Abstract：Because of the key software architecture requirements of middleware in the field of geological intelligence in big data, big computing, and big models, this paper designs and implements a new petroleum valuation distributed file system (PetroV DFS).PetroV DFS adopts a two-level data storage mechanism including local spatial database storage under ST-based KIDA metadata modeling and distributed data chunk file storage based on geographic meshing and geological information coding.The storage kinds of distributed chunk file include spatial index chunk for basin-level geological mapping (two-dimensional digital cores), octree branch chunk for three-dimensional volume data (seismic data, three-dimensional digital cores), and spatial key-value pairs for conventional, imaging log data storage.Following the geographical mesh subdivision algorithm, PetroV DFS supports the storage of different data types from the global geographic scale to single well or even smaller scales, and there is no upper allocation limits on the number of data chunk files.Especially, if there are no any constraints in the case of scale-out inexpensive servers from different regions (e.g., different local data center), it is no upper limits on the amount of data stored.PetroV DFS ensures that "geographically close and storage location close", namely, data from the same geographic area are stored in the same rack of the same data center.For the segmentation of 440G prestack seismic data files and the calculation of the full-time frequency-amplitude attributes, the generic programming of PetroV DFS can be effectively deployed on the commodity computers, and gets almost the same calculation effects preserved ever by high-performance computers in addition to significantly improving the professional data management.
盛秀杰, 金之钧, 彭成, 景妍. PetroV分布式文件系统的设计与实现[J]. 石油地球物理勘探, 2019, 54(3): 641-649.
SHENG Xiujie, JIN Zhijun, PENG Cheng, JING Yan. Design and implementation of the PetroV distributed file system based on geographic meshing and geological information coding. Oil Geophysical Prospecting, 2019, 54(3): 641-649.
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