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597 results on '"Zhu, Jianmin"'

30. A novel seismic inversion method based on multiple attributes and machine learning for hydrocarbon reservoir prediction in Bohai Bay Basin, Eastern China.

Author

Liu, Zongbin, Zhu, Jianmin, Tian, Bo, Zhang, Rui, Fu, Yongheng, Liu, Yuan, and Wang, Lixin

Subjects
CONVOLUTIONAL neural networks, PETROLEUM prospecting, MACHINE learning, OIL fields, GENETIC algorithms
Abstract

As the demands for hydrocarbon exploration continue to rise, the identification of thin sand bodies becomes significantly important for subsequent petroleum exploration and development efforts. However, traditional inversion techniques struggle with complex subsurface structures because of the low frequency seismic data. To characterize the architecture of hydrocarbon reservoir precisely, a novel seismic inversion method is applied to improve the resolution of seismic data for a high interpretation accuracy. In this study, we take the X Oilfield in Eastern China as an example, adopted a novel approach combining spectral decomposition with convolutional neural networks (CNNs) within a genetic algorithm (GA) framework for inversion. The CNNs are adept at recognizing and interpreting the spatial configurations in the data, thereby establishing a high correlation between seismic attributes and sand body distributions. GA helps CNNs to get an optimal solution in a fast speed. The results reveal that the model's sand thickness predictions closely match the actual measurements at wells, with a new horizontal well's alignment with the predicted output reaching an accuracy of 85.1%. Compared to traditional seismic inversion methods, our method requires less data. This approach may find a wider application, especially at offshore oilfields with few wells data and low quality seismic data. [ABSTRACT FROM AUTHOR]

Published
2024
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39. Periodic magnetic domains in single-crystalline cobalt filament arrays

Author

Chen, Fei, Wang, Fan, Jia, Fei, Li, Jingning, Liu, Kai, Huang, Sunxiang, Luan, Zhongzhi, Wu, Di, Chen, Yanbin, Zhu, Jianmin, Peng, Ru-Wen, and Wang, Mu

Subjects
Macromolecular and Materials Chemistry, Engineering, Chemical Sciences, Chemical sciences, Physical sciences
Abstract

Magnetic structures with controlled domain wall pattern may be applied as potential building blocks for three-dimensional magnetic memory and logic devices. Using a unique electrochemical self-assembly method, we achieve regular single-crystalline cobalt filament arrays with specific geometric profile and crystallographic orientation, and the magnetic domain configuration can be conveniently tailored. We report the transition of periodic antiparallel magnetic domains to compressed vortex magnetic domains depending on the ratio of height to width of the wires. A "phase diagram" is obtained to describe the dependence of the type of magnetic domain and the geometrical profiles of the wires. Magnetoresistance of the filaments demonstrates that the contribution of a series of 180 domain walls is over 0.15% of the zero-field resistance ρ(H=0). These self-assembled magnetic nanofilaments, with controlled periodic domain patterns, offer an interesting platform to explore domain-wall-based memory and logic devices.

Published
2016

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