Carbonate karst reservoir is the emphases of Tarim＇s carbonate exploration. However, it is buried at a large depth, which results in Weak seismic reflection signal and low S/N ratio. In addition, the karst reservoir ...Carbonate karst reservoir is the emphases of Tarim＇s carbonate exploration. However, it is buried at a large depth, which results in Weak seismic reflection signal and low S/N ratio. In addition, the karst reservoir contains great heterogeneity, so reservoir prediction is very difficult. Through many years of research and exploration, we have established a suite of comprehensive evaluation technology for carbonate karst reservoir using geophysical characteristics and a geological concept model, including a technique for reconstructing the paleogeomorphology of buried hills based on a sequence framework, seismic description of the karst reservoir, and strain variant analysis for fracture estimation. The evaluation technology has been successfully applied in the Tabei and Tazhong areas, and commercial production of oil and gas has been achieved. We show the application of this technology in the Lunguxi area in North Tarim in this paper.展开更多
High-precision and efficient structural response prediction is essential for intelligent disaster prevention and mitigation in building structures,including post-earthquake damage assessment,structural health monitori...High-precision and efficient structural response prediction is essential for intelligent disaster prevention and mitigation in building structures,including post-earthquake damage assessment,structural health monitoring,and seismic resilience assessment of buildings.To improve the accuracy and efficiency of structural response prediction,this study proposes a novel physics-informed deep-learning-based realtime structural response prediction method that can predict a large number of nodes in a structure through a data-driven training method and an autoregressive training strategy.The proposed method includes a Phy-Seisformer model that incorporates the physical information of the structure into the model,thereby enabling higher-precision predictions.Experiments were conducted on a four-story masonry structure,an eleven-story reinforced concrete irregular structure,and a twenty-one-story reinforced concrete frame structure to verify the accuracy and efficiency of the proposed method.In addition,the effectiveness of the structure in the Phy-Seisformer model was verified using an ablation study.Furthermore,by conducting a comparative experiment,the impact of the range of seismic wave amplitudes on the prediction accuracy was studied.The experimental results show that the method proposed in this paper can achieve very high accuracy and at least 5000 times faster calculation speed than finite element calculations for different types of building structures.展开更多
基金This project is the applied fundamental research projects (04A10101) sponsored by the scientific and technology developmentdepartment of CNPC.
文摘Carbonate karst reservoir is the emphases of Tarim＇s carbonate exploration. However, it is buried at a large depth, which results in Weak seismic reflection signal and low S/N ratio. In addition, the karst reservoir contains great heterogeneity, so reservoir prediction is very difficult. Through many years of research and exploration, we have established a suite of comprehensive evaluation technology for carbonate karst reservoir using geophysical characteristics and a geological concept model, including a technique for reconstructing the paleogeomorphology of buried hills based on a sequence framework, seismic description of the karst reservoir, and strain variant analysis for fracture estimation. The evaluation technology has been successfully applied in the Tabei and Tazhong areas, and commercial production of oil and gas has been achieved. We show the application of this technology in the Lunguxi area in North Tarim in this paper.
基金support from the National Natural Science Foundation of China(52025083 and U2139209)XPLORER PRIZE of New Cornerstone Science Foundation,the Shanghai Social Development Science and Technology Research Project(22dz1201400)the Shanghai Urban Digital Transformation Special Fund(202201033).
文摘High-precision and efficient structural response prediction is essential for intelligent disaster prevention and mitigation in building structures,including post-earthquake damage assessment,structural health monitoring,and seismic resilience assessment of buildings.To improve the accuracy and efficiency of structural response prediction,this study proposes a novel physics-informed deep-learning-based realtime structural response prediction method that can predict a large number of nodes in a structure through a data-driven training method and an autoregressive training strategy.The proposed method includes a Phy-Seisformer model that incorporates the physical information of the structure into the model,thereby enabling higher-precision predictions.Experiments were conducted on a four-story masonry structure,an eleven-story reinforced concrete irregular structure,and a twenty-one-story reinforced concrete frame structure to verify the accuracy and efficiency of the proposed method.In addition,the effectiveness of the structure in the Phy-Seisformer model was verified using an ablation study.Furthermore,by conducting a comparative experiment,the impact of the range of seismic wave amplitudes on the prediction accuracy was studied.The experimental results show that the method proposed in this paper can achieve very high accuracy and at least 5000 times faster calculation speed than finite element calculations for different types of building structures.
