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The Menilite Shales (Oligocene) of the Polish Carpathians are the source of low-sulfur oils in the thrust belt and some high-sulfur oils in the Carpathian Foredeep. These oil occurrences indicate that the high-sulfur oils in the Foredeep were generated and expelled before major thrusting and the low-sulfur oils in the thrust belt were generated and expelled during or after major thrusting. Two distinct organic facies have been observed in the Menilite Shales. One organic facies has a high clastic sediment input and contains Type-II kerogen. The other organic facies has a lower clastic sediment input and contains Type-IIS kerogen. Representative samples of both organic facies were used to determine kinetic parameters for immiscible oil generation by isothermal hydrous pyrolysis and S2 generation by non-isothermal open-system pyrolysis. The derived kinetic parameters showed that timing of S2 generation was not as different between the Type-IIS and -II kerogen based on open-system pyrolysis as compared with immiscible oil generation based on hydrous pyrolysis. Applying these kinetic parameters to a burial history in the Skole unit showed that some expelled oil would have been generated from the organic facies with Type-IIS kerogen before major thrusting with the hydrous-pyrolysis kinetic parameters but not with the open-system pyrolysis kinetic parameters. The inability of open-system pyrolysis to determine earlier petroleum generation from Type-IIS kerogen is attributed to the large polar-rich bitumen component in S2 generation, rapid loss of sulfur free-radical initiators in the open system, and diminished radical selectivity and rate constant differences at higher temperatures. Hydrous-pyrolysis kinetic parameters are determined in the presence of water at lower temperatures in a closed system, which allows differentiation of bitumen and oil generation, interaction of free-radical initiators, greater radical selectivity, and more distinguishable rate constants as would occur during natural maturation. Kinetic parameters derived from hydrous pyrolysis show good correlations with one another (compensation effect) and kerogen organic-sulfur contents. These correlations allow for indirect determination of hydrous-pyrolysis kinetic parameters on the basis of the organic-sulfur mole fraction of an immature Type-II or -IIS kerogen. 相似文献
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从土地征用基本概念和基本特征着手,描述了土地征用的主要风险,其表现为土地征用权力滥用、侵害失地农民的权益、诱发集体土地黑市、诱发社会矛盾等方面。分析了产生风险的“公共利益需要”缺乏明确界定、土地征用的补偿标准偏低或滞后、土地征用及相关制度不健全或存在缺陷等主要原因。提出了控制与规避风险的对策与措施:严格限定公益性用地范围,提高征地补偿标准,扩大补偿范围,完善相关制度,严格土地征用程序。 相似文献
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国内外旅游解说研究进展综述 总被引:14,自引:0,他引:14
本文对国外旅游解说系统研究从旅游解说的起源、原理、内涵、框架、功能、受众类型、解说方式、解说规划等方面进行了全面的总结,综合了国内旅游研究的研究现状与趋势。寻找出国内解说研究与国外解说研究的差距,借此推动国内的旅游解说系统研究,从而更好地为旅游规划、旅游业的可持续发展和游客管理服务。 相似文献
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Histograms of observations from spatial phenomena are often found to be more heavy-tailed than Gaussian distributions, which
makes the Gaussian random field model unsuited. A T-distributed random field model with heavy-tailed marginal probability density functions is defined. The model is a generalization
of the familiar Student-T distribution, and it may be given a Bayesian interpretation. The increased variability appears cross-realizations, contrary
to in-realizations, since all realizations are Gaussian-like with varying variance between realizations. The T-distributed random field model is analytically tractable and the conditional model is developed, which provides algorithms
for conditional simulation and prediction, so-called T-kriging. The model compares favourably with most previously defined random field models. The Gaussian random field model
appears as a special, limiting case of the T-distributed random field model. The model is particularly useful whenever multiple, sparsely sampled realizations of the
random field are available, and is clearly favourable to the Gaussian model in this case. The properties of the T-distributed random field model is demonstrated on well log observations from the Gullfaks field in the North Sea. The predictions
correspond to traditional kriging predictions, while the associated prediction variances are more representative, as they
are layer specific and include uncertainty caused by using variance estimates. 相似文献
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