin E32 segment Qaidam Basin
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Microstructural evolution of Al-Si coating and its influence on high temperature tribological behavior of ultra-high strength steel against H13 steel 认领 引用 被引量：4
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作者 Meng-xuan Guo Kai-xiang Gao +1 位作者 Wu-rong Wang Xi-cheng Wei 《Journal of Iron and Steel Research International》 SCIE EI CAS CSCD 2017年第10期1048-1058,共11页
Al-Si coated ultra-high strength steel（UHSS）has been commonly applied in hot stamping process.The influence of austenitizing temperature on microstructure of Al-Si coating of UHSS during hot stamping process and its... Al-Si coated ultra-high strength steel（UHSS）has been commonly applied in hot stamping process.The influence of austenitizing temperature on microstructure of Al-Si coating of UHSS during hot stamping process and its tribological behavior against H13 steel under elevated temperature were simulatively investigated.The austenitizing temperature of Al-Si coated UHSS and its microstructual evolution were confirmed and analyzed by differential scanning calorimetry and scanning electron microscopy.A novel approach to tribological testing by replicating hot stamping process temperature history was presented.Results show that the hard and stable phases Fe_2Al_5＋FeAl_2 formed on Al-Si coating surface after exposure to 930°C for 5 min,which was found to be correlated to the tribological behavior of coating.The friction coefficient of coated steel was more stable and higher than that of uncoated one.The main wear mechanism of Al-Si coated UHSS was adhesion wear,while abrasive wear was dominant for the uncoated UHSS. 展开更多
关键词 Hot stamping Al-Si coating Ultra-high strength steel Intermetallic compound Diffusion Friction coefficient Adhesive wear
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Tribological analysis of the surface layer coated by sintered serpentine-reinforced composites 认领 引用
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作者 Xiao Wang Jun-Wei Wu +2 位作者 Lu-Hai Zhou Xi-Cheng Wei Wu-Rong Wang 《Acta Metallurgica Sinica(English Letters)》 SCIE EI CAS CSCD 2018年第6期615-622,共8页
In this work,the serpentine powders were sintered to make the serpentine-reinforced Al-matrix composites,and the microstructures of which were characterized by differential scanning calorimetry,thermal gravimetric ana... In this work,the serpentine powders were sintered to make the serpentine-reinforced Al-matrix composites,and the microstructures of which were characterized by differential scanning calorimetry,thermal gravimetric analyzer,and X-ray diffractometer.Scanning electron microscopy equipped with energy dispersive spectroscopy.Results show that the sintered serpentine powders were deeply absorbed on the worn surface and embedded in the furrows and scratches of the matrix,forming a self-repairing surface layer which reduces the friction coefficient.The surface layer coated by serpentine was compact,dense,and uniform with the friction time prolonged,compensating the worn loss and increasing the matrix mass. 展开更多
关键词 Serpentine Friction Wear Surface layer
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Sediment-architectural characteristics and distribution patterns of climate-controlled shallow-water braided river deltas:A case study of the Neogene Guantao Formation in the P Oilfield,Bohai Bay Basin,China 认领 引用
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作者 Jing-Yang Liu Da-Li Yue +8 位作者 Wei Li Luca Colombera Wen-Zhi Zhao Jin Lin Wu-Rong Wang Zi-Hao Lin Jia-Yu Wei Xing-Jian Wang Zhi-Bo Li 《Journal of Palaeogeography》 SCIE CSCD 2026年第3期197-217,共21页
Characterizing the sedimentary architecture of successions of shallow-water deltas fed by braided rivers is crucial for the prediction of sandbody development in potential field.However,the complex distribution and di... Characterizing the sedimentary architecture of successions of shallow-water deltas fed by braided rivers is crucial for the prediction of sandbody development in potential field.However,the complex distribution and diverse sedimentary characteristics of these deltas have led to insufficient insight into the controlling factors for different types.Based on high-resolution seismic data and well-logging data from the P Oilfield in the Bohai Bay Basin,two zones(Ⅰ1 and Ⅲ2)within the Neogene upper member of Guantao Formation were selected.Utilizing palynological analysis,elemental geochemical analysis,and frequency-division seismic-attribute fusion technology based on an ensemble-machine-learning algorithm,paleoclimate analysis and detailed architectural characterization of the studied stratigraphic interval were conducted.The findings indicate that:(1)Two palynological assemblages were identified in the upper member of Guantao Formation:assemblageⅠsuggests a subtropical-temperate monsoon climate,while assemblageⅡindicates a continental semi-arid climate.(2)ZoneⅠ1 represents a lobate shallow-water braided-river delta,whereas zone Ⅲ2 represents a lobate shallow-water braided-river delta with bar-finger.(3)Stable discharge conditions and a high bedload fraction under a subtropical-temperate monsoon climate facilitate the formation of the lobate shallow water delta.A low bedload fraction provides a favorable condition for the development of barfinger,but flood events under a continental semi-arid climate enhance channel-mouth deposition driving the formation of the lobate delta with bar-finger. 展开更多
关键词 Braided-river delta Mouth-bar Paleoclimate Sedimentary architecture Bohai Bay Basin
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Lithofacies division and intelligent identification of the lacustrine mixed rocks in the Upper Xiaganchaigou Formation in Yingxi area of the Qaidam Basin,northwestern China 认领 引用 被引量：1
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作者 Yong-Shu Zhang Jia-Lin Fu +10 位作者 Kun-Yu Wu Wu-Rong Wang Ying-Hai Jiang Shu-Qi Zhang Jian Li Han Wang Li-Ben Deng Zi-Mo Xu Na Zhang Cheng-Zao Jia Da-Li Yue 《Journal of Palaeogeography》 SCIE CSCD 2025年第4期109-126,共18页
The Lower Ganchaigou Formation in the Yingxi area of the Qaidam Basin is a typical lacustrine mixed rock reservoir in western China.It is characterized by strong interlayer heterogeneity,development of diverse lithofa... The Lower Ganchaigou Formation in the Yingxi area of the Qaidam Basin is a typical lacustrine mixed rock reservoir in western China.It is characterized by strong interlayer heterogeneity,development of diverse lithofacies types,and complex response features in logging curves.These complexities make lithofacies identification of the Ganchaigou Formation particularly challenging for non-coring wells,demanding a more efficient and accurate approach.Based on lithology and structural patterns,a lithofacies classification scheme was established.Three intelligent logging identification methods based on improved long short-term memory(LSTM)networks were constructed for lithofacies identification.The accuracy of these methods was evaluated,and the most suitable intelligent logging identification method for the reservoir lithofacies in the Yingxi area was selected.In the Upper Xiaganchaigou Formation(E32 section)of the Yingxi area,a total of eight lithofacies types were identified:laminated lime-dolostone,stratified lime-dolostone,laminated dolostonelime,stratified dolostone-lime,laminated lime-dolomitic shale,massive mudstone,sandstone,and gypsum.The overall recognition accuracies of the LSTM,Bi-LSTM,and Attention-based Bi-LSTM intelligent identification models are 81%,85%,and 87%,respectively.The overall recognition accuracies of the three intelligent algorithms are relatively high,with the Attention-based Bi-LSTM model achieving the highest accuracy.This model demonstrates superior applicability for intelligent lithofacies identification in lacustrine mixed rock reservoirs,particularly those dominated by carbonates in the Yingxi area.It effectively interprets the lithofacies types of non-coring wells in the study area and provides a valuable reference for interpreting lithofacies logs in similar depositional environments. 展开更多
关键词 Qaidam Basin Yingxi area Lithofacies identification Neural network intelligent recognition E32segment
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Sedimentary architecture characterization by combining well logs and seismic data in river-dominated delta reservoirs:the Pearl River Mouth Basin,South China Sea 认领 引用
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作者 Hong-Hui Li Da-Li Yue +5 位作者 Wei Li Ling-Ling Dan Yuan Liu Wu-Rong Wang Ke-Yu Ren Ling Tan 《Journal of Palaeogeography》 SCIE CSCD 2025年第3期40-59,共20页
In offshore fields with limited well data,intricate geological configurations,and high reservoir heterogeneity,the accurate prediction of sand body distribution and characterization of sedimentary architecture pose si... In offshore fields with limited well data,intricate geological configurations,and high reservoir heterogeneity,the accurate prediction of sand body distribution and characterization of sedimentary architecture pose significant challenges due to inherent geological uncertainties and data limitations.This study employs a comprehensive approach integrating three key methods to enhance prediction accuracy:(i)fusion of spectral-decomposed seismic attributes,(ii)seismic attribute fusion of target and neighboring zones,and(iii)colored seismic inversion.The first method leverages seismic information across various frequencies,yielding reliable results for sand bodies of different thicknesses.The second method mitigates the impact of seismic responses from adjacent zones on sand body predictions,making it particularly suitable for target intervals where neighboring zones significantly influence the seismic response.The third one,colored seismic inversion enhances the prediction of vertical distribution and the stacking relationships of sand bodies.These methods have been applied in an oilfield in the Pearl River Mouth Basin,southern China.Consequently,the sedimentary architecture of a braided river delta reservoir is successfully characterized,leading to the identification of four distributary channels within a depositional Zone 1 of the Zhujiang Formation.Additionally,a comprehensive workflow integrating well logs,seismic data,and depositional models significantly improves predictions of sand body distribution and sedimentary architecture in complex geological settings,providing critical geological insights for optimizing subsequent oilfield development strategies. 展开更多
关键词 Seismic attributes Seismic inversion Sedimentary architecture River-dominated delta Machine learning The South China Sea
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