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
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
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
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(E_(3)^(2) 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
基金the financial support from National Natural Science Foundation of China(Grand No.51475280)
文摘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.
基金supported by the National Natural Science Foundation of China (Grant Nos. 50975166 and 51475280)the Excellent Engineer Training Program (Metallic material engineering of Shanghai University) of Ministry of Education, China
文摘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.
基金supported by National Natural Science Foundation Project of China(Nos.42272186,42302128,42202109,42472179)the Cooperation Project of the PetroChina Corporation(ZLZX2020-02)Young Elite Scientist Sponsorship Program by Bast of China(BYESS2023460)。
文摘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.
基金supported by the the National Natural Science Foundation of China(No.42272186,42302128,42202109 and 42472179)
文摘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(E_(3)^(2) 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.
