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5. Timing of petroleum accumulation and the division of reservoir-forming assemblages, Junggar Basin, NW China

Author

Zhang Yijie, Cao Jian, and Hu Wenxuan

Subjects
Petroleum refining. Petroleum products, TP690-692.5
Abstract

Timing of petroleum accumulations and distribution of vertical basin-scale sealing beds are important in the study of superimposed basins. This information can be used to divide petroleum accumulations of different times into different petroleum-forming assemblages vertically so as to reconstruct the petroleum accumulation process and distribution more accurately. The Mahu-West Well Pen 1 combination petroleum system in the Junggar Basin is taken as the case study. By isotopic K-Ar dating of authigenic illite, optical feature of organic inclusions, and fluid inclusion homogenization temperature, combined with burial and thermal history analysis, timing of petroleum accumulations is defined, i.e., P2–3, T3, J2—K1 and N. According to the vertical sealing mudstones of T3b, J1s1, and K1tg, four petroleum-forming assemblages were divided including C—T3, T3—J1, J2—K1, and K-N. 摘 要: 叠合盆地油气成藏研究不仅需要确定成藏期次，而且还要结合垂向封隔层的分布，将不同成藏期的油气藏从垂向上划分为不同的成藏组合，以期更准确地反映油气成藏过程和描述油气空间分布。以准噶尔盆地玛湖—盆1井西复合含油气系统为例，基于储集层自生伊利石K-Ar同位素测年、包裹体光性特征与均一温度分析、埋藏-热演化史分析，确定该油气系统共有4个主要的成藏期(P2—3、T3、J2—K1和N)。在此基础上，结合垂向封隔层(T3b、J1s1、K1tg泥岩)的分布，划分出C—T3、T3—J1、J2—K1和K—N等4个成藏组合，不同的成藏组合在不同区带具有不同的勘探意义。 Key words: petroleum accumulation timing, reservoir-forming assemblage, petroleum migration, Junggar Basin, superimposed basin

Published
2010
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27. OLR 2021 Challenge: Datasets, Rules and Baselines

Author

Wang, Binling, Hu, Wenxuan, Li, Jing, Zhi, Yiming, Li, Zheng, Hong, Qingyang, Li, Lin, Wang, Dong, Song, Liming, and Yang, Cheng

Subjects
Computer Science - Computation and Language, Computer Science - Machine Learning, Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing
Abstract

This paper introduces the sixth Oriental Language Recognition (OLR) 2021 Challenge, which intends to improve the performance of language recognition systems and speech recognition systems within multilingual scenarios. The data profile, four tasks, two baselines, and the evaluation principles are introduced in this paper. In addition to the Language Identification (LID) tasks, multilingual Automatic Speech Recognition (ASR) tasks are introduced to OLR 2021 Challenge for the first time. The challenge this year focuses on more practical and challenging problems, with four tasks: (1) constrained LID, (2) unconstrained LID, (3) constrained multilingual ASR, (4) unconstrained multilingual ASR. Baselines for LID tasks and multilingual ASR tasks are provided, respectively. The LID baseline system is an extended TDNN x-vector model constructed with Pytorch. A transformer-based end-to-end model is provided as the multilingual ASR baseline system. These recipes will be online published, and available for participants to construct their own LID or ASR systems. The baseline results demonstrate that those tasks are rather challenging and deserve more effort to achieve better performance., Comment: arXiv admin note: text overlap with arXiv:2006.03473, arXiv:1907.07626, arXiv:1806.00616, arXiv:1706.09742

Published
2021

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